\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS)\\ndef test_sample_gradient(jax_dist, sp_dist, params):\\n gamma_derived_params = {'Gamma': ['concentration'], 'Beta': [\\n 'concentration1', 'concentration0'], 'BetaProportion': ['mean',\\n 'concentration'], 'Chi2': ['df'], 'Dirichlet': ['concentration'],\\n 'InverseGamma': ['concentration'], 'LKJ': ['concentration'],\\n 'LKJCholesky': ['concentration'], 'StudentT': ['df']}.get(jax_dist.\\n __name__, [])\\n dist_args = [p for p in (inspect.getfullargspec(jax_dist.__init__)[0][1\\n :] if inspect.isclass(jax_dist) else inspect.getfullargspec(\\n jax_dist)[0])]\\n params_dict = dict(zip(dist_args[:len(params)], params))\\n jax_class = type(jax_dist(**params_dict))\\n reparametrized_params = [p for p in jax_class.reparametrized_params if \\n p not in gamma_derived_params]\\n if not reparametrized_params:\\n pytest.skip('{} not reparametrized.'.format(jax_class.__name__))\\n nonrepara_params_dict = {k: v for k, v in params_dict.items() if k not in\\n reparametrized_params}\\n repara_params = tuple(v for k, v in params_dict.items() if k in\\n reparametrized_params)\\n rng_key = random.PRNGKey(0)\\n\\n def fn(args):\\n args_dict = dict(zip(reparametrized_params, args))\\n return jnp.sum(jax_dist(**args_dict, **nonrepara_params_dict).\\n sample(key=rng_key))\\n actual_grad = jax.grad(fn)(repara_params)\\n assert len(actual_grad) == len(repara_params)\\n eps = 0.001\\n for i in range(len(repara_params)):\\n if repara_params[i] is None:\\n continue\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(\\n repara_params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(\\n repara_params)]\\n fn_lhs = fn(args_lhs)\\n fn_rhs = fn(args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad[i]) == jnp.shape(repara_params[i])\\n assert_allclose(jnp.sum(actual_grad[i]), expected_grad, rtol=0.02,\\n atol=0.03)\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (10, 3)])\\ndef test_sine_bivariate_von_mises_batch_shape(batch_shape):\\n phi_loc = jnp.broadcast_to(jnp.array(0.0), batch_shape)\\n psi_loc = jnp.array(0.0)\\n phi_conc = jnp.array(1.0)\\n psi_conc = jnp.array(1.0)\\n corr = jnp.array(0.1)\\n sine = SineBivariateVonMises(phi_loc, psi_loc, phi_conc, psi_conc, corr)\\n assert sine.batch_shape == batch_shape\\n samples = sine.sample(random.PRNGKey(0))\\n assert samples.shape == (*batch_shape, 2)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n\\n\\ndef sde_fn1(x, _):\\n lam = 0.1\\n sigma2 = 0.1\\n return lam * x, sigma2\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\ndef _mvn_to_scipy(loc, cov, prec, tril):\\n jax_dist = dist.MultivariateNormal(loc, cov, prec, tril)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\ndef _TruncatedCauchy(loc, scale, low, high):\\n return dist.TruncatedCauchy(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises_batched(self:\\n SineSkewedVonMisesBatched, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS)\\ndef test_sample_gradient(jax_dist, sp_dist, params):\\n gamma_derived_params = {'Gamma': ['concentration'], 'Beta': [\\n 'concentration1', 'concentration0'], 'BetaProportion': ['mean',\\n 'concentration'], 'Chi2': ['df'], 'Dirichlet': ['concentration'],\\n 'InverseGamma': ['concentration'], 'LKJ': ['concentration'],\\n 'LKJCholesky': ['concentration'], 'StudentT': ['df']}.get(jax_dist.\\n __name__, [])\\n dist_args = [p for p in (inspect.getfullargspec(jax_dist.__init__)[0][1\\n :] if inspect.isclass(jax_dist) else inspect.getfullargspec(\\n jax_dist)[0])]\\n params_dict = dict(zip(dist_args[:len(params)], params))\\n jax_class = type(jax_dist(**params_dict))\\n reparametrized_params = [p for p in jax_class.reparametrized_params if \\n p not in gamma_derived_params]\\n if not reparametrized_params:\\n pytest.skip('{} not reparametrized.'.format(jax_class.__name__))\\n nonrepara_params_dict = {k: v for k, v in params_dict.items() if k not in\\n reparametrized_params}\\n repara_params = tuple(v for k, v in params_dict.items() if k in\\n reparametrized_params)\\n rng_key = random.PRNGKey(0)\\n\\n def fn(args):\\n args_dict = dict(zip(reparametrized_params, args))\\n return jnp.sum(jax_dist(**args_dict, **nonrepara_params_dict).\\n sample(key=rng_key))\\n actual_grad = jax.grad(fn)(repara_params)\\n assert len(actual_grad) == len(repara_params)\\n eps = 0.001\\n for i in range(len(repara_params)):\\n if repara_params[i] is None:\\n continue\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(\\n repara_params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(\\n repara_params)]\\n fn_lhs = fn(args_lhs)\\n fn_rhs = fn(args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad[i]) == jnp.shape(repara_params[i])\\n assert_allclose(jnp.sum(actual_grad[i]), expected_grad, rtol=0.02,\\n atol=0.03)\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (10, 3)])\\ndef test_sine_bivariate_von_mises_batch_shape(batch_shape):\\n phi_loc = jnp.broadcast_to(jnp.array(0.0), batch_shape)\\n psi_loc = jnp.array(0.0)\\n phi_conc = jnp.array(1.0)\\n psi_conc = jnp.array(1.0)\\n corr = jnp.array(0.1)\\n sine = SineBivariateVonMises(phi_loc, psi_loc, phi_conc, psi_conc, corr)\\n assert sine.batch_shape == batch_shape\\n samples = sine.sample(random.PRNGKey(0))\\n assert samples.shape == (*batch_shape, 2)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n\\n\\ndef sde_fn1(x, _):\\n lam = 0.1\\n sigma2 = 0.1\\n return lam * x, sigma2\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\ndef _mvn_to_scipy(loc, cov, prec, tril):\\n jax_dist = dist.MultivariateNormal(loc, cov, prec, tril)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\ndef _TruncatedCauchy(loc, scale, low, high):\\n return dist.TruncatedCauchy(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises_batched(self:\\n SineSkewedVonMisesBatched, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (10, 3)])\\ndef test_sine_bivariate_von_mises_batch_shape(batch_shape):\\n phi_loc = jnp.broadcast_to(jnp.array(0.0), batch_shape)\\n psi_loc = jnp.array(0.0)\\n phi_conc = jnp.array(1.0)\\n psi_conc = jnp.array(1.0)\\n corr = jnp.array(0.1)\\n sine = SineBivariateVonMises(phi_loc, psi_loc, phi_conc, psi_conc, corr)\\n assert sine.batch_shape == batch_shape\\n samples = sine.sample(random.PRNGKey(0))\\n assert samples.shape == (*batch_shape, 2)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\ndef _mvn_to_scipy(loc, cov, prec, tril):\\n jax_dist = dist.MultivariateNormal(loc, cov, prec, tril)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\ndef _TruncatedCauchy(loc, scale, low, high):\\n return dist.TruncatedCauchy(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises_batched(self:\\n SineSkewedVonMisesBatched, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (10, 3)])\\ndef test_sine_bivariate_von_mises_batch_shape(batch_shape):\\n phi_loc = jnp.broadcast_to(jnp.array(0.0), batch_shape)\\n psi_loc = jnp.array(0.0)\\n phi_conc = jnp.array(1.0)\\n psi_conc = jnp.array(1.0)\\n corr = jnp.array(0.1)\\n sine = SineBivariateVonMises(phi_loc, psi_loc, phi_conc, psi_conc, corr)\\n assert sine.batch_shape == batch_shape\\n samples = sine.sample(random.PRNGKey(0))\\n assert samples.shape == (*batch_shape, 2)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\ndef _TruncatedCauchy(loc, scale, low, high):\\n return dist.TruncatedCauchy(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises_batched(self:\\n SineSkewedVonMisesBatched, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (10, 3)])\\ndef test_sine_bivariate_von_mises_batch_shape(batch_shape):\\n phi_loc = jnp.broadcast_to(jnp.array(0.0), batch_shape)\\n psi_loc = jnp.array(0.0)\\n phi_conc = jnp.array(1.0)\\n psi_conc = jnp.array(1.0)\\n corr = jnp.array(0.1)\\n sine = SineBivariateVonMises(phi_loc, psi_loc, phi_conc, psi_conc, corr)\\n assert sine.batch_shape == batch_shape\\n samples = sine.sample(random.PRNGKey(0))\\n assert samples.shape == (*batch_shape, 2)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises_batched(self:\\n SineSkewedVonMisesBatched, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (10, 3)])\\ndef test_sine_bivariate_von_mises_batch_shape(batch_shape):\\n phi_loc = jnp.broadcast_to(jnp.array(0.0), batch_shape)\\n psi_loc = jnp.array(0.0)\\n phi_conc = jnp.array(1.0)\\n psi_conc = jnp.array(1.0)\\n corr = jnp.array(0.1)\\n sine = SineBivariateVonMises(phi_loc, psi_loc, phi_conc, psi_conc, corr)\\n assert sine.batch_shape == batch_shape\\n samples = sine.sample(random.PRNGKey(0))\\n assert samples.shape == (*batch_shape, 2)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises_batched(self:\\n SineSkewedVonMisesBatched, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n@pytest.mark.parametrize('ts', [[transforms.PowerTransform(0.7), transforms\\n .AffineTransform(2.0, 3.0)], [transforms.ExpTransform()], [transforms.\\n ComposeTransform([transforms.AffineTransform(-2, 3), transforms.\\n ExpTransform()]), transforms.PowerTransform(3.0)], [_make_iaf(5,\\n hidden_dims=[10], rng_key=random.PRNGKey(0)), transforms.\\n PermuteTransform(jnp.arange(5)[::-1]), _make_iaf(5, hidden_dims=[10],\\n rng_key=random.PRNGKey(1))]])\\ndef test_compose_transform_with_intermediates(ts):\\n transform = transforms.ComposeTransform(ts)\\n x = random.normal(random.PRNGKey(2), (7, 5))\\n y, intermediates = transform.call_with_intermediates(x)\\n logdet = transform.log_abs_det_jacobian(x, y, intermediates)\\n assert_allclose(y, transform(x))\\n assert_allclose(logdet, transform.log_abs_det_jacobian(x, y))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_gamma_poisson_log_prob(shape):\\n gamma_conc = np.exp(np.random.normal(size=shape))\\n gamma_rate = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(15)\\n num_samples = 300000\\n poisson_rate = np.random.gamma(gamma_conc, 1 / gamma_rate, size=(\\n num_samples,) + shape)\\n log_probs = dist.Poisson(poisson_rate).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,)])\\ndef test_polya_gamma(batch_shape, num_points=20000):\\n d = dist.TruncatedPolyaGamma(batch_shape=batch_shape)\\n rng_key = random.PRNGKey(0)\\n x = jnp.linspace(1e-06, d.truncation_point, num_points)\\n prob = d.truncation_point / num_points * jnp.exp(logsumexp(d.log_prob(x\\n ), axis=-1))\\n assert_allclose(prob, jnp.ones(batch_shape), rtol=0.0001)\\n z = d.sample(rng_key, sample_shape=(3000,))\\n mean = jnp.mean(z, axis=-1)\\n assert_allclose(mean, 0.25 * jnp.ones(batch_shape), rtol=0.07)\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\ndef test_independent_shape(jax_dist, sp_dist, params):\\n d = jax_dist(*params)\\n batch_shape, event_shape = d.batch_shape, d.event_shape\\n shape = batch_shape + event_shape\\n for i in range(len(batch_shape)):\\n indep = dist.Independent(d, reinterpreted_batch_ndims=i)\\n sample = indep.sample(random.PRNGKey(0))\\n event_boundary = len(shape) - len(event_shape) - i\\n assert indep.batch_shape == shape[:event_boundary]\\n assert indep.event_shape == shape[event_boundary:]\\n assert jnp.shape(indep.log_prob(sample)) == shape[:event_boundary]\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef _identity(x):\\n return x\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE)\\n@pytest.mark.parametrize('prepend_shape', [(), (2, 3)])\\n@pytest.mark.parametrize('sample_shape', [(), (4,)])\\ndef test_expand(jax_dist, sp_dist, params, prepend_shape, sample_shape):\\n jax_dist = jax_dist(*params)\\n new_batch_shape = prepend_shape + jax_dist.batch_shape\\n expanded_dist = jax_dist.expand(new_batch_shape)\\n rng_key = random.PRNGKey(0)\\n samples = expanded_dist.sample(rng_key, sample_shape)\\n assert expanded_dist.batch_shape == new_batch_shape\\n assert samples.shape == sample_shape + new_batch_shape + jax_dist.event_shape\\n assert expanded_dist.log_prob(samples\\n ).shape == sample_shape + new_batch_shape\\n assert expanded_dist.expand((3,) + new_batch_shape).batch_shape == (3,\\n ) + new_batch_shape\\n if prepend_shape:\\n with pytest.raises(ValueError, match=\\n 'Cannot broadcast distribution of shape'):\\n assert expanded_dist.expand((3,) + jax_dist.batch_shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\ndef test_log_prob_LKJCholesky_uniform(dimension):\\n d = dist.LKJCholesky(dimension=dimension, concentration=1)\\n N = 5\\n corr_log_prob = []\\n for i in range(N):\\n sample = d.sample(random.PRNGKey(i))\\n log_prob = d.log_prob(sample)\\n sample_tril = matrix_to_tril_vec(sample, diagonal=-1)\\n cholesky_to_corr_jac = np.linalg.slogdet(jax.jacobian(\\n _tril_cholesky_to_tril_corr)(sample_tril))[1]\\n corr_log_prob.append(log_prob - cholesky_to_corr_jac)\\n corr_log_prob = np.array(corr_log_prob)\\n assert_allclose(corr_log_prob, jnp.broadcast_to(corr_log_prob[0],\\n corr_log_prob.shape), rtol=1e-06)\\n if dimension == 2:\\n assert_allclose(corr_log_prob[0], jnp.log(0.5), rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n\\n\\ndef _is_batched_multivariate(jax_dist):\\n return len(jax_dist.event_shape) > 0 and len(jax_dist.batch_shape) > 0\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_general_mixture(self: _GeneralMixture, locs=None, scales=None):\\n component_distributions = [vmap_over(d, loc=locs, scale=scales) for d in\\n self.component_distributions]\\n return vmap_over.dispatch(dist.MixtureGeneral)(self,\\n _component_distributions=component_distributions)\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('dimension', [2, 3, 5])\\n@pytest.mark.parametrize('concentration', [0.6, 2.2])\\ndef test_log_prob_LKJCholesky(dimension, concentration):\\n d = dist.LKJCholesky(dimension, concentration, sample_method='cvine')\\n beta_sample = d._beta.sample(random.PRNGKey(0))\\n beta_log_prob = jnp.sum(d._beta.log_prob(beta_sample))\\n partial_correlation = 2 * beta_sample - 1\\n affine_logdet = beta_sample.shape[-1] * jnp.log(2)\\n sample = signed_stick_breaking_tril(partial_correlation)\\n inv_tanh = lambda t: jnp.log((1 + t) / (1 - t)) / 2\\n inv_tanh_logdet = jnp.sum(jnp.log(vmap(grad(inv_tanh))(\\n partial_correlation)))\\n unconstrained = inv_tanh(partial_correlation)\\n corr_cholesky_logdet = biject_to(constraints.corr_cholesky\\n ).log_abs_det_jacobian(unconstrained, sample)\\n signed_stick_breaking_logdet = corr_cholesky_logdet + inv_tanh_logdet\\n actual_log_prob = d.log_prob(sample)\\n expected_log_prob = (beta_log_prob - affine_logdet -\\n signed_stick_breaking_logdet)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-05)\\n assert_allclose(jax.jit(d.log_prob)(sample), d.log_prob(sample), atol=2e-06\\n )\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_zero_inflated_enumerate_support():\\n base_dist = dist.Bernoulli(0.5)\\n d = dist.ZeroInflatedDistribution(base_dist, gate=0.5)\\n assert d.has_enumerate_support\\n assert_allclose(d.enumerate_support(), base_dist.enumerate_support())\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n\\n\\ndef _TruncatedNormal(loc, scale, low, high):\\n return dist.TruncatedNormal(loc=loc, scale=scale, low=low, high=high)\\n\\n\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_dist_pytree(jax_dist, sp_dist, params):\\n\\n def f(x):\\n return jax_dist(*params)\\n if jax_dist is _ImproperWrapper:\\n pytest.skip('Cannot flattening ImproperUniform')\\n if jax_dist is dist.EulerMaruyama:\\n pytest.skip(\\\"EulerMaruyama doesn't define flatten/unflatten\\\")\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n expected_dist = f(0)\\n actual_dist = jax.jit(f)(0)\\n expected_sample = expected_dist.sample(random.PRNGKey(0))\\n actual_sample = actual_dist.sample(random.PRNGKey(0))\\n expected_log_prob = expected_dist.log_prob(expected_sample)\\n actual_log_prob = actual_dist.log_prob(actual_sample)\\n assert_allclose(actual_sample, expected_sample, rtol=1e-06)\\n assert_allclose(actual_log_prob, expected_log_prob, rtol=2e-06)\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_jit_log_likelihood(jax_dist, sp_dist, params):\\n if jax_dist.__name__ in ('EulerMaruyama', 'GaussianRandomWalk',\\n '_ImproperWrapper', 'LKJ', 'LKJCholesky', '_SparseCAR'):\\n pytest.xfail(reason='non-jittable params')\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist(*params).sample(key=rng_key, sample_shape=(2, 3))\\n\\n def log_likelihood(*params):\\n return jax_dist(*params).log_prob(samples)\\n expected = log_likelihood(*params)\\n actual = jax.jit(log_likelihood)(*params)\\n assert_allclose(actual, expected, atol=2e-05, rtol=2e-05)\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('extra_event_dims,expand_shape', [(0, (4, 3, 2, 1)\\n ), (0, (4, 3, 2, 2)), (1, (5, 4, 3, 2)), (2, (5, 4, 3))])\\ndef test_expand_reshaped_distribution(extra_event_dims, expand_shape):\\n loc = jnp.zeros((1, 6))\\n scale_tril = jnp.eye(6)\\n d = dist.MultivariateNormal(loc, scale_tril=scale_tril)\\n full_shape = 4, 1, 1, 1, 6\\n reshaped_dist = d.expand([4, 1, 1, 1]).to_event(extra_event_dims)\\n cut = 4 - extra_event_dims\\n batch_shape, event_shape = full_shape[:cut], full_shape[cut:]\\n assert reshaped_dist.batch_shape == batch_shape\\n assert reshaped_dist.event_shape == event_shape\\n large = reshaped_dist.expand(expand_shape)\\n assert large.batch_shape == expand_shape\\n assert large.event_shape == event_shape\\n with pytest.raises((RuntimeError, ValueError)):\\n reshaped_dist.expand(expand_shape + (3,))\\n with pytest.raises((RuntimeError, ValueError)):\\n large.expand(expand_shape[1:])\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n\\n\\ndef test_beta_proportion_invalid_mean():\\n with dist.distribution.validation_enabled(), pytest.raises(ValueError,\\n match='^BetaProportion distribution got invalid mean parameter\\\\\\\\.$'):\\n dist.BetaProportion(1.0, 1.0)\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n@pytest.mark.parametrize('total_count', [1, 2, 3, 10])\\n@pytest.mark.parametrize('shape', [(1,), (3, 1), (2, 3, 1)])\\ndef test_beta_binomial_log_prob(total_count, shape):\\n concentration0 = np.exp(np.random.normal(size=shape))\\n concentration1 = np.exp(np.random.normal(size=shape))\\n value = jnp.arange(1 + total_count)\\n num_samples = 100000\\n probs = np.random.beta(concentration1, concentration0, size=(\\n num_samples,) + shape)\\n log_probs = dist.Binomial(total_count, probs).log_prob(value)\\n expected = logsumexp(log_probs, 0) - jnp.log(num_samples)\\n actual = dist.BetaBinomial(concentration1, concentration0, total_count\\n ).log_prob(value)\\n assert_allclose(actual, expected, rtol=0.02)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DIRECTIONAL)\\ndef test_gof(jax_dist, sp_dist, params):\\n if 'Improper' in jax_dist.__name__:\\n pytest.skip('distribution has improper .log_prob()')\\n if 'LKJ' in jax_dist.__name__:\\n pytest.xfail('incorrect submanifold scaling')\\n if jax_dist is dist.EulerMaruyama:\\n d = jax_dist(*params)\\n if d.event_dim > 1:\\n pytest.skip(\\n 'EulerMaruyama skip test when event shape is non-trivial.')\\n num_samples = 10000\\n if 'BetaProportion' in jax_dist.__name__:\\n num_samples = 20000\\n rng_key = random.PRNGKey(0)\\n d = jax_dist(*params)\\n samples = d.sample(key=rng_key, sample_shape=(num_samples,))\\n probs = np.exp(d.log_prob(samples))\\n dim = None\\n if jax_dist is dist.ProjectedNormal:\\n dim = samples.shape[-1] - 1\\n probs = probs.reshape(num_samples, -1)\\n samples = samples.reshape(probs.shape + d.event_shape)\\n if 'Dirichlet' in jax_dist.__name__:\\n samples = samples[..., :-1]\\n for b in range(probs.shape[1]):\\n try:\\n gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)\\n except InvalidTest:\\n pytest.skip('expensive test')\\n else:\\n assert gof > TEST_FAILURE_RATE\\n\\n\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\ndef test_simplex_to_order_transform(batch_shape):\\n simplex = jnp.arange(5.0) / jnp.arange(5.0).sum()\\n simplex = jnp.broadcast_to(simplex, batch_shape + simplex.shape)\\n transform = SimplexToOrderedTransform()\\n out = transform(simplex)\\n assert out.shape == transform.forward_shape(simplex.shape)\\n assert simplex.shape == transform.inverse_shape(out.shape)\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\ndef test_multinomial_abstract_total_count():\\n probs = jnp.array([0.2, 0.5, 0.3])\\n key = random.PRNGKey(0)\\n\\n def f(x):\\n total_count = x.sum(-1)\\n return dist.Multinomial(total_count, probs=probs, total_count_max=10\\n ).sample(key)\\n x = dist.Multinomial(10, probs).sample(key)\\n y = jax.jit(f)(x)\\n assert_allclose(x, y, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_sample_truncated_normal_in_tail():\\n tail_dist = dist.TruncatedNormal(loc=0, scale=1, low=-16, high=-15)\\n samples = tail_dist.sample(random.PRNGKey(0), sample_shape=(10000,))\\n assert ~jnp.isinf(samples).any()\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_mixture(self: _GaussianMixture, loc=None, scale=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc,\\n scale=scale)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, params', [(dist.Gamma, (1.0,)), (dist.\\n Gamma, (0.1,)), (dist.Gamma, (10.0,)), (dist.Chi2, (1.0,)), (dist.Chi2,\\n (0.1,)), (dist.Chi2, (10.0,)), (dist.Beta, (1.0, 1.0)), (dist.StudentT,\\n (5.0, 2.0, 4.0))])\\ndef test_pathwise_gradient(jax_dist, params):\\n rng_key = random.PRNGKey(0)\\n N = 1000000\\n\\n def f(params):\\n z = jax_dist(*params).sample(key=rng_key, sample_shape=(N,))\\n return (z + z ** 2).mean(0)\\n\\n def g(params):\\n d = jax_dist(*params)\\n return d.mean + d.variance + d.mean ** 2\\n actual_grad = grad(f)(params)\\n expected_grad = grad(g)(params)\\n assert_allclose(actual_grad, expected_grad, rtol=0.005)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transformed_dist', [dist.TransformedDistribution(\\n dist.Normal(np.array([2.0, 3.0]), 1.0), transforms.ExpTransform()),\\n dist.TransformedDistribution(dist.Exponential(jnp.ones(2)), [transforms\\n .PowerTransform(0.7), transforms.AffineTransform(0.0, jnp.ones(2) * 3)])])\\ndef test_transformed_distribution_intermediates(transformed_dist):\\n sample, intermediates = transformed_dist.sample_with_intermediates(random\\n .PRNGKey(1))\\n assert_allclose(transformed_dist.log_prob(sample, intermediates),\\n transformed_dist.log_prob(sample))\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n@vmap_over.register\\ndef _vmap_over_sine_skewed_von_mises(self: SineSkewedVonMises, skewness=None):\\n return vmap_over.dispatch(dist.SineSkewed)(self, base_dist=None,\\n skewness=skewness)\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 4)])\\ndef test_mask_grad(event_shape):\\n\\n def f(x, data):\\n base_dist = dist.Beta(jnp.exp(x), jnp.ones(event_shape)).to_event()\\n mask = jnp.all(jnp.isfinite(data), tuple(-i - 1 for i in range(len(\\n event_shape))))\\n log_prob = base_dist.mask(mask).log_prob(data)\\n assert log_prob.shape == data.shape[:len(data.shape) - len(event_shape)\\n ]\\n return log_prob.sum()\\n data = np.array([[0.4, np.nan, 0.2, np.nan], [0.5, 0.5, 0.5, 0.5]])\\n log_prob, grad = jax.value_and_grad(f)(1.0, data)\\n assert jnp.isfinite(grad) and jnp.isfinite(log_prob)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@jax.enable_custom_prng()\\ndef test_jax_custom_prng():\\n samples = dist.Normal(0, 5).sample(random.PRNGKey(0), sample_shape=(1000,))\\n assert ~jnp.isinf(samples).any()\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('p_dist, q_dist', [(dist.Beta, dist.Beta), (dist.\\n Gamma, dist.Gamma), (dist.Kumaraswamy, dist.Beta), (dist.Normal, dist.\\n Normal), (dist.Weibull, dist.Gamma)])\\ndef test_kl_univariate(shape, p_dist, q_dist):\\n\\n def make_dist(dist_class):\\n params = {}\\n for k, c in dist_class.arg_constraints.items():\\n if c is constraints.real:\\n params[k] = np.random.normal(size=shape)\\n elif c is constraints.positive:\\n params[k] = np.exp(np.random.normal(size=shape))\\n else:\\n raise ValueError(f'Missing pattern for param {k}.')\\n d = dist_class(**params)\\n if dist_class is dist.Kumaraswamy:\\n d.KL_KUMARASWAMY_BETA_TAYLOR_ORDER = 1000\\n return d\\n p = make_dist(p_dist)\\n q = make_dist(q_dist)\\n actual = kl_divergence(p, q)\\n x = p.sample(random.PRNGKey(0), (10000,)).copy()\\n expected = jnp.mean(p.log_prob(x) - q.log_prob(x), 0)\\n assert_allclose(actual, expected, rtol=0.05)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\ndef _make_iaf(input_dim, hidden_dims, rng_key):\\n arn_init, arn = AutoregressiveNN(input_dim, hidden_dims, param_dims=[1, 1])\\n _, init_params = arn_init(rng_key, (input_dim,))\\n return InverseAutoregressiveTransform(partial(arn, init_params))\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tril_cholesky_to_tril_corr(x):\\n w = vec_to_tril_matrix(x, diagonal=-1)\\n diag = jnp.sqrt(1 - jnp.sum(w ** 2, axis=-1))\\n cholesky = w + jnp.expand_dims(diag, axis=-1) * jnp.identity(w.shape[-1])\\n corr = jnp.matmul(cholesky, cholesky.T)\\n return matrix_to_tril_vec(corr, diagonal=-1)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _get_vmappable_dist_init_params(jax_dist):\\n if jax_dist.__name__ == '_TruncatedCauchy':\\n return [2, 3]\\n elif jax_dist.__name__ == '_TruncatedNormal':\\n return [2, 3]\\n elif issubclass(jax_dist, dist.Distribution):\\n init_parameters = list(inspect.signature(jax_dist.__init__).\\n parameters.keys())[1:]\\n vmap_over_parameters = list(inspect.signature(vmap_over.dispatch(\\n jax_dist)).parameters.keys())[1:]\\n return list([i for i, name in enumerate(init_parameters) if name in\\n vmap_over_parameters])\\n else:\\n raise ValueError\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\n@pytest.mark.parametrize('event_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_independent_normal(batch_shape, event_shape):\\n shape = batch_shape + event_shape\\n p = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n q = dist.Normal(np.random.normal(size=shape), np.exp(np.random.normal(\\n size=shape)))\\n actual = kl_divergence(dist.Independent(p, len(event_shape)), dist.\\n Independent(q, len(event_shape)))\\n expected = sum_rightmost(kl_divergence(p, q), len(event_shape))\\n assert_allclose(actual, expected)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape, mask_shape', [((), ()), ((2,), ()),\\n ((), (2,)), ((2,), (2,)), ((4, 2), (1, 2)), ((2,), (4, 2))])\\n@pytest.mark.parametrize('event_shape', [(), (3,)])\\ndef test_mask(batch_shape, event_shape, mask_shape):\\n jax_dist = dist.Normal().expand(batch_shape + event_shape).to_event(len\\n (event_shape))\\n mask = dist.Bernoulli(0.5).sample(random.PRNGKey(0), mask_shape)\\n if mask_shape == ():\\n mask = bool(mask)\\n samples = jax_dist.sample(random.PRNGKey(1))\\n actual = jax_dist.mask(mask).log_prob(samples)\\n assert_allclose(actual != 0, jnp.broadcast_to(mask, lax.\\n broadcast_shapes(batch_shape, mask_shape)))\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('method, arg', [('to_event', 1), ('mask', False),\\n ('expand', [5])])\\ndef test_special_dist_pytree(method, arg):\\n\\n def f(x):\\n d = dist.Normal(np.zeros(1), np.ones(1))\\n return getattr(d, method)(arg)\\n jax.jit(f)(0)\\n lax.map(f, np.ones(3))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _allclose_or_equal(a1, a2):\\n if isinstance(a1, np.ndarray):\\n return np.allclose(a2, a1)\\n elif isinstance(a1, jnp.ndarray):\\n return jnp.allclose(a2, a1)\\n elif isinstance(a1, csr_matrix):\\n return np.allclose(a2.todense(), a1.todense())\\n else:\\n return a2 == a1 or a2 is a1\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (5,)])\\n@pytest.mark.parametrize('prepend_event_shape', [(), (4,)])\\n@pytest.mark.parametrize('sample_shape', [(), (7,)])\\ndef test_transformed_distribution(batch_shape, prepend_event_shape,\\n sample_shape):\\n base_dist = dist.Normal(0, 1).expand(batch_shape + prepend_event_shape +\\n (6,)).to_event(1 + len(prepend_event_shape))\\n t1 = transforms.AffineTransform(0, 2)\\n t2 = transforms.LowerCholeskyTransform()\\n d = dist.TransformedDistribution(base_dist, [t1, t2, t1])\\n assert d.event_dim == 2 + len(prepend_event_shape)\\n y = d.sample(random.PRNGKey(0), sample_shape)\\n t = transforms.ComposeTransform([t1, t2, t1])\\n x = t.inv(y)\\n assert x.shape == sample_shape + base_dist.shape()\\n log_prob = d.log_prob(y)\\n assert log_prob.shape == sample_shape + batch_shape\\n t_log_det = t.log_abs_det_jacobian(x, y)\\n if prepend_event_shape:\\n t_log_det = t_log_det.sum(-1)\\n expected_log_prob = base_dist.log_prob(x) - t_log_det\\n assert_allclose(log_prob, expected_log_prob, atol=1e-05)\\n\\n\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint, x, expected', [(constraints.boolean,\\n np.array([True, False]), np.array([True, True])), (constraints.boolean,\\n np.array([1, 1]), np.array([True, True])), (constraints.boolean, np.\\n array([-1, 1]), np.array([False, True])), (constraints.corr_cholesky,\\n np.array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False\\n ])), (constraints.corr_cholesky, np.array([[[1, 0], [1, 0]], [[1, 0], [\\n 0.5, 0.5]]]), np.array([False, False])), (constraints.corr_matrix, np.\\n array([[[1, 0], [0, 1]], [[1, 0.1], [0, 1]]]), np.array([True, False])),\\n (constraints.corr_matrix, np.array([[[1, 0], [1, 0]], [[1, 0], [0.5, \\n 0.5]]]), np.array([False, False])), (constraints.greater_than(1), 3, \\n True), (constraints.greater_than(1), np.array([-1, 1, 5]), np.array([\\n False, False, True])), (constraints.integer_interval(-3, 5), 0, True),\\n (constraints.integer_interval(-3, 5), np.array([-5, -3, 0, 1.1, 5, 7]),\\n np.array([False, True, True, False, True, False])), (constraints.\\n interval(-3, 5), 0, True), (constraints.interval(-3, 5), np.array([-5, \\n -3, 0, 5, 7]), np.array([False, True, True, True, False])), (\\n constraints.less_than(1), -2, True), (constraints.less_than(1), np.\\n array([-1, 1, 5]), np.array([True, False, False])), (constraints.\\n lower_cholesky, np.array([[1.0, 0.0], [-2.0, 0.1]]), True), (\\n constraints.lower_cholesky, np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0,\\n 0.1], [2.0, 0.2]]]), np.array([False, False])), (constraints.\\n nonnegative_integer, 3, True), (constraints.nonnegative_integer, np.\\n array([-1.0, 0.0, 5.0]), np.array([False, True, True])), (constraints.\\n positive, 3, True), (constraints.positive, np.array([-1, 0, 5]), np.\\n array([False, False, True])), (constraints.positive_definite, np.array(\\n [[1.0, 0.3], [0.3, 1.0]]), True), (constraints.positive_definite, np.\\n array([[[2.0, 0.4], [0.3, 2.0]], [[1.0, 0.1], [0.1, 0.0]]]), np.array([\\n False, False])), (constraints.positive_integer, 3, True), (constraints.\\n positive_integer, np.array([-1.0, 0.0, 5.0]), np.array([False, False, \\n True])), (constraints.real, -1, True), (constraints.real, np.array([np.\\n inf, -np.inf, np.nan, np.pi]), np.array([False, False, False, True])),\\n (constraints.simplex, np.array([0.1, 0.3, 0.6]), True), (constraints.\\n simplex, np.array([[0.1, 0.3, 0.6], [-0.1, 0.6, 0.5], [0.1, 0.6, 0.5]]),\\n np.array([True, False, False])), (constraints.softplus_positive, 3, \\n True), (constraints.softplus_positive, np.array([-1, 0, 5]), np.array([\\n False, False, True])), (constraints.softplus_lower_cholesky, np.array([\\n [1.0, 0.0], [-2.0, 0.1]]), True), (constraints.softplus_lower_cholesky,\\n np.array([[[1.0, 0.0], [-2.0, -0.1]], [[1.0, 0.1], [2.0, 0.2]]]), np.\\n array([False, False])), (constraints.unit_interval, 0.1, True), (\\n constraints.unit_interval, np.array([-5, 0, 0.5, 1, 7]), np.array([\\n False, True, True, True, False])), (constraints.sphere, np.array([[1, 0,\\n 0], [0.5, 0.5, 0]]), np.array([True, False])), (constraints.\\n open_interval(0.0, 1.0), np.array([-5, 0, 0.5, 1, 7]), np.array([False,\\n False, True, False, False]))])\\ndef test_constraints(constraint, x, expected):\\n v = constraint.feasible_like(x)\\n if jnp.result_type(v) == 'float32' or jnp.result_type(v) == 'float64':\\n assert not constraint.is_discrete\\n assert_array_equal(constraint(x), expected)\\n feasible_value = constraint.feasible_like(x)\\n assert jnp.shape(feasible_value) == jnp.shape(x)\\n assert_allclose(constraint(feasible_value), jnp.full(jnp.shape(expected\\n ), True))\\n try:\\n inverse = biject_to(constraint).inv(feasible_value)\\n except NotImplementedError:\\n pass\\n else:\\n assert_allclose(inverse, jnp.zeros_like(inverse), atol=2e-07)\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n@vmap_over.register\\ndef _vmap_over_gaussian_2d_mixture(self: _Gaussian2DMixture, loc=None):\\n component_distribution = vmap_over(self.component_distribution, loc=loc)\\n return vmap_over.dispatch(dist.MixtureSameFamily)(self,\\n _component_distribution=component_distribution)\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_distribution_constraints(jax_dist, sp_dist, params, prepend_shape):\\n if jax_dist in (_TruncatedNormal, _TruncatedCauchy, _GaussianMixture,\\n _Gaussian2DMixture, _GeneralMixture, _General2DMixture):\\n pytest.skip(f'{jax_dist.__name__} is a function, not a class')\\n dist_args = [p for p in inspect.getfullargspec(jax_dist.__init__)[0][1:]]\\n valid_params, oob_params = list(params), list(params)\\n key = random.PRNGKey(1)\\n dependent_constraint = False\\n for i in range(len(params)):\\n if jax_dist in (_ImproperWrapper, dist.LKJ, dist.LKJCholesky\\n ) and dist_args[i] != 'concentration':\\n continue\\n if 'SineSkewed' in jax_dist.__name__ and dist_args[i] != 'skewness':\\n continue\\n if jax_dist is dist.EulerMaruyama and dist_args[i] != 't':\\n continue\\n if jax_dist is dist.TwoSidedTruncatedDistribution and dist_args[i\\n ] == 'base_dist':\\n continue\\n if jax_dist is dist.GaussianRandomWalk and dist_args[i] == 'num_steps':\\n continue\\n if jax_dist is dist.SineBivariateVonMises and dist_args[i\\n ] == 'weighted_correlation':\\n continue\\n if params[i] is None:\\n oob_params[i] = None\\n valid_params[i] = None\\n continue\\n constraint = jax_dist.arg_constraints[dist_args[i]]\\n if isinstance(constraint, constraints._Dependent):\\n dependent_constraint = True\\n break\\n key, key_gen = random.split(key)\\n oob_params[i] = gen_values_outside_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n valid_params[i] = gen_values_within_bounds(constraint, jnp.shape(\\n params[i]), key_gen)\\n if jax_dist is dist.MultivariateStudentT:\\n valid_params[0] += 1\\n if jax_dist is dist.LogUniform:\\n valid_params[1] += valid_params[0]\\n assert jax_dist(*oob_params)\\n if not dependent_constraint and (jax_dist is not _ImproperWrapper and \\n 'SineSkewed' not in jax_dist.__name__):\\n with pytest.raises(ValueError):\\n jax_dist(*oob_params, validate_args=True)\\n with pytest.raises(ValueError):\\n oob_params = jax.device_get(oob_params)\\n\\n def dist_gen_fn():\\n d = jax_dist(*oob_params, validate_args=True)\\n return d\\n jax.jit(dist_gen_fn)()\\n d = jax_dist(*valid_params, validate_args=True)\\n if sp_dist and not _is_batched_multivariate(d) and not (d.event_shape and\\n prepend_shape):\\n valid_samples = gen_values_within_bounds(d.support, size=\\n prepend_shape + d.batch_shape + d.event_shape)\\n try:\\n expected = sp_dist(*valid_params).logpdf(valid_samples)\\n except AttributeError:\\n expected = sp_dist(*valid_params).logpmf(valid_samples)\\n assert_allclose(d.log_prob(valid_samples), expected, atol=1e-05,\\n rtol=1e-05)\\n oob_samples = gen_values_outside_bounds(d.support, size=prepend_shape +\\n d.batch_shape + d.event_shape)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n d.log_prob(oob_samples)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n oob_samples = jax.device_get(oob_samples)\\n valid_params = jax.device_get(valid_params)\\n\\n def log_prob_fn():\\n d = jax_dist(*valid_params, validate_args=True)\\n return d.log_prob(oob_samples)\\n jax.jit(log_prob_fn)()\\n\\n\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_omnistaging_invalid_sample():\\n\\n def f(x):\\n return dist.LogNormal(x, np.ones(2), validate_args=True).log_prob(-1)\\n with pytest.warns(UserWarning, match='Out-of-support'):\\n jax.jit(f)(0)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_log_prob_gradient(jax_dist, sp_dist, params):\\n if jax_dist in [dist.LKJ, dist.LKJCholesky]:\\n pytest.skip('we have separated tests for LKJCholesky distribution')\\n if jax_dist is _ImproperWrapper:\\n pytest.skip(\\n 'no param for ImproperUniform to test for log_prob gradient')\\n rng_key = random.PRNGKey(0)\\n value = jax_dist(*params).sample(rng_key)\\n\\n def fn(*args):\\n return jnp.sum(jax_dist(*args).log_prob(value))\\n eps = 0.001\\n for i in range(len(params)):\\n if jax_dist is dist.EulerMaruyama and i == 1:\\n continue\\n if jax_dist is _SparseCAR and i == 3:\\n continue\\n if isinstance(params[i], dist.Distribution):\\n continue\\n if params[i] is None or jnp.result_type(params[i]) in (jnp.int32,\\n jnp.int64):\\n continue\\n actual_grad = jax.grad(fn, i)(*params)\\n args_lhs = [(p if j != i else p - eps) for j, p in enumerate(params)]\\n args_rhs = [(p if j != i else p + eps) for j, p in enumerate(params)]\\n fn_lhs = fn(*args_lhs)\\n fn_rhs = fn(*args_rhs)\\n expected_grad = (fn_rhs - fn_lhs) / (2.0 * eps)\\n assert jnp.shape(actual_grad) == jnp.shape(params[i])\\n if i == 0 and jax_dist is dist.Delta:\\n expected_grad = 0.0\\n assert_allclose(jnp.sum(actual_grad), expected_grad, rtol=0.01,\\n atol=0.01)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef test_transformed_transformed_distribution():\\n loc, scale = -2, 3\\n dist1 = dist.TransformedDistribution(dist.Normal(2, 3), transforms.\\n PowerTransform(2.0))\\n dist2 = dist.TransformedDistribution(dist1, transforms.AffineTransform(\\n -2, 3))\\n assert isinstance(dist2.base_dist, dist.Normal)\\n assert len(dist2.transforms) == 2\\n assert isinstance(dist2.transforms[0], transforms.PowerTransform)\\n assert isinstance(dist2.transforms[1], transforms.AffineTransform)\\n rng_key = random.PRNGKey(0)\\n assert_allclose(loc + scale * dist1.sample(rng_key), dist2.sample(rng_key))\\n intermediates = dist2.sample_with_intermediates(rng_key)\\n assert len(intermediates) == 2\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('constraint', [constraints.corr_cholesky,\\n constraints.corr_matrix, constraints.greater_than(2), constraints.\\n interval(-3, 5), constraints.l1_ball, constraints.less_than(1),\\n constraints.lower_cholesky, constraints.scaled_unit_lower_cholesky,\\n constraints.ordered_vector, constraints.positive, constraints.\\n positive_definite, constraints.positive_ordered_vector, constraints.\\n real, constraints.real_vector, constraints.simplex, constraints.\\n softplus_positive, constraints.softplus_lower_cholesky, constraints.\\n unit_interval, constraints.open_interval(0.0, 1.0)], ids=lambda x: x.\\n __class__)\\n@pytest.mark.parametrize('shape', [(), (1,), (3,), (6,), (3, 1), (1, 3), (5,\\n 3)])\\ndef test_biject_to(constraint, shape):\\n transform = biject_to(constraint)\\n event_dim = transform.domain.event_dim\\n if isinstance(constraint, constraints._Interval):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._GreaterThan):\\n assert transform.codomain.lower_bound == constraint.lower_bound\\n elif isinstance(constraint, constraints._LessThan):\\n assert transform.codomain.upper_bound == constraint.upper_bound\\n if len(shape) < event_dim:\\n return\\n rng_key = random.PRNGKey(0)\\n x = random.normal(rng_key, shape)\\n y = transform(x)\\n assert transform.forward_shape(x.shape) == y.shape\\n assert transform.inverse_shape(y.shape) == x.shape\\n x_nan = transform.inv(jnp.full(jnp.shape(y), np.nan))\\n assert x_nan.shape == x.shape\\n batch_shape = shape if event_dim == 0 else shape[:-1]\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape, dtype=\\n jnp.bool_))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-05, rtol=1e-05)\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == event_dim:\\n if constraint is constraints.simplex:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x)[:-1, :])[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y)\\n [:, :-1])[1]\\n elif constraint in [constraints.real_vector, constraints.\\n ordered_vector, constraints.positive_ordered_vector,\\n constraints.l1_ball]:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n elif constraint in [constraints.corr_cholesky, constraints.corr_matrix\\n ]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x),\\n diagonal=-1)\\n y_tril = matrix_to_tril_vec(y, diagonal=-1)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y, diagonal=-1)\\n if constraint is constraints.corr_matrix:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1\\n ) + jnp.identity(matrix.shape[-1])\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n elif constraint in [constraints.lower_cholesky, constraints.\\n scaled_unit_lower_cholesky, constraints.positive_definite,\\n constraints.softplus_lower_cholesky]:\\n vec_transform = lambda x: matrix_to_tril_vec(transform(x))\\n y_tril = matrix_to_tril_vec(y)\\n\\n def inv_vec_transform(y):\\n matrix = vec_to_tril_matrix(y)\\n if constraint is constraints.positive_definite:\\n matrix = matrix + jnp.swapaxes(matrix, -2, -1) - jnp.diag(\\n jnp.diag(matrix))\\n return transform.inv(matrix)\\n expected = np.linalg.slogdet(jax.jacobian(vec_transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(inv_vec_transform\\n )(y_tril))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-05, rtol=1e-05)\\n assert_allclose(actual, -inv_expected, atol=1e-05, rtol=1e-05)\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('transform, event_shape', [(PermuteTransform(np.\\n array([3, 0, 4, 1, 2])), (5,)), (PowerTransform(2.0), ()), (\\n SoftplusTransform(), ()), (LowerCholeskyAffine(np.array([1.0, 2.0]), np\\n .array([[0.6, 0.0], [1.5, 0.4]])), (2,)), (transforms.ComposeTransform(\\n [biject_to(constraints.simplex), SimplexToOrderedTransform(0.0),\\n biject_to(constraints.ordered_vector).inv]), (5,))])\\n@pytest.mark.parametrize('batch_shape', [(), (1,), (3,), (6,), (3, 1), (1, \\n 3), (5, 3)])\\ndef test_bijective_transforms(transform, event_shape, batch_shape):\\n shape = batch_shape + event_shape\\n rng_key = random.PRNGKey(0)\\n x = biject_to(transform.domain)(random.normal(rng_key, shape))\\n y = transform(x)\\n assert_array_equal(transform.codomain(y), jnp.ones(batch_shape))\\n z = transform.inv(y)\\n assert_allclose(x, z, atol=1e-06, rtol=0.0001)\\n assert transform.inv.inv is transform\\n assert transform.inv is transform.inv\\n assert transform.domain is transform.inv.codomain\\n assert transform.codomain is transform.inv.domain\\n assert_array_equal(transform.domain(z), jnp.ones(batch_shape))\\n actual = transform.log_abs_det_jacobian(x, y)\\n assert_allclose(actual, -transform.inv.log_abs_det_jacobian(y, x))\\n assert jnp.shape(actual) == batch_shape\\n if len(shape) == transform.domain.event_dim:\\n if len(event_shape) == 1:\\n expected = np.linalg.slogdet(jax.jacobian(transform)(x))[1]\\n inv_expected = np.linalg.slogdet(jax.jacobian(transform.inv)(y))[1]\\n else:\\n expected = jnp.log(jnp.abs(grad(transform)(x)))\\n inv_expected = jnp.log(jnp.abs(grad(transform.inv)(y)))\\n assert_allclose(actual, expected, atol=1e-06)\\n assert_allclose(actual, -inv_expected, atol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('rate', [0.1, 0.5, 0.9, 1.0, 1.1, 2.0, 10.0])\\ndef test_ZIP_log_prob(rate):\\n zip_ = dist.ZeroInflatedPoisson(0.0, rate)\\n pois = dist.Poisson(rate)\\n s = zip_.sample(random.PRNGKey(0), (20,))\\n zip_prob = zip_.log_prob(s)\\n pois_prob = pois.log_prob(s)\\n assert_allclose(zip_prob, pois_prob, rtol=1e-06)\\n zip_ = dist.ZeroInflatedPoisson(1.0, rate)\\n delta = dist.Delta(0.0)\\n s = np.array([0.0, 1.0])\\n zip_prob = zip_.log_prob(s)\\n delta_prob = delta.log_prob(s)\\n assert_allclose(zip_prob, delta_prob, rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef sde_fn2(xy, _):\\n tau, a = 2.0, 1.1\\n x, y = xy[0], xy[1]\\n dx = tau * (x - x ** 3.0 / 3.0 + y)\\n dy = 1.0 / tau * (a - x)\\n dxy = jnp.vstack([dx, dy]).reshape(xy.shape)\\n sigma2 = 0.1\\n return dxy, sigma2\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (3, 2)])\\ndef test_unit(batch_shape):\\n log_factor = random.normal(random.PRNGKey(0), batch_shape)\\n d = dist.Unit(log_factor=log_factor)\\n x = d.sample(random.PRNGKey(1))\\n assert x.shape == batch_shape + (0,)\\n assert (d.log_prob(x) == log_factor).all()\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\n@pytest.mark.parametrize('jit', [False, True])\\ndef test_log_prob(jax_dist, sp_dist, params, prepend_shape, jit):\\n jit_fn = _identity if not jit else jax.jit\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert jax_dist.log_prob(samples\\n ).shape == prepend_shape + jax_dist.batch_shape\\n truncated_dists = (dist.LeftTruncatedDistribution, dist.\\n RightTruncatedDistribution, dist.TwoSidedTruncatedDistribution)\\n if sp_dist is None:\\n if isinstance(jax_dist, truncated_dists):\\n if isinstance(params[0], dist.Distribution):\\n loc, scale, low, high = params[0].loc, params[0].scale, params[\\n 1], params[2]\\n else:\\n loc, scale, low, high = params\\n if low is None:\\n low = -np.inf\\n if high is None:\\n high = np.inf\\n sp_dist = get_sp_dist(type(jax_dist.base_dist))(loc, scale)\\n expected = sp_dist.logpdf(samples) - jnp.log(sp_dist.cdf(high) -\\n sp_dist.cdf(low))\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected,\\n atol=1e-05)\\n return\\n pytest.skip('no corresponding scipy distn.')\\n if _is_batched_multivariate(jax_dist):\\n pytest.skip('batching not allowed in multivariate distns.')\\n if jax_dist.event_shape and prepend_shape:\\n pytest.skip(\\n 'batched samples cannot be scored by multivariate distributions.')\\n sp_dist = sp_dist(*params)\\n try:\\n expected = sp_dist.logpdf(samples)\\n except AttributeError:\\n expected = sp_dist.logpmf(samples)\\n except ValueError as e:\\n if \\\"The input vector 'x' must lie within the normal simplex.\\\" in str(e\\n ):\\n samples = jax.device_get(samples).astype('float64')\\n samples = samples / samples.sum(axis=-1, keepdims=True)\\n expected = sp_dist.logpdf(samples)\\n else:\\n raise e\\n assert_allclose(jit_fn(jax_dist.log_prob)(samples), expected, atol=1e-05)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n\\n\\ndef gen_values_within_bounds(constraint, size, key=random.PRNGKey(11)):\\n eps = 1e-06\\n if constraint is constraints.boolean:\\n return random.bernoulli(key, shape=size)\\n elif isinstance(constraint, constraints.greater_than):\\n return jnp.exp(random.normal(key, size)) + constraint.lower_bound + eps\\n elif isinstance(constraint, constraints.integer_interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.randint(key, size, lower_bound, upper_bound + 1)\\n elif isinstance(constraint, constraints.integer_greater_than):\\n return constraint.lower_bound + random.poisson(key, np.array(5),\\n shape=size)\\n elif isinstance(constraint, constraints.interval):\\n lower_bound = jnp.broadcast_to(constraint.lower_bound, size)\\n upper_bound = jnp.broadcast_to(constraint.upper_bound, size)\\n return random.uniform(key, size, minval=lower_bound, maxval=upper_bound\\n )\\n elif constraint in (constraints.real, constraints.real_vector):\\n return random.normal(key, size)\\n elif constraint is constraints.simplex:\\n return osp.dirichlet.rvs(alpha=jnp.ones((size[-1],)), size=size[:-1])\\n elif isinstance(constraint, constraints.multinomial):\\n n = size[-1]\\n return multinomial(key, p=jnp.ones((n,)) / n, n=constraint.\\n upper_bound, shape=size[:-1])\\n elif constraint is constraints.corr_cholesky:\\n return signed_stick_breaking_tril(random.uniform(key, size[:-2] + (\\n size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n elif constraint is constraints.corr_matrix:\\n cholesky = signed_stick_breaking_tril(random.uniform(key, size[:-2] +\\n (size[-1] * (size[-1] - 1) // 2,), minval=-1, maxval=1))\\n return jnp.matmul(cholesky, jnp.swapaxes(cholesky, -2, -1))\\n elif constraint is constraints.lower_cholesky:\\n return jnp.tril(random.uniform(key, size))\\n elif constraint is constraints.positive_definite:\\n x = random.normal(key, size)\\n return jnp.matmul(x, jnp.swapaxes(x, -2, -1))\\n elif constraint is constraints.ordered_vector:\\n x = jnp.cumsum(random.exponential(key, size), -1)\\n return x - random.normal(key, size[:-1] + (1,))\\n elif isinstance(constraint, constraints.independent):\\n return gen_values_within_bounds(constraint.base_constraint, size, key)\\n elif constraint is constraints.sphere:\\n x = random.normal(key, size)\\n return x / jnp.linalg.norm(x, axis=-1)\\n elif constraint is constraints.l1_ball:\\n key1, key2 = random.split(key)\\n sign = random.bernoulli(key1)\\n bounds = [0, (-1) ** sign * 0.5]\\n return random.uniform(key, size, float, *sorted(bounds))\\n else:\\n raise NotImplementedError('{} not implemented.'.format(constraint))\\n\\n\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('batch_shape', [(), (4,), (2, 3)], ids=str)\\ndef test_kl_delta_normal_shape(batch_shape):\\n v = np.random.normal(size=batch_shape)\\n loc = np.random.normal(size=batch_shape)\\n scale = np.exp(np.random.normal(size=batch_shape))\\n p = dist.Delta(v)\\n q = dist.Normal(loc, scale)\\n assert kl_divergence(p, q).shape == batch_shape\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef _tree_equal(t1, t2):\\n t = jax.tree_util.tree_map(_allclose_or_equal, t1, t2)\\n return jnp.all(jax.flatten_util.ravel_pytree(t)[0])\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\n@pytest.mark.parametrize('prepend_shape', [(), (2,), (2, 3)])\\ndef test_dist_shape(jax_dist, sp_dist, params, prepend_shape):\\n jax_dist = jax_dist(*params)\\n rng_key = random.PRNGKey(0)\\n expected_shape = (prepend_shape + jax_dist.batch_shape + jax_dist.\\n event_shape)\\n samples = jax_dist.sample(key=rng_key, sample_shape=prepend_shape)\\n assert isinstance(samples, jnp.ndarray)\\n assert jnp.shape(samples) == expected_shape\\n if sp_dist and not _is_batched_multivariate(jax_dist) and not isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n sp_samples = sp_dist.rvs(size=prepend_shape + jax_dist.batch_shape)\\n assert jnp.shape(sp_samples) == expected_shape\\n elif sp_dist and not _is_batched_multivariate(jax_dist) and isinstance(\\n jax_dist, dist.MultivariateStudentT):\\n sp_dist = sp_dist(*params)\\n size_ = prepend_shape + jax_dist.batch_shape\\n size = 1 if size_ == () else size_\\n try:\\n sp_samples = sp_dist.rvs(size=size)\\n except ValueError:\\n pytest.skip(\\n \\\"scipy multivariate t doesn't support size with > 1 element\\\")\\n assert jnp.shape(sp_samples) == expected_shape\\n if isinstance(jax_dist, (dist.MultivariateNormal, dist.\\n MultivariateStudentT)):\\n assert jax_dist.covariance_matrix.ndim == len(jax_dist.batch_shape) + 2\\n assert_allclose(jax_dist.precision_matrix, jnp.linalg.inv(jax_dist.\\n covariance_matrix), rtol=1e-06)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef _lowrank_mvn_to_scipy(loc, cov_fac, cov_diag):\\n jax_dist = dist.LowRankMultivariateNormal(loc, cov_fac, cov_diag)\\n mean = jax_dist.mean\\n cov = jax_dist.covariance_matrix\\n return osp.multivariate_normal(mean=mean, cov=cov)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\n@pytest.mark.parametrize('jax_dist, sp_dist, params', CONTINUOUS + DISCRETE +\\n DIRECTIONAL)\\ndef test_vmap_dist(jax_dist, sp_dist, params):\\n param_names = list(inspect.signature(jax_dist).parameters.keys())\\n vmappable_param_idxs = _get_vmappable_dist_init_params(jax_dist)\\n vmappable_param_idxs = vmappable_param_idxs[:len(params)]\\n if len(vmappable_param_idxs) == 0:\\n return\\n\\n def make_jax_dist(*params):\\n return jax_dist(*params)\\n\\n def sample(d: dist.Distribution):\\n return d.sample(random.PRNGKey(0))\\n d = make_jax_dist(*params)\\n if isinstance(d, _SparseCAR) and d.is_sparse:\\n return\\n in_out_axes_cases = [(tuple(0 if i in vmappable_param_idxs else None for\\n i in range(len(params))), 0), *(([(0 if i == idx else None) for i in\\n range(len(params))], 0) for idx in vmappable_param_idxs if params[\\n idx] is not None), *(([(0 if i == idx else None) for i in range(len\\n (params))], vmap_over(d, **{param_names[idx]: 0})) for idx in\\n vmappable_param_idxs if params[idx] is not None), *(([(1 if i ==\\n idx else None) for i in range(len(params))], vmap_over(d, **{\\n param_names[idx]: 1})) for idx in vmappable_param_idxs if \\n isinstance(params[idx], jnp.ndarray) and jnp.array(params[idx]).\\n ndim > 0 and jax_dist is not _GeneralMixture)]\\n for in_axes, out_axes in in_out_axes_cases:\\n batched_params = [(jax.tree_map(lambda x: jnp.expand_dims(x, ax),\\n arg) if isinstance(ax, int) else arg) for arg, ax in zip(params,\\n in_axes)]\\n d = make_jax_dist(*params)\\n batched_d = jax.vmap(make_jax_dist, in_axes=in_axes, out_axes=out_axes\\n )(*batched_params)\\n eq = vmap(lambda x, y: _tree_equal(x, y), in_axes=(out_axes, None))(\\n batched_d, d)\\n assert eq == jnp.array([True])\\n samples_dist = sample(d)\\n samples_batched_dist = jax.vmap(sample, in_axes=(out_axes,))(batched_d)\\n assert samples_batched_dist.shape == (1, *samples_dist.shape)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n\\n def __new__(cls, jax_dist, *params):\\n sp_dist = get_sp_dist(jax_dist)\\n return super(cls, T).__new__(cls, jax_dist, sp_dist, params)\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\nclass T(namedtuple('TestCase', ['jax_dist', 'sp_dist', 'params'])):\\n <function token>\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n lower, upper = np.array([-math.pi, -math.pi]), np.array([math.pi,\\n math.pi])\\n base_dist = dist.Uniform(lower, upper, **kwargs).to_event(lower.ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n\\n\\nclass SineSkewedUniform(dist.SineSkewed):\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0]), np.array([1.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n\\n\\nclass SineSkewedVonMises(dist.SineSkewed):\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n\\n def __init__(self, skewness, **kwargs):\\n von_loc, von_conc = np.array([0.0, -1.234]), np.array([1.0, 10.0])\\n base_dist = dist.VonMises(von_loc, von_conc, **kwargs).to_event(von_loc\\n .ndim)\\n super().__init__(base_dist, skewness, **kwargs)\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass SineSkewedVonMisesBatched(dist.SineSkewed):\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n <function token>\\n\\n @property\\n def scale(self):\\n return self.component_distribution.scale\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, loc, scale):\\n component_dist = dist.Normal(loc=loc, scale=scale)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GaussianMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n\\n @property\\n def loc(self):\\n return self.component_distribution.loc\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n <function token>\\n\\n @property\\n def covariance_matrix(self):\\n return self.component_distribution.covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, loc, covariance_matrix):\\n component_dist = dist.MultivariateNormal(loc=loc, covariance_matrix\\n =covariance_matrix)\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n super().__init__(mixing_distribution=mixing_distribution,\\n component_distribution=component_dist)\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _Gaussian2DMixture(dist.MixtureSameFamily):\\n <assignment token>\\n <assignment token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n <function token>\\n\\n @property\\n def scales(self):\\n return self.component_distributions[0].scale\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, locs, scales):\\n component_dists = [dist.Normal(loc=loc_, scale=scale_) for loc_,\\n scale_ in zip(locs, scales)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _GeneralMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n arg_constraints = {}\\n reparametrized_params = []\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n\\n @property\\n def locs(self):\\n return self.component_distributions[0].loc\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, mixing_probs, locs, covariance_matrices):\\n component_dists = [dist.MultivariateNormal(loc=loc_,\\n covariance_matrix=covariance_matrix) for loc_,\\n covariance_matrix in zip(locs, covariance_matrices)]\\n mixing_distribution = dist.Categorical(probs=mixing_probs)\\n return super().__init__(mixing_distribution=mixing_distribution,\\n component_distributions=component_dists)\\n <function token>\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n <function token>\\n <function token>\\n\\n @property\\n def covariance_matrices(self):\\n return self.component_distributions[0].covariance_matrix\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _General2DMixture(dist.MixtureGeneral):\\n <assignment token>\\n <assignment token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n\\n def sample(self, key, sample_shape=()):\\n transform = biject_to(self.support)\\n prototype_value = jnp.zeros(self.event_shape)\\n unconstrained_event_shape = jnp.shape(transform.inv(prototype_value))\\n shape = sample_shape + self.batch_shape + unconstrained_event_shape\\n unconstrained_samples = random.uniform(key, shape, minval=-2, maxval=2)\\n return transform(unconstrained_samples)\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n\\n\\nclass _ImproperWrapper(dist.ImproperUniform):\\n <function token>\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n arg_constraints = {'rate': constraints.positive, 'gate_logits':\\n constraints.real}\\n pytree_data_fields = 'rate',\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n <assignment token>\\n <assignment token>\\n\\n def __init__(self, rate, gate_logits, *, validate_args=None):\\n self.rate = rate\\n super().__init__(dist.Poisson(rate), gate_logits, validate_args=\\n validate_args)\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n\\n\\nclass ZeroInflatedPoissonLogits(dist.discrete.ZeroInflatedLogits):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n\\n def __init__(self, rate, *, validate_args=None):\\n super().__init__(rate, is_sparse=True, validate_args=validate_args)\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n\\n\\nclass SparsePoisson(dist.Poisson):\\n <function token>\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n <assignment token>\\n\\n def __init__(self, loc, scale, validate_args=None):\\n self.loc = loc\\n self.scale = scale\\n super().__init__(dist.Normal(loc, scale), validate_args=validate_args)\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n\\n\\nclass FoldedNormal(dist.FoldedDistribution):\\n <assignment token>\\n <function token>\\n\\n\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n reparametrized_params = ['loc', 'correlation', 'conditional_precision']\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n <assignment token>\\n\\n def __init__(self, loc, correlation, conditional_precision, adj_matrix,\\n *, is_sparse=True, validate_args=None):\\n super().__init__(loc, correlation, conditional_precision,\\n adj_matrix, is_sparse=True, validate_args=validate_args)\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n\\n\\nclass _SparseCAR(dist.CAR):\\n <assignment token>\\n <function token>\\n\\n\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n<class token>\\n<function token>\\n<class token>\\n<assignment token>\\n<function token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7563,"cells":{"index":{"kind":"number","value":7563,"string":"7,563"},"blob_id":{"kind":"string","value":"d77036ed07231719358658a42dc14d20453bd792"},"code":{"kind":"string","value":"def pattern4(n):\n\t\"\"\"\n\tn: length of the base of the triangle ie. the max number\n\t\tof starts it will contain.\n\t\"\"\"\n\tfor row in range(1, n+1):\n\t\tfor col in range(1, row+1):\n\t\t\tprint(\"*\", end=\"\")\n\t\tprint(\"\")\nif __name__ == '__main__':\n\tn = int(input((\"Enter height of the triangle: \")))\n\tpattern4(n)\n"},"steps":{"kind":"list like","value":["def pattern4(n):\n\t\"\"\"\n\tn: length of the base of the triangle ie. the max number\n\t\tof starts it will contain.\n\t\"\"\"\n\tfor row in range(1, n+1):\n\t\tfor col in range(1, row+1):\n\t\t\tprint(\"*\", end=\"\")\n\t\tprint(\"\")\nif __name__ == '__main__':\n\tn = int(input((\"Enter height of the triangle: \")))\n\tpattern4(n)\n","def pattern4(n):\n \"\"\"\n\tn: length of the base of the triangle ie. the max number\n\t\tof starts it will contain.\n\t\"\"\"\n for row in range(1, n + 1):\n for col in range(1, row + 1):\n print('*', end='')\n print('')\n\n\nif __name__ == '__main__':\n n = int(input('Enter height of the triangle: '))\n pattern4(n)\n","def pattern4(n):\n \"\"\"\n\tn: length of the base of the triangle ie. the max number\n\t\tof starts it will contain.\n\t\"\"\"\n for row in range(1, n + 1):\n for col in range(1, row + 1):\n print('*', end='')\n print('')\n\n\n<code token>\n","<function token>\n<code token>\n"],"string":"[\n \"def pattern4(n):\\n\\t\\\"\\\"\\\"\\n\\tn: length of the base of the triangle ie. the max number\\n\\t\\tof starts it will contain.\\n\\t\\\"\\\"\\\"\\n\\tfor row in range(1, n+1):\\n\\t\\tfor col in range(1, row+1):\\n\\t\\t\\tprint(\\\"*\\\", end=\\\"\\\")\\n\\t\\tprint(\\\"\\\")\\nif __name__ == '__main__':\\n\\tn = int(input((\\\"Enter height of the triangle: \\\")))\\n\\tpattern4(n)\\n\",\n \"def pattern4(n):\\n \\\"\\\"\\\"\\n\\tn: length of the base of the triangle ie. the max number\\n\\t\\tof starts it will contain.\\n\\t\\\"\\\"\\\"\\n for row in range(1, n + 1):\\n for col in range(1, row + 1):\\n print('*', end='')\\n print('')\\n\\n\\nif __name__ == '__main__':\\n n = int(input('Enter height of the triangle: '))\\n pattern4(n)\\n\",\n \"def pattern4(n):\\n \\\"\\\"\\\"\\n\\tn: length of the base of the triangle ie. the max number\\n\\t\\tof starts it will contain.\\n\\t\\\"\\\"\\\"\\n for row in range(1, n + 1):\\n for col in range(1, row + 1):\\n print('*', end='')\\n print('')\\n\\n\\n<code token>\\n\",\n \"<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7564,"cells":{"index":{"kind":"number","value":7564,"string":"7,564"},"blob_id":{"kind":"string","value":"511016b9cd54f6824360d609ede233b9cc3e4447"},"code":{"kind":"string","value":"class Mood(object):\n\n GENERIC = 1\n HIGH_TEMP = 2\n LOW_TEMP = 3\n HIGH_DUST = 4\n LOW_DUST = 5\n\n def decision(self, data):\n temp = float(data)\n\n if temp <= 10:\n return self.LOW_TEMP\n\n if temp > 30:\n return self.HIGH_TEMP\n\n if (10 < temp <=30):\n return self.GENERIC\n"},"steps":{"kind":"list like","value":["class Mood(object):\n\n GENERIC = 1\n HIGH_TEMP = 2\n LOW_TEMP = 3\n HIGH_DUST = 4\n LOW_DUST = 5\n\n def decision(self, data):\n temp = float(data)\n\n if temp <= 10:\n return self.LOW_TEMP\n\n if temp > 30:\n return self.HIGH_TEMP\n\n if (10 < temp <=30):\n return self.GENERIC\n","class Mood(object):\n GENERIC = 1\n HIGH_TEMP = 2\n LOW_TEMP = 3\n HIGH_DUST = 4\n LOW_DUST = 5\n\n def decision(self, data):\n temp = float(data)\n if temp <= 10:\n return self.LOW_TEMP\n if temp > 30:\n return self.HIGH_TEMP\n if 10 < temp <= 30:\n return self.GENERIC\n","class Mood(object):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def decision(self, data):\n temp = float(data)\n if temp <= 10:\n return self.LOW_TEMP\n if temp > 30:\n return self.HIGH_TEMP\n if 10 < temp <= 30:\n return self.GENERIC\n","class Mood(object):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n","<class token>\n"],"string":"[\n \"class Mood(object):\\n\\n GENERIC = 1\\n HIGH_TEMP = 2\\n LOW_TEMP = 3\\n HIGH_DUST = 4\\n LOW_DUST = 5\\n\\n def decision(self, data):\\n temp = float(data)\\n\\n if temp <= 10:\\n return self.LOW_TEMP\\n\\n if temp > 30:\\n return self.HIGH_TEMP\\n\\n if (10 < temp <=30):\\n return self.GENERIC\\n\",\n \"class Mood(object):\\n GENERIC = 1\\n HIGH_TEMP = 2\\n LOW_TEMP = 3\\n HIGH_DUST = 4\\n LOW_DUST = 5\\n\\n def decision(self, data):\\n temp = float(data)\\n if temp <= 10:\\n return self.LOW_TEMP\\n if temp > 30:\\n return self.HIGH_TEMP\\n if 10 < temp <= 30:\\n return self.GENERIC\\n\",\n \"class Mood(object):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def decision(self, data):\\n temp = float(data)\\n if temp <= 10:\\n return self.LOW_TEMP\\n if temp > 30:\\n return self.HIGH_TEMP\\n if 10 < temp <= 30:\\n return self.GENERIC\\n\",\n \"class Mood(object):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\",\n \"<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7565,"cells":{"index":{"kind":"number","value":7565,"string":"7,565"},"blob_id":{"kind":"string","value":"9ad92b23b8a02204a86af599e507eb889e5bcec7"},"code":{"kind":"string","value":"#!/usr/bin/env python\n\nimport rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom styx_msgs.msg import Lane, Waypoint\nfrom scipy.spatial import KDTree\nimport numpy as np\nfrom std_msgs.msg import Int32\n\nimport math\n\n'''\nThis node will publish waypoints from the car's current position to some `x` distance ahead.\n\nAs mentioned in the doc, you should ideally first implement a version which does not care\nabout traffic lights or obstacles.\n\nOnce you have created dbw_node, you will update this node to use the status of traffic lights too.\n\nPlease note that our simulator also provides the exact location of traffic lights and their\ncurrent status in `/vehicle/traffic_lights` message. You can use this message to build this node\nas well as to verify your TL classifier.\n\nTODO (for Yousuf and Aaron): Stopline location for each traffic light.\n'''\n\nLOOKAHEAD_WPS = 60 # Number of waypoints we will publish. You can change this number\nMAX_DECEL = 0.5\nMPH_TO_MPS = 0.447\nMAX_SPEED = 20 # in MPH\n\n\nclass WaypointUpdater(object):\n def __init__(self):\n #rospy.loginfo('Entered WaypointUpdater init')\n rospy.init_node('waypoint_updater')\n\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n\n # TODO: Add a subscriber for /traffic_waypoint and /obstacle_waypoint below\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n\n\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane, queue_size=1)\n\n # TODO: Add other member variables you need below\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n\n #rospy.spin()\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n #Get closest waypoint\n #rospy.loginfo('Value of self pose is %d, %d',self.pose.pose.position.x, self.pose.pose.position.y)\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x,y], 1)[1]\n\n #Check if closest coord is ahead of or behind the vehicle\n #rospy.loginfo('Value of closest_idx is %d',closest_idx)\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx-1]\n\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x,y])\n\n val = np.dot(closest_vect-previous_vect, pos_vect-closest_vect)\n\n if val > 0:\n closest_idx = (closest_idx+1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx]\n\n #rospy.loginfo('Entered generate_lane, farthest_idx: %d', farthest_idx)\n if self.stopline_wp_idx == -1 or (self.stopline_wp_idx >= farthest_idx):\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo('Stopline index non-default: %d, closest_car index: %d', self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints, closest_idx)\n return lane\n\n def limitMaxSpeed(self, waypoints):\n velocity = MAX_SPEED * MPH_TO_MPS\n for i in range(len(waypoints)):\n self.set_waypoint_velocity(waypoints, i, velocity)\n return waypoints\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0) # 2 waypoints back from stopline so that the front of the car is at the line\n if i > stop_idx:\n vel = 0\n else:\n #rospy.loginfo('Decelerate waypoints index values: %d, stop_idx: %d', i, stop_idx)\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n #rospy.loginfo('Waypoint velocity for index %d is %f', i, p.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n #rospy.loginfo('Entered pose_cb')\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.pose.pose.position.y] for waypoint in waypoints.waypoints]\n #rospy.loginfo('First waypoint: %d, %d', waypoints.waypoints[0].pose.pose.position.x, waypoints.waypoints[0].pose.pose.position.y)\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n # TODO: Callback for /traffic_waypoint message. Implement\n \n self.stopline_wp_idx = msg.data\n #rospy.loginfo('traffic_cb called with stop_idx: %d', self.stopline_wp_idx)\n\n def obstacle_cb(self, msg):\n # TODO: Callback for /obstacle_waypoint message. We will implement it later\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x-b.x)**2 + (a.y-b.y)**2 + (a.z-b.z)**2)\n for i in range(wp1, wp2+1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose.pose.position)\n wp1 = i\n return dist\n\n\nif __name__ == '__main__':\n try:\n WaypointUpdater()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start waypoint updater node.')\n"},"steps":{"kind":"list like","value":["#!/usr/bin/env python\n\nimport rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom styx_msgs.msg import Lane, Waypoint\nfrom scipy.spatial import KDTree\nimport numpy as np\nfrom std_msgs.msg import Int32\n\nimport math\n\n'''\nThis node will publish waypoints from the car's current position to some `x` distance ahead.\n\nAs mentioned in the doc, you should ideally first implement a version which does not care\nabout traffic lights or obstacles.\n\nOnce you have created dbw_node, you will update this node to use the status of traffic lights too.\n\nPlease note that our simulator also provides the exact location of traffic lights and their\ncurrent status in `/vehicle/traffic_lights` message. You can use this message to build this node\nas well as to verify your TL classifier.\n\nTODO (for Yousuf and Aaron): Stopline location for each traffic light.\n'''\n\nLOOKAHEAD_WPS = 60 # Number of waypoints we will publish. You can change this number\nMAX_DECEL = 0.5\nMPH_TO_MPS = 0.447\nMAX_SPEED = 20 # in MPH\n\n\nclass WaypointUpdater(object):\n def __init__(self):\n #rospy.loginfo('Entered WaypointUpdater init')\n rospy.init_node('waypoint_updater')\n\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n\n # TODO: Add a subscriber for /traffic_waypoint and /obstacle_waypoint below\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n\n\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane, queue_size=1)\n\n # TODO: Add other member variables you need below\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n\n #rospy.spin()\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n #Get closest waypoint\n #rospy.loginfo('Value of self pose is %d, %d',self.pose.pose.position.x, self.pose.pose.position.y)\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x,y], 1)[1]\n\n #Check if closest coord is ahead of or behind the vehicle\n #rospy.loginfo('Value of closest_idx is %d',closest_idx)\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx-1]\n\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x,y])\n\n val = np.dot(closest_vect-previous_vect, pos_vect-closest_vect)\n\n if val > 0:\n closest_idx = (closest_idx+1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx]\n\n #rospy.loginfo('Entered generate_lane, farthest_idx: %d', farthest_idx)\n if self.stopline_wp_idx == -1 or (self.stopline_wp_idx >= farthest_idx):\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo('Stopline index non-default: %d, closest_car index: %d', self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints, closest_idx)\n return lane\n\n def limitMaxSpeed(self, waypoints):\n velocity = MAX_SPEED * MPH_TO_MPS\n for i in range(len(waypoints)):\n self.set_waypoint_velocity(waypoints, i, velocity)\n return waypoints\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0) # 2 waypoints back from stopline so that the front of the car is at the line\n if i > stop_idx:\n vel = 0\n else:\n #rospy.loginfo('Decelerate waypoints index values: %d, stop_idx: %d', i, stop_idx)\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n #rospy.loginfo('Waypoint velocity for index %d is %f', i, p.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n #rospy.loginfo('Entered pose_cb')\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.pose.pose.position.y] for waypoint in waypoints.waypoints]\n #rospy.loginfo('First waypoint: %d, %d', waypoints.waypoints[0].pose.pose.position.x, waypoints.waypoints[0].pose.pose.position.y)\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n # TODO: Callback for /traffic_waypoint message. Implement\n \n self.stopline_wp_idx = msg.data\n #rospy.loginfo('traffic_cb called with stop_idx: %d', self.stopline_wp_idx)\n\n def obstacle_cb(self, msg):\n # TODO: Callback for /obstacle_waypoint message. We will implement it later\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x-b.x)**2 + (a.y-b.y)**2 + (a.z-b.z)**2)\n for i in range(wp1, wp2+1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose.pose.position)\n wp1 = i\n return dist\n\n\nif __name__ == '__main__':\n try:\n WaypointUpdater()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start waypoint updater node.')\n","import rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom styx_msgs.msg import Lane, Waypoint\nfrom scipy.spatial import KDTree\nimport numpy as np\nfrom std_msgs.msg import Int32\nimport math\n<docstring token>\nLOOKAHEAD_WPS = 60\nMAX_DECEL = 0.5\nMPH_TO_MPS = 0.447\nMAX_SPEED = 20\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\n ]\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo(\n 'Stopline index non-default: %d, closest_car index: %d',\n self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\n closest_idx)\n return lane\n\n def limitMaxSpeed(self, waypoints):\n velocity = MAX_SPEED * MPH_TO_MPS\n for i in range(len(waypoints)):\n self.set_waypoint_velocity(waypoints, i, velocity)\n return waypoints\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.stopline_wp_idx = msg.data\n\n def obstacle_cb(self, msg):\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\nif __name__ == '__main__':\n try:\n WaypointUpdater()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start waypoint updater node.')\n","<import token>\n<docstring token>\nLOOKAHEAD_WPS = 60\nMAX_DECEL = 0.5\nMPH_TO_MPS = 0.447\nMAX_SPEED = 20\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\n ]\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo(\n 'Stopline index non-default: %d, closest_car index: %d',\n self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\n closest_idx)\n return lane\n\n def limitMaxSpeed(self, waypoints):\n velocity = MAX_SPEED * MPH_TO_MPS\n for i in range(len(waypoints)):\n self.set_waypoint_velocity(waypoints, i, velocity)\n return waypoints\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.stopline_wp_idx = msg.data\n\n def obstacle_cb(self, msg):\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\nif __name__ == '__main__':\n try:\n WaypointUpdater()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start waypoint updater node.')\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\n ]\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo(\n 'Stopline index non-default: %d, closest_car index: %d',\n self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\n closest_idx)\n return lane\n\n def limitMaxSpeed(self, waypoints):\n velocity = MAX_SPEED * MPH_TO_MPS\n for i in range(len(waypoints)):\n self.set_waypoint_velocity(waypoints, i, velocity)\n return waypoints\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.stopline_wp_idx = msg.data\n\n def obstacle_cb(self, msg):\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\nif __name__ == '__main__':\n try:\n WaypointUpdater()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start waypoint updater node.')\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\n ]\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo(\n 'Stopline index non-default: %d, closest_car index: %d',\n self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\n closest_idx)\n return lane\n\n def limitMaxSpeed(self, waypoints):\n velocity = MAX_SPEED * MPH_TO_MPS\n for i in range(len(waypoints)):\n self.set_waypoint_velocity(waypoints, i, velocity)\n return waypoints\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.stopline_wp_idx = msg.data\n\n def obstacle_cb(self, msg):\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\n ]\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo(\n 'Stopline index non-default: %d, closest_car index: %d',\n self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\n closest_idx)\n return lane\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.stopline_wp_idx = msg.data\n\n def obstacle_cb(self, msg):\n pass\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n\n def generate_lane(self):\n lane = Lane()\n lane.header = self.base_waypoints.header\n closest_idx = self.get_closest_waypoint_idx()\n farthest_idx = closest_idx + LOOKAHEAD_WPS\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\n ]\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\n else:\n rospy.loginfo(\n 'Stopline index non-default: %d, closest_car index: %d',\n self.stopline_wp_idx, closest_idx)\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\n closest_idx)\n return lane\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.stopline_wp_idx = msg.data\n <function token>\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.stopline_wp_idx = msg.data\n <function token>\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n <function token>\n <function token>\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\n waypoints[waypoint].twist.twist.linear.x = velocity\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n <function token>\n <function token>\n\n def get_waypoint_velocity(self, waypoint):\n return waypoint.twist.twist.linear.x\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n <function token>\n <function token>\n <function token>\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n\n def loop(self):\n rospy.loginfo('Entered WaypointUpdater loop')\n rate = rospy.Rate(50)\n while not rospy.is_shutdown():\n if self.pose and self.base_waypoints:\n closest_waypoint_idx = self.get_closest_waypoint_idx()\n self.publish_waypoints(closest_waypoint_idx)\n rate.sleep()\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n <function token>\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n <function token>\n <function token>\n <function token>\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n <function token>\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n <function token>\n\n def waypoints_cb(self, waypoints):\n self.base_waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\n pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n <function token>\n <function token>\n <function token>\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n <function token>\n\n def get_closest_waypoint_idx(self):\n x = self.pose.pose.position.x\n y = self.pose.pose.position.y\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\n closest_coord = self.waypoints_2d[closest_idx]\n prev_coord = self.waypoints_2d[closest_idx - 1]\n closest_vect = np.array(closest_coord)\n previous_vect = np.array(prev_coord)\n pos_vect = np.array([x, y])\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\n if val > 0:\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\n return closest_idx\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n\n def __init__(self):\n rospy.init_node('waypoint_updater')\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\n queue_size=1)\n self.pose = None\n self.base_waypoints = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.stopline_wp_idx = -1\n self.loop()\n <function token>\n <function token>\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n <function token>\n <function token>\n <function token>\n\n def publish_waypoints(self, closest_idx):\n final_lane = self.generate_lane()\n self.final_waypoints_pub.publish(final_lane)\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def distance(self, waypoints, wp1, wp2):\n dist = 0\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\n a.z - b.z) ** 2)\n for i in range(wp1, wp2 + 1):\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\n .pose.position)\n wp1 = i\n return dist\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def decelerate_waypoints(self, waypoints, closest_idx):\n tmp = []\n stopline_wp_idx = self.stopline_wp_idx\n for i, wp in enumerate(waypoints):\n p = Waypoint()\n p.pose = wp.pose\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\n if i > stop_idx:\n vel = 0\n else:\n dist = self.distance(waypoints, i, stop_idx)\n vel = math.sqrt(2 * MAX_DECEL * dist)\n if vel < 1.0:\n vel = 0.0\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\n tmp.append(p)\n return tmp\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n\n\nclass WaypointUpdater(object):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<code token>\n","<import token>\n<docstring token>\n<assignment token>\n<class token>\n<code token>\n"],"string":"[\n \"#!/usr/bin/env python\\n\\nimport rospy\\nfrom geometry_msgs.msg import PoseStamped\\nfrom styx_msgs.msg import Lane, Waypoint\\nfrom scipy.spatial import KDTree\\nimport numpy as np\\nfrom std_msgs.msg import Int32\\n\\nimport math\\n\\n'''\\nThis node will publish waypoints from the car's current position to some `x` distance ahead.\\n\\nAs mentioned in the doc, you should ideally first implement a version which does not care\\nabout traffic lights or obstacles.\\n\\nOnce you have created dbw_node, you will update this node to use the status of traffic lights too.\\n\\nPlease note that our simulator also provides the exact location of traffic lights and their\\ncurrent status in `/vehicle/traffic_lights` message. You can use this message to build this node\\nas well as to verify your TL classifier.\\n\\nTODO (for Yousuf and Aaron): Stopline location for each traffic light.\\n'''\\n\\nLOOKAHEAD_WPS = 60 # Number of waypoints we will publish. You can change this number\\nMAX_DECEL = 0.5\\nMPH_TO_MPS = 0.447\\nMAX_SPEED = 20 # in MPH\\n\\n\\nclass WaypointUpdater(object):\\n def __init__(self):\\n #rospy.loginfo('Entered WaypointUpdater init')\\n rospy.init_node('waypoint_updater')\\n\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n\\n # TODO: Add a subscriber for /traffic_waypoint and /obstacle_waypoint below\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n\\n\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane, queue_size=1)\\n\\n # TODO: Add other member variables you need below\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n\\n #rospy.spin()\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n #Get closest waypoint\\n #rospy.loginfo('Value of self pose is %d, %d',self.pose.pose.position.x, self.pose.pose.position.y)\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x,y], 1)[1]\\n\\n #Check if closest coord is ahead of or behind the vehicle\\n #rospy.loginfo('Value of closest_idx is %d',closest_idx)\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx-1]\\n\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x,y])\\n\\n val = np.dot(closest_vect-previous_vect, pos_vect-closest_vect)\\n\\n if val > 0:\\n closest_idx = (closest_idx+1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n\\n def generate_lane(self):\\n lane = Lane()\\n lane.header = self.base_waypoints.header\\n\\n closest_idx = self.get_closest_waypoint_idx()\\n farthest_idx = closest_idx + LOOKAHEAD_WPS\\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx]\\n\\n #rospy.loginfo('Entered generate_lane, farthest_idx: %d', farthest_idx)\\n if self.stopline_wp_idx == -1 or (self.stopline_wp_idx >= farthest_idx):\\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\\n else:\\n rospy.loginfo('Stopline index non-default: %d, closest_car index: %d', self.stopline_wp_idx, closest_idx)\\n lane.waypoints = self.decelerate_waypoints(base_waypoints, closest_idx)\\n return lane\\n\\n def limitMaxSpeed(self, waypoints):\\n velocity = MAX_SPEED * MPH_TO_MPS\\n for i in range(len(waypoints)):\\n self.set_waypoint_velocity(waypoints, i, velocity)\\n return waypoints\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0) # 2 waypoints back from stopline so that the front of the car is at the line\\n if i > stop_idx:\\n vel = 0\\n else:\\n #rospy.loginfo('Decelerate waypoints index values: %d, stop_idx: %d', i, stop_idx)\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n #rospy.loginfo('Waypoint velocity for index %d is %f', i, p.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n #rospy.loginfo('Entered pose_cb')\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.pose.pose.position.y] for waypoint in waypoints.waypoints]\\n #rospy.loginfo('First waypoint: %d, %d', waypoints.waypoints[0].pose.pose.position.x, waypoints.waypoints[0].pose.pose.position.y)\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n # TODO: Callback for /traffic_waypoint message. Implement\\n \\n self.stopline_wp_idx = msg.data\\n #rospy.loginfo('traffic_cb called with stop_idx: %d', self.stopline_wp_idx)\\n\\n def obstacle_cb(self, msg):\\n # TODO: Callback for /obstacle_waypoint message. We will implement it later\\n pass\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x-b.x)**2 + (a.y-b.y)**2 + (a.z-b.z)**2)\\n for i in range(wp1, wp2+1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose.pose.position)\\n wp1 = i\\n return dist\\n\\n\\nif __name__ == '__main__':\\n try:\\n WaypointUpdater()\\n except rospy.ROSInterruptException:\\n rospy.logerr('Could not start waypoint updater node.')\\n\",\n \"import rospy\\nfrom geometry_msgs.msg import PoseStamped\\nfrom styx_msgs.msg import Lane, Waypoint\\nfrom scipy.spatial import KDTree\\nimport numpy as np\\nfrom std_msgs.msg import Int32\\nimport math\\n<docstring token>\\nLOOKAHEAD_WPS = 60\\nMAX_DECEL = 0.5\\nMPH_TO_MPS = 0.447\\nMAX_SPEED = 20\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n\\n def generate_lane(self):\\n lane = Lane()\\n lane.header = self.base_waypoints.header\\n closest_idx = self.get_closest_waypoint_idx()\\n farthest_idx = closest_idx + LOOKAHEAD_WPS\\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\\n ]\\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\\n else:\\n rospy.loginfo(\\n 'Stopline index non-default: %d, closest_car index: %d',\\n self.stopline_wp_idx, closest_idx)\\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\\n closest_idx)\\n return lane\\n\\n def limitMaxSpeed(self, waypoints):\\n velocity = MAX_SPEED * MPH_TO_MPS\\n for i in range(len(waypoints)):\\n self.set_waypoint_velocity(waypoints, i, velocity)\\n return waypoints\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n self.stopline_wp_idx = msg.data\\n\\n def obstacle_cb(self, msg):\\n pass\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\nif __name__ == '__main__':\\n try:\\n WaypointUpdater()\\n except rospy.ROSInterruptException:\\n rospy.logerr('Could not start waypoint updater node.')\\n\",\n \"<import token>\\n<docstring token>\\nLOOKAHEAD_WPS = 60\\nMAX_DECEL = 0.5\\nMPH_TO_MPS = 0.447\\nMAX_SPEED = 20\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n\\n def generate_lane(self):\\n lane = Lane()\\n lane.header = self.base_waypoints.header\\n closest_idx = self.get_closest_waypoint_idx()\\n farthest_idx = closest_idx + LOOKAHEAD_WPS\\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\\n ]\\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\\n else:\\n rospy.loginfo(\\n 'Stopline index non-default: %d, closest_car index: %d',\\n self.stopline_wp_idx, closest_idx)\\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\\n closest_idx)\\n return lane\\n\\n def limitMaxSpeed(self, waypoints):\\n velocity = MAX_SPEED * MPH_TO_MPS\\n for i in range(len(waypoints)):\\n self.set_waypoint_velocity(waypoints, i, velocity)\\n return waypoints\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n self.stopline_wp_idx = msg.data\\n\\n def obstacle_cb(self, msg):\\n pass\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\nif __name__ == '__main__':\\n try:\\n WaypointUpdater()\\n except rospy.ROSInterruptException:\\n rospy.logerr('Could not start waypoint updater node.')\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n\\n def generate_lane(self):\\n lane = Lane()\\n lane.header = self.base_waypoints.header\\n closest_idx = self.get_closest_waypoint_idx()\\n farthest_idx = closest_idx + LOOKAHEAD_WPS\\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\\n ]\\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\\n else:\\n rospy.loginfo(\\n 'Stopline index non-default: %d, closest_car index: %d',\\n self.stopline_wp_idx, closest_idx)\\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\\n closest_idx)\\n return lane\\n\\n def limitMaxSpeed(self, waypoints):\\n velocity = MAX_SPEED * MPH_TO_MPS\\n for i in range(len(waypoints)):\\n self.set_waypoint_velocity(waypoints, i, velocity)\\n return waypoints\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n self.stopline_wp_idx = msg.data\\n\\n def obstacle_cb(self, msg):\\n pass\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\nif __name__ == '__main__':\\n try:\\n WaypointUpdater()\\n except rospy.ROSInterruptException:\\n rospy.logerr('Could not start waypoint updater node.')\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n\\n def generate_lane(self):\\n lane = Lane()\\n lane.header = self.base_waypoints.header\\n closest_idx = self.get_closest_waypoint_idx()\\n farthest_idx = closest_idx + LOOKAHEAD_WPS\\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\\n ]\\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\\n else:\\n rospy.loginfo(\\n 'Stopline index non-default: %d, closest_car index: %d',\\n self.stopline_wp_idx, closest_idx)\\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\\n closest_idx)\\n return lane\\n\\n def limitMaxSpeed(self, waypoints):\\n velocity = MAX_SPEED * MPH_TO_MPS\\n for i in range(len(waypoints)):\\n self.set_waypoint_velocity(waypoints, i, velocity)\\n return waypoints\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n self.stopline_wp_idx = msg.data\\n\\n def obstacle_cb(self, msg):\\n pass\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n\\n def generate_lane(self):\\n lane = Lane()\\n lane.header = self.base_waypoints.header\\n closest_idx = self.get_closest_waypoint_idx()\\n farthest_idx = closest_idx + LOOKAHEAD_WPS\\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\\n ]\\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\\n else:\\n rospy.loginfo(\\n 'Stopline index non-default: %d, closest_car index: %d',\\n self.stopline_wp_idx, closest_idx)\\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\\n closest_idx)\\n return lane\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n self.stopline_wp_idx = msg.data\\n\\n def obstacle_cb(self, msg):\\n pass\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n\\n def generate_lane(self):\\n lane = Lane()\\n lane.header = self.base_waypoints.header\\n closest_idx = self.get_closest_waypoint_idx()\\n farthest_idx = closest_idx + LOOKAHEAD_WPS\\n base_waypoints = self.base_waypoints.waypoints[closest_idx:farthest_idx\\n ]\\n if self.stopline_wp_idx == -1 or self.stopline_wp_idx >= farthest_idx:\\n lane.waypoints = self.limitMaxSpeed(base_waypoints)\\n else:\\n rospy.loginfo(\\n 'Stopline index non-default: %d, closest_car index: %d',\\n self.stopline_wp_idx, closest_idx)\\n lane.waypoints = self.decelerate_waypoints(base_waypoints,\\n closest_idx)\\n return lane\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n self.stopline_wp_idx = msg.data\\n <function token>\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n\\n def traffic_cb(self, msg):\\n self.stopline_wp_idx = msg.data\\n <function token>\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n <function token>\\n <function token>\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n\\n def set_waypoint_velocity(self, waypoints, waypoint, velocity):\\n waypoints[waypoint].twist.twist.linear.x = velocity\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n <function token>\\n <function token>\\n\\n def get_waypoint_velocity(self, waypoint):\\n return waypoint.twist.twist.linear.x\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n\\n def pose_cb(self, msg):\\n self.pose = msg\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n\\n def loop(self):\\n rospy.loginfo('Entered WaypointUpdater loop')\\n rate = rospy.Rate(50)\\n while not rospy.is_shutdown():\\n if self.pose and self.base_waypoints:\\n closest_waypoint_idx = self.get_closest_waypoint_idx()\\n self.publish_waypoints(closest_waypoint_idx)\\n rate.sleep()\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n <function token>\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n <function token>\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n <function token>\\n\\n def waypoints_cb(self, waypoints):\\n self.base_waypoints = waypoints\\n if not self.waypoints_2d:\\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.\\n pose.pose.position.y] for waypoint in waypoints.waypoints]\\n self.waypoint_tree = KDTree(self.waypoints_2d)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n <function token>\\n\\n def get_closest_waypoint_idx(self):\\n x = self.pose.pose.position.x\\n y = self.pose.pose.position.y\\n closest_idx = self.waypoint_tree.query([x, y], 1)[1]\\n closest_coord = self.waypoints_2d[closest_idx]\\n prev_coord = self.waypoints_2d[closest_idx - 1]\\n closest_vect = np.array(closest_coord)\\n previous_vect = np.array(prev_coord)\\n pos_vect = np.array([x, y])\\n val = np.dot(closest_vect - previous_vect, pos_vect - closest_vect)\\n if val > 0:\\n closest_idx = (closest_idx + 1) % len(self.waypoints_2d)\\n return closest_idx\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n\\n def __init__(self):\\n rospy.init_node('waypoint_updater')\\n rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\\n rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\\n rospy.Subscriber('/traffic_waypoint', Int32, self.traffic_cb)\\n self.final_waypoints_pub = rospy.Publisher('final_waypoints', Lane,\\n queue_size=1)\\n self.pose = None\\n self.base_waypoints = None\\n self.waypoints_2d = None\\n self.waypoint_tree = None\\n self.stopline_wp_idx = -1\\n self.loop()\\n <function token>\\n <function token>\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n <function token>\\n <function token>\\n <function token>\\n\\n def publish_waypoints(self, closest_idx):\\n final_lane = self.generate_lane()\\n self.final_waypoints_pub.publish(final_lane)\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def distance(self, waypoints, wp1, wp2):\\n dist = 0\\n dl = lambda a, b: math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2 + (\\n a.z - b.z) ** 2)\\n for i in range(wp1, wp2 + 1):\\n dist += dl(waypoints[wp1].pose.pose.position, waypoints[i].pose\\n .pose.position)\\n wp1 = i\\n return dist\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def decelerate_waypoints(self, waypoints, closest_idx):\\n tmp = []\\n stopline_wp_idx = self.stopline_wp_idx\\n for i, wp in enumerate(waypoints):\\n p = Waypoint()\\n p.pose = wp.pose\\n stop_idx = max(stopline_wp_idx - 5 - closest_idx, 0)\\n if i > stop_idx:\\n vel = 0\\n else:\\n dist = self.distance(waypoints, i, stop_idx)\\n vel = math.sqrt(2 * MAX_DECEL * dist)\\n if vel < 1.0:\\n vel = 0.0\\n p.twist.twist.linear.x = min(vel, wp.twist.twist.linear.x)\\n tmp.append(p)\\n return tmp\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n\\n\\nclass WaypointUpdater(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<docstring token>\\n<assignment token>\\n<class token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7566,"cells":{"index":{"kind":"number","value":7566,"string":"7,566"},"blob_id":{"kind":"string","value":"1896f4d5b304915d5cbbb30b0a83854c4a8cc60c"},"code":{"kind":"string","value":"from wtforms import Form, StringField\n\nclass SearchForm(Form):\n\tcriteria = StringField(\"Texto a buscar\")\n\t\n"},"steps":{"kind":"list like","value":["from wtforms import Form, StringField\n\nclass SearchForm(Form):\n\tcriteria = StringField(\"Texto a buscar\")\n\t\n","from wtforms import Form, StringField\n\n\nclass SearchForm(Form):\n criteria = StringField('Texto a buscar')\n","<import token>\n\n\nclass SearchForm(Form):\n criteria = StringField('Texto a buscar')\n","<import token>\n\n\nclass SearchForm(Form):\n <assignment token>\n","<import token>\n<class token>\n"],"string":"[\n \"from wtforms import Form, StringField\\n\\nclass SearchForm(Form):\\n\\tcriteria = StringField(\\\"Texto a buscar\\\")\\n\\t\\n\",\n \"from wtforms import Form, StringField\\n\\n\\nclass SearchForm(Form):\\n criteria = StringField('Texto a buscar')\\n\",\n \"<import token>\\n\\n\\nclass SearchForm(Form):\\n criteria = StringField('Texto a buscar')\\n\",\n \"<import token>\\n\\n\\nclass SearchForm(Form):\\n <assignment token>\\n\",\n \"<import token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7567,"cells":{"index":{"kind":"number","value":7567,"string":"7,567"},"blob_id":{"kind":"string","value":"42a717591fb8fe480581d8996e9811d0292d0eb1"},"code":{"kind":"string","value":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom cv2 import DualTVL1OpticalFlow_create as DualTVL1\nfrom tensorflow.python.platform import flags\nimport os\nimport sys\n\nsys.path.insert(0, '..')\nfrom utils import image_funcs\n\nimport numpy as np\n\n\ndef make_dir(directory):\n import os\n import errno\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string('expected_rgb_frame_suffix', \".jpg\", 'Expected RGB frame files\\' suffix.')\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n\n # Collect RGB frame paths.\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files\n if frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, \"Only find %d (<=2) RGB frames under %s.\" % (num_frames, video_frames_folder)\n\n # Iteratively compute optical flows.\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert (cur_flow.dtype == np.float32)\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx+i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\ndef main():\n optical_flows = compute_optical_flow_tvl1(\"\")\n frame_file_pattern = \"%05d.jpg\"\n folder = \"\"\n file_pattern = os.path.join(folder, frame_file_pattern)\n save_images(optical_flows, file_pattern)\n\n\nif __name__ == '__main__':\n main()\n"},"steps":{"kind":"list like","value":["from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom cv2 import DualTVL1OpticalFlow_create as DualTVL1\nfrom tensorflow.python.platform import flags\nimport os\nimport sys\n\nsys.path.insert(0, '..')\nfrom utils import image_funcs\n\nimport numpy as np\n\n\ndef make_dir(directory):\n import os\n import errno\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string('expected_rgb_frame_suffix', \".jpg\", 'Expected RGB frame files\\' suffix.')\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n\n # Collect RGB frame paths.\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files\n if frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, \"Only find %d (<=2) RGB frames under %s.\" % (num_frames, video_frames_folder)\n\n # Iteratively compute optical flows.\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert (cur_flow.dtype == np.float32)\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx+i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\ndef main():\n optical_flows = compute_optical_flow_tvl1(\"\")\n frame_file_pattern = \"%05d.jpg\"\n folder = \"\"\n file_pattern = os.path.join(folder, frame_file_pattern)\n save_images(optical_flows, file_pattern)\n\n\nif __name__ == '__main__':\n main()\n","from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom cv2 import DualTVL1OpticalFlow_create as DualTVL1\nfrom tensorflow.python.platform import flags\nimport os\nimport sys\nsys.path.insert(0, '..')\nfrom utils import image_funcs\nimport numpy as np\n\n\ndef make_dir(directory):\n import os\n import errno\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string('expected_rgb_frame_suffix', '.jpg',\n \"Expected RGB frame files' suffix.\")\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\n num_frames, video_frames_folder)\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert cur_flow.dtype == np.float32\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx + i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\ndef main():\n optical_flows = compute_optical_flow_tvl1('')\n frame_file_pattern = '%05d.jpg'\n folder = ''\n file_pattern = os.path.join(folder, frame_file_pattern)\n save_images(optical_flows, file_pattern)\n\n\nif __name__ == '__main__':\n main()\n","<import token>\nsys.path.insert(0, '..')\n<import token>\n\n\ndef make_dir(directory):\n import os\n import errno\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string('expected_rgb_frame_suffix', '.jpg',\n \"Expected RGB frame files' suffix.\")\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\n num_frames, video_frames_folder)\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert cur_flow.dtype == np.float32\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx + i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\ndef main():\n optical_flows = compute_optical_flow_tvl1('')\n frame_file_pattern = '%05d.jpg'\n folder = ''\n file_pattern = os.path.join(folder, frame_file_pattern)\n save_images(optical_flows, file_pattern)\n\n\nif __name__ == '__main__':\n main()\n","<import token>\nsys.path.insert(0, '..')\n<import token>\n\n\ndef make_dir(directory):\n import os\n import errno\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\n<assignment token>\nflags.DEFINE_string('expected_rgb_frame_suffix', '.jpg',\n \"Expected RGB frame files' suffix.\")\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\n num_frames, video_frames_folder)\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert cur_flow.dtype == np.float32\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx + i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\ndef main():\n optical_flows = compute_optical_flow_tvl1('')\n frame_file_pattern = '%05d.jpg'\n folder = ''\n file_pattern = os.path.join(folder, frame_file_pattern)\n save_images(optical_flows, file_pattern)\n\n\nif __name__ == '__main__':\n main()\n","<import token>\n<code token>\n<import token>\n\n\ndef make_dir(directory):\n import os\n import errno\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\n<assignment token>\n<code token>\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\n num_frames, video_frames_folder)\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert cur_flow.dtype == np.float32\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx + i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\ndef main():\n optical_flows = compute_optical_flow_tvl1('')\n frame_file_pattern = '%05d.jpg'\n folder = ''\n file_pattern = os.path.join(folder, frame_file_pattern)\n save_images(optical_flows, file_pattern)\n\n\n<code token>\n","<import token>\n<code token>\n<import token>\n<function token>\n<assignment token>\n<code token>\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\n num_frames, video_frames_folder)\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert cur_flow.dtype == np.float32\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx + i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\ndef main():\n optical_flows = compute_optical_flow_tvl1('')\n frame_file_pattern = '%05d.jpg'\n folder = ''\n file_pattern = os.path.join(folder, frame_file_pattern)\n save_images(optical_flows, file_pattern)\n\n\n<code token>\n","<import token>\n<code token>\n<import token>\n<function token>\n<assignment token>\n<code token>\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\n num_frames, video_frames_folder)\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert cur_flow.dtype == np.float32\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\ndef save_images(images, file_pattern, start_idx=1):\n for i, image in enumerate(images):\n file_path = file_pattern % (start_idx + i)\n make_dir(file_path)\n image_funcs.save_image(image, file_path)\n\n\n<function token>\n<code token>\n","<import token>\n<code token>\n<import token>\n<function token>\n<assignment token>\n<code token>\n\n\ndef compute_optical_flow_tvl1(video_frames_folder):\n \"\"\"Compute the TV-L1 optical flow.\"\"\"\n TVL1 = DualTVL1()\n rgb_frame_files = os.listdir(video_frames_folder)\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\n rgb_frame_files.sort()\n num_frames = len(rgb_frame_files)\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\n num_frames, video_frames_folder)\n optical_flows = []\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[0], to_float=False))\n for i in range(1, num_frames):\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\n rgb_frame_files[1], to_float=False))\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\n assert cur_flow.dtype == np.float32\n optical_flows.append(cur_flow)\n prev_frame = cur_frame\n return optical_flows\n\n\n<function token>\n<function token>\n<code token>\n","<import token>\n<code token>\n<import token>\n<function token>\n<assignment token>\n<code token>\n<function token>\n<function token>\n<function token>\n<code token>\n"],"string":"[\n \"from __future__ import absolute_import\\nfrom __future__ import division\\nfrom __future__ import print_function\\n\\nfrom cv2 import DualTVL1OpticalFlow_create as DualTVL1\\nfrom tensorflow.python.platform import flags\\nimport os\\nimport sys\\n\\nsys.path.insert(0, '..')\\nfrom utils import image_funcs\\n\\nimport numpy as np\\n\\n\\ndef make_dir(directory):\\n import os\\n import errno\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n\\n\\nFLAGS = flags.FLAGS\\nflags.DEFINE_string('expected_rgb_frame_suffix', \\\".jpg\\\", 'Expected RGB frame files\\\\' suffix.')\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n\\n # Collect RGB frame paths.\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files\\n if frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, \\\"Only find %d (<=2) RGB frames under %s.\\\" % (num_frames, video_frames_folder)\\n\\n # Iteratively compute optical flows.\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert (cur_flow.dtype == np.float32)\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\ndef save_images(images, file_pattern, start_idx=1):\\n for i, image in enumerate(images):\\n file_path = file_pattern % (start_idx+i)\\n make_dir(file_path)\\n image_funcs.save_image(image, file_path)\\n\\n\\ndef main():\\n optical_flows = compute_optical_flow_tvl1(\\\"\\\")\\n frame_file_pattern = \\\"%05d.jpg\\\"\\n folder = \\\"\\\"\\n file_pattern = os.path.join(folder, frame_file_pattern)\\n save_images(optical_flows, file_pattern)\\n\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"from __future__ import absolute_import\\nfrom __future__ import division\\nfrom __future__ import print_function\\nfrom cv2 import DualTVL1OpticalFlow_create as DualTVL1\\nfrom tensorflow.python.platform import flags\\nimport os\\nimport sys\\nsys.path.insert(0, '..')\\nfrom utils import image_funcs\\nimport numpy as np\\n\\n\\ndef make_dir(directory):\\n import os\\n import errno\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n\\n\\nFLAGS = flags.FLAGS\\nflags.DEFINE_string('expected_rgb_frame_suffix', '.jpg',\\n \\\"Expected RGB frame files' suffix.\\\")\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\\n num_frames, video_frames_folder)\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert cur_flow.dtype == np.float32\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\ndef save_images(images, file_pattern, start_idx=1):\\n for i, image in enumerate(images):\\n file_path = file_pattern % (start_idx + i)\\n make_dir(file_path)\\n image_funcs.save_image(image, file_path)\\n\\n\\ndef main():\\n optical_flows = compute_optical_flow_tvl1('')\\n frame_file_pattern = '%05d.jpg'\\n folder = ''\\n file_pattern = os.path.join(folder, frame_file_pattern)\\n save_images(optical_flows, file_pattern)\\n\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"<import token>\\nsys.path.insert(0, '..')\\n<import token>\\n\\n\\ndef make_dir(directory):\\n import os\\n import errno\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n\\n\\nFLAGS = flags.FLAGS\\nflags.DEFINE_string('expected_rgb_frame_suffix', '.jpg',\\n \\\"Expected RGB frame files' suffix.\\\")\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\\n num_frames, video_frames_folder)\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert cur_flow.dtype == np.float32\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\ndef save_images(images, file_pattern, start_idx=1):\\n for i, image in enumerate(images):\\n file_path = file_pattern % (start_idx + i)\\n make_dir(file_path)\\n image_funcs.save_image(image, file_path)\\n\\n\\ndef main():\\n optical_flows = compute_optical_flow_tvl1('')\\n frame_file_pattern = '%05d.jpg'\\n folder = ''\\n file_pattern = os.path.join(folder, frame_file_pattern)\\n save_images(optical_flows, file_pattern)\\n\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"<import token>\\nsys.path.insert(0, '..')\\n<import token>\\n\\n\\ndef make_dir(directory):\\n import os\\n import errno\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n\\n\\n<assignment token>\\nflags.DEFINE_string('expected_rgb_frame_suffix', '.jpg',\\n \\\"Expected RGB frame files' suffix.\\\")\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\\n num_frames, video_frames_folder)\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert cur_flow.dtype == np.float32\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\ndef save_images(images, file_pattern, start_idx=1):\\n for i, image in enumerate(images):\\n file_path = file_pattern % (start_idx + i)\\n make_dir(file_path)\\n image_funcs.save_image(image, file_path)\\n\\n\\ndef main():\\n optical_flows = compute_optical_flow_tvl1('')\\n frame_file_pattern = '%05d.jpg'\\n folder = ''\\n file_pattern = os.path.join(folder, frame_file_pattern)\\n save_images(optical_flows, file_pattern)\\n\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"<import token>\\n<code token>\\n<import token>\\n\\n\\ndef make_dir(directory):\\n import os\\n import errno\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n\\n\\n<assignment token>\\n<code token>\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\\n num_frames, video_frames_folder)\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert cur_flow.dtype == np.float32\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\ndef save_images(images, file_pattern, start_idx=1):\\n for i, image in enumerate(images):\\n file_path = file_pattern % (start_idx + i)\\n make_dir(file_path)\\n image_funcs.save_image(image, file_path)\\n\\n\\ndef main():\\n optical_flows = compute_optical_flow_tvl1('')\\n frame_file_pattern = '%05d.jpg'\\n folder = ''\\n file_pattern = os.path.join(folder, frame_file_pattern)\\n save_images(optical_flows, file_pattern)\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<import token>\\n<function token>\\n<assignment token>\\n<code token>\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\\n num_frames, video_frames_folder)\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert cur_flow.dtype == np.float32\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\ndef save_images(images, file_pattern, start_idx=1):\\n for i, image in enumerate(images):\\n file_path = file_pattern % (start_idx + i)\\n make_dir(file_path)\\n image_funcs.save_image(image, file_path)\\n\\n\\ndef main():\\n optical_flows = compute_optical_flow_tvl1('')\\n frame_file_pattern = '%05d.jpg'\\n folder = ''\\n file_pattern = os.path.join(folder, frame_file_pattern)\\n save_images(optical_flows, file_pattern)\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<import token>\\n<function token>\\n<assignment token>\\n<code token>\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\\n num_frames, video_frames_folder)\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert cur_flow.dtype == np.float32\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\ndef save_images(images, file_pattern, start_idx=1):\\n for i, image in enumerate(images):\\n file_path = file_pattern % (start_idx + i)\\n make_dir(file_path)\\n image_funcs.save_image(image, file_path)\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<import token>\\n<function token>\\n<assignment token>\\n<code token>\\n\\n\\ndef compute_optical_flow_tvl1(video_frames_folder):\\n \\\"\\\"\\\"Compute the TV-L1 optical flow.\\\"\\\"\\\"\\n TVL1 = DualTVL1()\\n rgb_frame_files = os.listdir(video_frames_folder)\\n rgb_frame_files = [frame_file for frame_file in rgb_frame_files if\\n frame_file.endswith(FLAGS.expected_rgb_frame_suffix)]\\n rgb_frame_files.sort()\\n num_frames = len(rgb_frame_files)\\n assert num_frames >= 2, 'Only find %d (<=2) RGB frames under %s.' % (\\n num_frames, video_frames_folder)\\n optical_flows = []\\n prev_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[0], to_float=False))\\n for i in range(1, num_frames):\\n cur_frame = image_funcs.rgb_to_gray(image_funcs.read_image(\\n rgb_frame_files[1], to_float=False))\\n cur_flow = TVL1.calc(prev_frame, cur_frame, None)\\n assert cur_flow.dtype == np.float32\\n optical_flows.append(cur_flow)\\n prev_frame = cur_frame\\n return optical_flows\\n\\n\\n<function token>\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<import token>\\n<function token>\\n<assignment token>\\n<code token>\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7568,"cells":{"index":{"kind":"number","value":7568,"string":"7,568"},"blob_id":{"kind":"string","value":"c7553cadb49c9c7e80a7800b9bff4d5f64796494"},"code":{"kind":"string","value":"import pytest\nfrom a3 import *\nfrom test_utils import *\nfrom numpy import allclose\n\ndef test_problem_7_1_8():\n assert(check_linalg())\n assert(abs(problem_7_1_8(5000)-84.8)<1)\n"},"steps":{"kind":"list like","value":["import pytest\nfrom a3 import *\nfrom test_utils import *\nfrom numpy import allclose\n\ndef test_problem_7_1_8():\n assert(check_linalg())\n assert(abs(problem_7_1_8(5000)-84.8)<1)\n","import pytest\nfrom a3 import *\nfrom test_utils import *\nfrom numpy import allclose\n\n\ndef test_problem_7_1_8():\n assert check_linalg()\n assert abs(problem_7_1_8(5000) - 84.8) < 1\n","<import token>\n\n\ndef test_problem_7_1_8():\n assert check_linalg()\n assert abs(problem_7_1_8(5000) - 84.8) < 1\n","<import token>\n<function token>\n"],"string":"[\n \"import pytest\\nfrom a3 import *\\nfrom test_utils import *\\nfrom numpy import allclose\\n\\ndef test_problem_7_1_8():\\n assert(check_linalg())\\n assert(abs(problem_7_1_8(5000)-84.8)<1)\\n\",\n \"import pytest\\nfrom a3 import *\\nfrom test_utils import *\\nfrom numpy import allclose\\n\\n\\ndef test_problem_7_1_8():\\n assert check_linalg()\\n assert abs(problem_7_1_8(5000) - 84.8) < 1\\n\",\n \"<import token>\\n\\n\\ndef test_problem_7_1_8():\\n assert check_linalg()\\n assert abs(problem_7_1_8(5000) - 84.8) < 1\\n\",\n \"<import token>\\n<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7569,"cells":{"index":{"kind":"number","value":7569,"string":"7,569"},"blob_id":{"kind":"string","value":"7ff19ee35422395f78dca1e17a736df20a40ea98"},"code":{"kind":"string","value":"import os\nimport sqlite3 as db\n\nos.system('clear')\npersons = []\n\nclass Person:\n\tdef __init__(self, name, surname, job, salary):\n\t\tself.name = name\n\t\tself.surname = surname\n\t\tself.job = job\n\t\tself.salary = salary\n\ndef create(name):\n\tconn = db.connect(name + '.db')\n\tc = conn.cursor()\n\n\tc.execute(\"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\")\n\n\tc.execute(\"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\")\n\n\tconn.commit()\n\tconn.close()\t\n\ndef database(s):\n\tconn = db.connect(sqldb+'.db')\n\tc = conn.cursor()\n\tc.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.surname))\n\tc.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (s.surname, s.job, s.salary))\n\tconn.commit()\n\tconn.close()\n\ndef insert():\n\tname = input('Enter your name: ')\n\tsurname = input('Enter your surname: ')\n\tconfirm = input('Have you got a job? ')\n\tif 'y' in confirm:\n\t\tjob = input('What kind of job you have? ')\n\t\tsalary = input('How much they pay for you? ')\n\t\tsurname = Person(name, surname, job, salary)\n\t\tpersons.append(surname)\n\t\tdatabase(surname)\n\telse:\n\t\tprint('We need a humans with job, bye')\n\n\nwhile True:\n\tcommand = input(\">> \")\n\tif command == 'insert':\n\t\tinsert()\n\telif command == 'list':\n\t\tfor i in persons:\n\t\t\tprint(i.surname)\n\t\tcontinue\n\telif command == 'create database':\n\t\tsqldb = input('Enter the name of new database: ')\n\t\tcreate(sqldb)\n\telif command == 'clear' or command == 'cls':\n\t\tloc = os.getcwd()\n\t\tif 'C:' in loc or 'D:' in loc:\n\t\t\tos.system('cls')\n\t\telse:\n\t\t\tos.system('clear')\n\telse:\n\t\tprint('No command found')\n\t\tcontinue"},"steps":{"kind":"list like","value":["import os\nimport sqlite3 as db\n\nos.system('clear')\npersons = []\n\nclass Person:\n\tdef __init__(self, name, surname, job, salary):\n\t\tself.name = name\n\t\tself.surname = surname\n\t\tself.job = job\n\t\tself.salary = salary\n\ndef create(name):\n\tconn = db.connect(name + '.db')\n\tc = conn.cursor()\n\n\tc.execute(\"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\")\n\n\tc.execute(\"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\")\n\n\tconn.commit()\n\tconn.close()\t\n\ndef database(s):\n\tconn = db.connect(sqldb+'.db')\n\tc = conn.cursor()\n\tc.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.surname))\n\tc.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (s.surname, s.job, s.salary))\n\tconn.commit()\n\tconn.close()\n\ndef insert():\n\tname = input('Enter your name: ')\n\tsurname = input('Enter your surname: ')\n\tconfirm = input('Have you got a job? ')\n\tif 'y' in confirm:\n\t\tjob = input('What kind of job you have? ')\n\t\tsalary = input('How much they pay for you? ')\n\t\tsurname = Person(name, surname, job, salary)\n\t\tpersons.append(surname)\n\t\tdatabase(surname)\n\telse:\n\t\tprint('We need a humans with job, bye')\n\n\nwhile True:\n\tcommand = input(\">> \")\n\tif command == 'insert':\n\t\tinsert()\n\telif command == 'list':\n\t\tfor i in persons:\n\t\t\tprint(i.surname)\n\t\tcontinue\n\telif command == 'create database':\n\t\tsqldb = input('Enter the name of new database: ')\n\t\tcreate(sqldb)\n\telif command == 'clear' or command == 'cls':\n\t\tloc = os.getcwd()\n\t\tif 'C:' in loc or 'D:' in loc:\n\t\t\tos.system('cls')\n\t\telse:\n\t\t\tos.system('clear')\n\telse:\n\t\tprint('No command found')\n\t\tcontinue","import os\nimport sqlite3 as db\nos.system('clear')\npersons = []\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\ndef create(name):\n conn = db.connect(name + '.db')\n c = conn.cursor()\n c.execute(\n \"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\"\n )\n c.execute(\n \"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\"\n )\n conn.commit()\n conn.close()\n\n\ndef database(s):\n conn = db.connect(sqldb + '.db')\n c = conn.cursor()\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\n surname))\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\n s.surname, s.job, s.salary))\n conn.commit()\n conn.close()\n\n\ndef insert():\n name = input('Enter your name: ')\n surname = input('Enter your surname: ')\n confirm = input('Have you got a job? ')\n if 'y' in confirm:\n job = input('What kind of job you have? ')\n salary = input('How much they pay for you? ')\n surname = Person(name, surname, job, salary)\n persons.append(surname)\n database(surname)\n else:\n print('We need a humans with job, bye')\n\n\nwhile True:\n command = input('>> ')\n if command == 'insert':\n insert()\n elif command == 'list':\n for i in persons:\n print(i.surname)\n continue\n elif command == 'create database':\n sqldb = input('Enter the name of new database: ')\n create(sqldb)\n elif command == 'clear' or command == 'cls':\n loc = os.getcwd()\n if 'C:' in loc or 'D:' in loc:\n os.system('cls')\n else:\n os.system('clear')\n else:\n print('No command found')\n continue\n","<import token>\nos.system('clear')\npersons = []\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\ndef create(name):\n conn = db.connect(name + '.db')\n c = conn.cursor()\n c.execute(\n \"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\"\n )\n c.execute(\n \"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\"\n )\n conn.commit()\n conn.close()\n\n\ndef database(s):\n conn = db.connect(sqldb + '.db')\n c = conn.cursor()\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\n surname))\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\n s.surname, s.job, s.salary))\n conn.commit()\n conn.close()\n\n\ndef insert():\n name = input('Enter your name: ')\n surname = input('Enter your surname: ')\n confirm = input('Have you got a job? ')\n if 'y' in confirm:\n job = input('What kind of job you have? ')\n salary = input('How much they pay for you? ')\n surname = Person(name, surname, job, salary)\n persons.append(surname)\n database(surname)\n else:\n print('We need a humans with job, bye')\n\n\nwhile True:\n command = input('>> ')\n if command == 'insert':\n insert()\n elif command == 'list':\n for i in persons:\n print(i.surname)\n continue\n elif command == 'create database':\n sqldb = input('Enter the name of new database: ')\n create(sqldb)\n elif command == 'clear' or command == 'cls':\n loc = os.getcwd()\n if 'C:' in loc or 'D:' in loc:\n os.system('cls')\n else:\n os.system('clear')\n else:\n print('No command found')\n continue\n","<import token>\nos.system('clear')\n<assignment token>\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\ndef create(name):\n conn = db.connect(name + '.db')\n c = conn.cursor()\n c.execute(\n \"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\"\n )\n c.execute(\n \"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\"\n )\n conn.commit()\n conn.close()\n\n\ndef database(s):\n conn = db.connect(sqldb + '.db')\n c = conn.cursor()\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\n surname))\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\n s.surname, s.job, s.salary))\n conn.commit()\n conn.close()\n\n\ndef insert():\n name = input('Enter your name: ')\n surname = input('Enter your surname: ')\n confirm = input('Have you got a job? ')\n if 'y' in confirm:\n job = input('What kind of job you have? ')\n salary = input('How much they pay for you? ')\n surname = Person(name, surname, job, salary)\n persons.append(surname)\n database(surname)\n else:\n print('We need a humans with job, bye')\n\n\nwhile True:\n command = input('>> ')\n if command == 'insert':\n insert()\n elif command == 'list':\n for i in persons:\n print(i.surname)\n continue\n elif command == 'create database':\n sqldb = input('Enter the name of new database: ')\n create(sqldb)\n elif command == 'clear' or command == 'cls':\n loc = os.getcwd()\n if 'C:' in loc or 'D:' in loc:\n os.system('cls')\n else:\n os.system('clear')\n else:\n print('No command found')\n continue\n","<import token>\n<code token>\n<assignment token>\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\ndef create(name):\n conn = db.connect(name + '.db')\n c = conn.cursor()\n c.execute(\n \"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\"\n )\n c.execute(\n \"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\"\n )\n conn.commit()\n conn.close()\n\n\ndef database(s):\n conn = db.connect(sqldb + '.db')\n c = conn.cursor()\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\n surname))\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\n s.surname, s.job, s.salary))\n conn.commit()\n conn.close()\n\n\ndef insert():\n name = input('Enter your name: ')\n surname = input('Enter your surname: ')\n confirm = input('Have you got a job? ')\n if 'y' in confirm:\n job = input('What kind of job you have? ')\n salary = input('How much they pay for you? ')\n surname = Person(name, surname, job, salary)\n persons.append(surname)\n database(surname)\n else:\n print('We need a humans with job, bye')\n\n\n<code token>\n","<import token>\n<code token>\n<assignment token>\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\ndef create(name):\n conn = db.connect(name + '.db')\n c = conn.cursor()\n c.execute(\n \"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\"\n )\n c.execute(\n \"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\"\n )\n conn.commit()\n conn.close()\n\n\ndef database(s):\n conn = db.connect(sqldb + '.db')\n c = conn.cursor()\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\n surname))\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\n s.surname, s.job, s.salary))\n conn.commit()\n conn.close()\n\n\n<function token>\n<code token>\n","<import token>\n<code token>\n<assignment token>\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\ndef create(name):\n conn = db.connect(name + '.db')\n c = conn.cursor()\n c.execute(\n \"\"\"CREATE TABLE first(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tname text,\n\t\t\tsurname text\n\t\t)\"\"\"\n )\n c.execute(\n \"\"\"CREATE TABLE second(\n\t\t\tid integer PRIMARY KEY AUTOINCREMENT,\n\t\t\tsurname text,\n\t\t\tjob text,\n\t\t\tsalary integer,\n\t\t\tFOREIGN KEY(id) REFERENCES first(id),\n\t\t\tFOREIGN KEY(surname) REFERENCES first(surname)\n\t\t)\"\"\"\n )\n conn.commit()\n conn.close()\n\n\n<function token>\n<function token>\n<code token>\n","<import token>\n<code token>\n<assignment token>\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n","<import token>\n<code token>\n<assignment token>\n\n\nclass Person:\n <function token>\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n","<import token>\n<code token>\n<assignment token>\n<class token>\n<function token>\n<function token>\n<function token>\n<code token>\n"],"string":"[\n \"import os\\nimport sqlite3 as db\\n\\nos.system('clear')\\npersons = []\\n\\nclass Person:\\n\\tdef __init__(self, name, surname, job, salary):\\n\\t\\tself.name = name\\n\\t\\tself.surname = surname\\n\\t\\tself.job = job\\n\\t\\tself.salary = salary\\n\\ndef create(name):\\n\\tconn = db.connect(name + '.db')\\n\\tc = conn.cursor()\\n\\n\\tc.execute(\\\"\\\"\\\"CREATE TABLE first(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tname text,\\n\\t\\t\\tsurname text\\n\\t\\t)\\\"\\\"\\\")\\n\\n\\tc.execute(\\\"\\\"\\\"CREATE TABLE second(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tsurname text,\\n\\t\\t\\tjob text,\\n\\t\\t\\tsalary integer,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES first(id),\\n\\t\\t\\tFOREIGN KEY(surname) REFERENCES first(surname)\\n\\t\\t)\\\"\\\"\\\")\\n\\n\\tconn.commit()\\n\\tconn.close()\\t\\n\\ndef database(s):\\n\\tconn = db.connect(sqldb+'.db')\\n\\tc = conn.cursor()\\n\\tc.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.surname))\\n\\tc.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (s.surname, s.job, s.salary))\\n\\tconn.commit()\\n\\tconn.close()\\n\\ndef insert():\\n\\tname = input('Enter your name: ')\\n\\tsurname = input('Enter your surname: ')\\n\\tconfirm = input('Have you got a job? ')\\n\\tif 'y' in confirm:\\n\\t\\tjob = input('What kind of job you have? ')\\n\\t\\tsalary = input('How much they pay for you? ')\\n\\t\\tsurname = Person(name, surname, job, salary)\\n\\t\\tpersons.append(surname)\\n\\t\\tdatabase(surname)\\n\\telse:\\n\\t\\tprint('We need a humans with job, bye')\\n\\n\\nwhile True:\\n\\tcommand = input(\\\">> \\\")\\n\\tif command == 'insert':\\n\\t\\tinsert()\\n\\telif command == 'list':\\n\\t\\tfor i in persons:\\n\\t\\t\\tprint(i.surname)\\n\\t\\tcontinue\\n\\telif command == 'create database':\\n\\t\\tsqldb = input('Enter the name of new database: ')\\n\\t\\tcreate(sqldb)\\n\\telif command == 'clear' or command == 'cls':\\n\\t\\tloc = os.getcwd()\\n\\t\\tif 'C:' in loc or 'D:' in loc:\\n\\t\\t\\tos.system('cls')\\n\\t\\telse:\\n\\t\\t\\tos.system('clear')\\n\\telse:\\n\\t\\tprint('No command found')\\n\\t\\tcontinue\",\n \"import os\\nimport sqlite3 as db\\nos.system('clear')\\npersons = []\\n\\n\\nclass Person:\\n\\n def __init__(self, name, surname, job, salary):\\n self.name = name\\n self.surname = surname\\n self.job = job\\n self.salary = salary\\n\\n\\ndef create(name):\\n conn = db.connect(name + '.db')\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE first(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tname text,\\n\\t\\t\\tsurname text\\n\\t\\t)\\\"\\\"\\\"\\n )\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE second(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tsurname text,\\n\\t\\t\\tjob text,\\n\\t\\t\\tsalary integer,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES first(id),\\n\\t\\t\\tFOREIGN KEY(surname) REFERENCES first(surname)\\n\\t\\t)\\\"\\\"\\\"\\n )\\n conn.commit()\\n conn.close()\\n\\n\\ndef database(s):\\n conn = db.connect(sqldb + '.db')\\n c = conn.cursor()\\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\\n surname))\\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\\n s.surname, s.job, s.salary))\\n conn.commit()\\n conn.close()\\n\\n\\ndef insert():\\n name = input('Enter your name: ')\\n surname = input('Enter your surname: ')\\n confirm = input('Have you got a job? ')\\n if 'y' in confirm:\\n job = input('What kind of job you have? ')\\n salary = input('How much they pay for you? ')\\n surname = Person(name, surname, job, salary)\\n persons.append(surname)\\n database(surname)\\n else:\\n print('We need a humans with job, bye')\\n\\n\\nwhile True:\\n command = input('>> ')\\n if command == 'insert':\\n insert()\\n elif command == 'list':\\n for i in persons:\\n print(i.surname)\\n continue\\n elif command == 'create database':\\n sqldb = input('Enter the name of new database: ')\\n create(sqldb)\\n elif command == 'clear' or command == 'cls':\\n loc = os.getcwd()\\n if 'C:' in loc or 'D:' in loc:\\n os.system('cls')\\n else:\\n os.system('clear')\\n else:\\n print('No command found')\\n continue\\n\",\n \"<import token>\\nos.system('clear')\\npersons = []\\n\\n\\nclass Person:\\n\\n def __init__(self, name, surname, job, salary):\\n self.name = name\\n self.surname = surname\\n self.job = job\\n self.salary = salary\\n\\n\\ndef create(name):\\n conn = db.connect(name + '.db')\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE first(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tname text,\\n\\t\\t\\tsurname text\\n\\t\\t)\\\"\\\"\\\"\\n )\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE second(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tsurname text,\\n\\t\\t\\tjob text,\\n\\t\\t\\tsalary integer,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES first(id),\\n\\t\\t\\tFOREIGN KEY(surname) REFERENCES first(surname)\\n\\t\\t)\\\"\\\"\\\"\\n )\\n conn.commit()\\n conn.close()\\n\\n\\ndef database(s):\\n conn = db.connect(sqldb + '.db')\\n c = conn.cursor()\\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\\n surname))\\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\\n s.surname, s.job, s.salary))\\n conn.commit()\\n conn.close()\\n\\n\\ndef insert():\\n name = input('Enter your name: ')\\n surname = input('Enter your surname: ')\\n confirm = input('Have you got a job? ')\\n if 'y' in confirm:\\n job = input('What kind of job you have? ')\\n salary = input('How much they pay for you? ')\\n surname = Person(name, surname, job, salary)\\n persons.append(surname)\\n database(surname)\\n else:\\n print('We need a humans with job, bye')\\n\\n\\nwhile True:\\n command = input('>> ')\\n if command == 'insert':\\n insert()\\n elif command == 'list':\\n for i in persons:\\n print(i.surname)\\n continue\\n elif command == 'create database':\\n sqldb = input('Enter the name of new database: ')\\n create(sqldb)\\n elif command == 'clear' or command == 'cls':\\n loc = os.getcwd()\\n if 'C:' in loc or 'D:' in loc:\\n os.system('cls')\\n else:\\n os.system('clear')\\n else:\\n print('No command found')\\n continue\\n\",\n \"<import token>\\nos.system('clear')\\n<assignment token>\\n\\n\\nclass Person:\\n\\n def __init__(self, name, surname, job, salary):\\n self.name = name\\n self.surname = surname\\n self.job = job\\n self.salary = salary\\n\\n\\ndef create(name):\\n conn = db.connect(name + '.db')\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE first(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tname text,\\n\\t\\t\\tsurname text\\n\\t\\t)\\\"\\\"\\\"\\n )\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE second(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tsurname text,\\n\\t\\t\\tjob text,\\n\\t\\t\\tsalary integer,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES first(id),\\n\\t\\t\\tFOREIGN KEY(surname) REFERENCES first(surname)\\n\\t\\t)\\\"\\\"\\\"\\n )\\n conn.commit()\\n conn.close()\\n\\n\\ndef database(s):\\n conn = db.connect(sqldb + '.db')\\n c = conn.cursor()\\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\\n surname))\\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\\n s.surname, s.job, s.salary))\\n conn.commit()\\n conn.close()\\n\\n\\ndef insert():\\n name = input('Enter your name: ')\\n surname = input('Enter your surname: ')\\n confirm = input('Have you got a job? ')\\n if 'y' in confirm:\\n job = input('What kind of job you have? ')\\n salary = input('How much they pay for you? ')\\n surname = Person(name, surname, job, salary)\\n persons.append(surname)\\n database(surname)\\n else:\\n print('We need a humans with job, bye')\\n\\n\\nwhile True:\\n command = input('>> ')\\n if command == 'insert':\\n insert()\\n elif command == 'list':\\n for i in persons:\\n print(i.surname)\\n continue\\n elif command == 'create database':\\n sqldb = input('Enter the name of new database: ')\\n create(sqldb)\\n elif command == 'clear' or command == 'cls':\\n loc = os.getcwd()\\n if 'C:' in loc or 'D:' in loc:\\n os.system('cls')\\n else:\\n os.system('clear')\\n else:\\n print('No command found')\\n continue\\n\",\n \"<import token>\\n<code token>\\n<assignment token>\\n\\n\\nclass Person:\\n\\n def __init__(self, name, surname, job, salary):\\n self.name = name\\n self.surname = surname\\n self.job = job\\n self.salary = salary\\n\\n\\ndef create(name):\\n conn = db.connect(name + '.db')\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE first(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tname text,\\n\\t\\t\\tsurname text\\n\\t\\t)\\\"\\\"\\\"\\n )\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE second(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tsurname text,\\n\\t\\t\\tjob text,\\n\\t\\t\\tsalary integer,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES first(id),\\n\\t\\t\\tFOREIGN KEY(surname) REFERENCES first(surname)\\n\\t\\t)\\\"\\\"\\\"\\n )\\n conn.commit()\\n conn.close()\\n\\n\\ndef database(s):\\n conn = db.connect(sqldb + '.db')\\n c = conn.cursor()\\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\\n surname))\\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\\n s.surname, s.job, s.salary))\\n conn.commit()\\n conn.close()\\n\\n\\ndef insert():\\n name = input('Enter your name: ')\\n surname = input('Enter your surname: ')\\n confirm = input('Have you got a job? ')\\n if 'y' in confirm:\\n job = input('What kind of job you have? ')\\n salary = input('How much they pay for you? ')\\n surname = Person(name, surname, job, salary)\\n persons.append(surname)\\n database(surname)\\n else:\\n print('We need a humans with job, bye')\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<assignment token>\\n\\n\\nclass Person:\\n\\n def __init__(self, name, surname, job, salary):\\n self.name = name\\n self.surname = surname\\n self.job = job\\n self.salary = salary\\n\\n\\ndef create(name):\\n conn = db.connect(name + '.db')\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE first(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tname text,\\n\\t\\t\\tsurname text\\n\\t\\t)\\\"\\\"\\\"\\n )\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE second(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tsurname text,\\n\\t\\t\\tjob text,\\n\\t\\t\\tsalary integer,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES first(id),\\n\\t\\t\\tFOREIGN KEY(surname) REFERENCES first(surname)\\n\\t\\t)\\\"\\\"\\\"\\n )\\n conn.commit()\\n conn.close()\\n\\n\\ndef database(s):\\n conn = db.connect(sqldb + '.db')\\n c = conn.cursor()\\n c.execute('INSERT INTO first(name, surname) VALUES(?, ?)', (s.name, s.\\n surname))\\n c.execute('INSERT INTO second(surname, job, salary) VALUES(?, ?, ?)', (\\n s.surname, s.job, s.salary))\\n conn.commit()\\n conn.close()\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<assignment token>\\n\\n\\nclass Person:\\n\\n def __init__(self, name, surname, job, salary):\\n self.name = name\\n self.surname = surname\\n self.job = job\\n self.salary = salary\\n\\n\\ndef create(name):\\n conn = db.connect(name + '.db')\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE first(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tname text,\\n\\t\\t\\tsurname text\\n\\t\\t)\\\"\\\"\\\"\\n )\\n c.execute(\\n \\\"\\\"\\\"CREATE TABLE second(\\n\\t\\t\\tid integer PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tsurname text,\\n\\t\\t\\tjob text,\\n\\t\\t\\tsalary integer,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES first(id),\\n\\t\\t\\tFOREIGN KEY(surname) REFERENCES first(surname)\\n\\t\\t)\\\"\\\"\\\"\\n )\\n conn.commit()\\n conn.close()\\n\\n\\n<function token>\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<assignment token>\\n\\n\\nclass Person:\\n\\n def __init__(self, name, surname, job, salary):\\n self.name = name\\n self.surname = surname\\n self.job = job\\n self.salary = salary\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<assignment token>\\n\\n\\nclass Person:\\n <function token>\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<assignment token>\\n<class token>\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7570,"cells":{"index":{"kind":"number","value":7570,"string":"7,570"},"blob_id":{"kind":"string","value":"0dec0f04cfe891eea74ef45484fa7433e3429dcd"},"code":{"kind":"string","value":"import os\nimport glob\n\nONE_KB = 1024\n\n\ndef get_files(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n return (\n filename\n for _, _, files in os.walk(dirname)\n for filename in files\n if int(filename) >= size_in_kb * ONE_KB\n )\n\n\n# Pybites solution\ndef get_files1(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n for file in glob.glob(os.path.join(dirname, \"*\")):\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\n yield file\n"},"steps":{"kind":"list like","value":["import os\nimport glob\n\nONE_KB = 1024\n\n\ndef get_files(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n return (\n filename\n for _, _, files in os.walk(dirname)\n for filename in files\n if int(filename) >= size_in_kb * ONE_KB\n )\n\n\n# Pybites solution\ndef get_files1(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n for file in glob.glob(os.path.join(dirname, \"*\")):\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\n yield file\n","import os\nimport glob\nONE_KB = 1024\n\n\ndef get_files(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n return (filename for _, _, files in os.walk(dirname) for filename in\n files if int(filename) >= size_in_kb * ONE_KB)\n\n\ndef get_files1(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n for file in glob.glob(os.path.join(dirname, '*')):\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\n yield file\n","<import token>\nONE_KB = 1024\n\n\ndef get_files(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n return (filename for _, _, files in os.walk(dirname) for filename in\n files if int(filename) >= size_in_kb * ONE_KB)\n\n\ndef get_files1(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n for file in glob.glob(os.path.join(dirname, '*')):\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\n yield file\n","<import token>\n<assignment token>\n\n\ndef get_files(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n return (filename for _, _, files in os.walk(dirname) for filename in\n files if int(filename) >= size_in_kb * ONE_KB)\n\n\ndef get_files1(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n for file in glob.glob(os.path.join(dirname, '*')):\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\n yield file\n","<import token>\n<assignment token>\n<function token>\n\n\ndef get_files1(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n for file in glob.glob(os.path.join(dirname, '*')):\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\n yield file\n","<import token>\n<assignment token>\n<function token>\n<function token>\n"],"string":"[\n \"import os\\nimport glob\\n\\nONE_KB = 1024\\n\\n\\ndef get_files(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n return (\\n filename\\n for _, _, files in os.walk(dirname)\\n for filename in files\\n if int(filename) >= size_in_kb * ONE_KB\\n )\\n\\n\\n# Pybites solution\\ndef get_files1(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n for file in glob.glob(os.path.join(dirname, \\\"*\\\")):\\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\\n yield file\\n\",\n \"import os\\nimport glob\\nONE_KB = 1024\\n\\n\\ndef get_files(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n return (filename for _, _, files in os.walk(dirname) for filename in\\n files if int(filename) >= size_in_kb * ONE_KB)\\n\\n\\ndef get_files1(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n for file in glob.glob(os.path.join(dirname, '*')):\\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\\n yield file\\n\",\n \"<import token>\\nONE_KB = 1024\\n\\n\\ndef get_files(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n return (filename for _, _, files in os.walk(dirname) for filename in\\n files if int(filename) >= size_in_kb * ONE_KB)\\n\\n\\ndef get_files1(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n for file in glob.glob(os.path.join(dirname, '*')):\\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\\n yield file\\n\",\n \"<import token>\\n<assignment token>\\n\\n\\ndef get_files(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n return (filename for _, _, files in os.walk(dirname) for filename in\\n files if int(filename) >= size_in_kb * ONE_KB)\\n\\n\\ndef get_files1(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n for file in glob.glob(os.path.join(dirname, '*')):\\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\\n yield file\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\ndef get_files1(dirname, size_in_kb):\\n \\\"\\\"\\\"Return files in dirname that are >= size_in_kb\\\"\\\"\\\"\\n for file in glob.glob(os.path.join(dirname, '*')):\\n if os.stat(file).st_size >= size_in_kb * ONE_KB:\\n yield file\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7571,"cells":{"index":{"kind":"number","value":7571,"string":"7,571"},"blob_id":{"kind":"string","value":"d567dfe29380a34534308446a9c8940cede84083"},"code":{"kind":"string","value":"def func_sum_even(n):\n e_digit1=n%10\n n//=10\n e_digit2=n%10\n e_digit3=n//10\n sum_even=e_digit1*(1-e_digit1%2)+e_digit2*(1-e_digit2%2)+e_digit3*(1-e_digit3%2)\n return sum_even\n# n=int(input())\n# print(func_sum_even(n)) "},"steps":{"kind":"list like","value":["def func_sum_even(n):\n e_digit1=n%10\n n//=10\n e_digit2=n%10\n e_digit3=n//10\n sum_even=e_digit1*(1-e_digit1%2)+e_digit2*(1-e_digit2%2)+e_digit3*(1-e_digit3%2)\n return sum_even\n# n=int(input())\n# print(func_sum_even(n)) ","def func_sum_even(n):\n e_digit1 = n % 10\n n //= 10\n e_digit2 = n % 10\n e_digit3 = n // 10\n sum_even = e_digit1 * (1 - e_digit1 % 2) + e_digit2 * (1 - e_digit2 % 2\n ) + e_digit3 * (1 - e_digit3 % 2)\n return sum_even\n","<function token>\n"],"string":"[\n \"def func_sum_even(n):\\n e_digit1=n%10\\n n//=10\\n e_digit2=n%10\\n e_digit3=n//10\\n sum_even=e_digit1*(1-e_digit1%2)+e_digit2*(1-e_digit2%2)+e_digit3*(1-e_digit3%2)\\n return sum_even\\n# n=int(input())\\n# print(func_sum_even(n)) \",\n \"def func_sum_even(n):\\n e_digit1 = n % 10\\n n //= 10\\n e_digit2 = n % 10\\n e_digit3 = n // 10\\n sum_even = e_digit1 * (1 - e_digit1 % 2) + e_digit2 * (1 - e_digit2 % 2\\n ) + e_digit3 * (1 - e_digit3 % 2)\\n return sum_even\\n\",\n \"<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7572,"cells":{"index":{"kind":"number","value":7572,"string":"7,572"},"blob_id":{"kind":"string","value":"e6b3def6ed6f2523d88912832a876caf2742b786"},"code":{"kind":"string","value":"import argparse\nimport pickle\n\nimport pandas as pd\nfrom pyspark.sql.session import SparkSession\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--rs', type=str, nargs='+')\nargs = parser.parse_args()\n\nss = SparkSession.builder.getOrCreate()\npost_df = None\nfor f in args.rs:\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\n post_df = df if post_df is None else post_df.union(df)\nsubreddit_ids = pickle.load(open('subreddit_ids', 'rb'))\nret = post_df.filter(post_df.subreddit_id.isin(*subreddit_ids)).coalesce(1)\nret.write.orc('RS.orc', mode='overwrite')\nret.write.json('RS.json', mode='overwrite')\n"},"steps":{"kind":"list like","value":["import argparse\nimport pickle\n\nimport pandas as pd\nfrom pyspark.sql.session import SparkSession\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--rs', type=str, nargs='+')\nargs = parser.parse_args()\n\nss = SparkSession.builder.getOrCreate()\npost_df = None\nfor f in args.rs:\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\n post_df = df if post_df is None else post_df.union(df)\nsubreddit_ids = pickle.load(open('subreddit_ids', 'rb'))\nret = post_df.filter(post_df.subreddit_id.isin(*subreddit_ids)).coalesce(1)\nret.write.orc('RS.orc', mode='overwrite')\nret.write.json('RS.json', mode='overwrite')\n","import argparse\nimport pickle\nimport pandas as pd\nfrom pyspark.sql.session import SparkSession\nparser = argparse.ArgumentParser()\nparser.add_argument('--rs', type=str, nargs='+')\nargs = parser.parse_args()\nss = SparkSession.builder.getOrCreate()\npost_df = None\nfor f in args.rs:\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\n post_df = df if post_df is None else post_df.union(df)\nsubreddit_ids = pickle.load(open('subreddit_ids', 'rb'))\nret = post_df.filter(post_df.subreddit_id.isin(*subreddit_ids)).coalesce(1)\nret.write.orc('RS.orc', mode='overwrite')\nret.write.json('RS.json', mode='overwrite')\n","<import token>\nparser = argparse.ArgumentParser()\nparser.add_argument('--rs', type=str, nargs='+')\nargs = parser.parse_args()\nss = SparkSession.builder.getOrCreate()\npost_df = None\nfor f in args.rs:\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\n post_df = df if post_df is None else post_df.union(df)\nsubreddit_ids = pickle.load(open('subreddit_ids', 'rb'))\nret = post_df.filter(post_df.subreddit_id.isin(*subreddit_ids)).coalesce(1)\nret.write.orc('RS.orc', mode='overwrite')\nret.write.json('RS.json', mode='overwrite')\n","<import token>\n<assignment token>\nparser.add_argument('--rs', type=str, nargs='+')\n<assignment token>\nfor f in args.rs:\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\n post_df = df if post_df is None else post_df.union(df)\n<assignment token>\nret.write.orc('RS.orc', mode='overwrite')\nret.write.json('RS.json', mode='overwrite')\n","<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"],"string":"[\n \"import argparse\\nimport pickle\\n\\nimport pandas as pd\\nfrom pyspark.sql.session import SparkSession\\n\\nparser = argparse.ArgumentParser()\\nparser.add_argument('--rs', type=str, nargs='+')\\nargs = parser.parse_args()\\n\\nss = SparkSession.builder.getOrCreate()\\npost_df = None\\nfor f in args.rs:\\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\\n post_df = df if post_df is None else post_df.union(df)\\nsubreddit_ids = pickle.load(open('subreddit_ids', 'rb'))\\nret = post_df.filter(post_df.subreddit_id.isin(*subreddit_ids)).coalesce(1)\\nret.write.orc('RS.orc', mode='overwrite')\\nret.write.json('RS.json', mode='overwrite')\\n\",\n \"import argparse\\nimport pickle\\nimport pandas as pd\\nfrom pyspark.sql.session import SparkSession\\nparser = argparse.ArgumentParser()\\nparser.add_argument('--rs', type=str, nargs='+')\\nargs = parser.parse_args()\\nss = SparkSession.builder.getOrCreate()\\npost_df = None\\nfor f in args.rs:\\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\\n post_df = df if post_df is None else post_df.union(df)\\nsubreddit_ids = pickle.load(open('subreddit_ids', 'rb'))\\nret = post_df.filter(post_df.subreddit_id.isin(*subreddit_ids)).coalesce(1)\\nret.write.orc('RS.orc', mode='overwrite')\\nret.write.json('RS.json', mode='overwrite')\\n\",\n \"<import token>\\nparser = argparse.ArgumentParser()\\nparser.add_argument('--rs', type=str, nargs='+')\\nargs = parser.parse_args()\\nss = SparkSession.builder.getOrCreate()\\npost_df = None\\nfor f in args.rs:\\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\\n post_df = df if post_df is None else post_df.union(df)\\nsubreddit_ids = pickle.load(open('subreddit_ids', 'rb'))\\nret = post_df.filter(post_df.subreddit_id.isin(*subreddit_ids)).coalesce(1)\\nret.write.orc('RS.orc', mode='overwrite')\\nret.write.json('RS.json', mode='overwrite')\\n\",\n \"<import token>\\n<assignment token>\\nparser.add_argument('--rs', type=str, nargs='+')\\n<assignment token>\\nfor f in args.rs:\\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\\n post_df = df if post_df is None else post_df.union(df)\\n<assignment token>\\nret.write.orc('RS.orc', mode='overwrite')\\nret.write.json('RS.json', mode='overwrite')\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7573,"cells":{"index":{"kind":"number","value":7573,"string":"7,573"},"blob_id":{"kind":"string","value":"422491852b80c2fc4a2e73c01fd01acaad4cf9c8"},"code":{"kind":"string","value":"#Testing Operating System Descriptions\n\n#OS : LMDE 4 Debbie\n#Version: 4.6.7\n#Kernal Version : 4.19.0-8-amd64\n\n#Scripting Langages : Python3\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Libraries used\n\n#pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\nimport pandas as pd\n\n#NumPy is a Python library used for working with arrays.\n#NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.Arrays are very frequently used in data science, where speed and resources are very important.\nimport numpy as np\n\n#Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python\n#matplotlib.pyplot is a state-based interface to matplotlib. It provides a MATLAB-like way of plotting.\n#pyplot is mainly intended for interactive plots and simple cases of programmatic plot generation\nimport matplotlib.pyplot as plt\n\n#Simple and efficient tools for predictive data analysis. Built on NumPy, SciPy, and matplotlib. Scikit-learn is probably the most useful library for machine learning in Python. \n#The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics\n\n#-----------------------------------------------------------------------------------------------------------------------------\n#Reading the dataset\ndataset = pd.read_csv('./dataset/datafile.csv')\n\n#----------------------------------------------------------------------------------------------------------------------------\n#Extracting X and Y values\n#Preparing the Data\n#divide the data into \"attributes\" and \"labels\". Attributes are the independent variables while labels are dependent variables whose values are to be predicted\n\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 1].values\n\n#------------------------------------------------------------------------------------------------------------------------------\n#converting to arrays of data\nX = np.array(X).reshape(-1, 1) \ny = np.array(X).reshape(-1, 1)\n\n#-----------------------------------------------------------------------------------------------------------------------------\n#we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split()\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) \n\n#The above script splits 80% of the data to training set while 20% of the data to test set. The test_size variable is where we actually specify the proportion of test set.\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n#Training the Algorithm\n\nregr = LinearRegression() \nregr.fit(X_train, y_train) \nprint(regr.score(X_test, y_test)) \n\n#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Making Predictions\n\ny_pred = regr.predict(X_test) \n\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Evaluating the Algorithm\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Plotting the scatter\nplt.scatter(X_test, y_test, color ='b') \nplt.plot(X_test, y_pred, color ='k') \nplt.show()\n\n"},"steps":{"kind":"list like","value":["#Testing Operating System Descriptions\n\n#OS : LMDE 4 Debbie\n#Version: 4.6.7\n#Kernal Version : 4.19.0-8-amd64\n\n#Scripting Langages : Python3\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Libraries used\n\n#pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\nimport pandas as pd\n\n#NumPy is a Python library used for working with arrays.\n#NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.Arrays are very frequently used in data science, where speed and resources are very important.\nimport numpy as np\n\n#Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python\n#matplotlib.pyplot is a state-based interface to matplotlib. It provides a MATLAB-like way of plotting.\n#pyplot is mainly intended for interactive plots and simple cases of programmatic plot generation\nimport matplotlib.pyplot as plt\n\n#Simple and efficient tools for predictive data analysis. Built on NumPy, SciPy, and matplotlib. Scikit-learn is probably the most useful library for machine learning in Python. \n#The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics\n\n#-----------------------------------------------------------------------------------------------------------------------------\n#Reading the dataset\ndataset = pd.read_csv('./dataset/datafile.csv')\n\n#----------------------------------------------------------------------------------------------------------------------------\n#Extracting X and Y values\n#Preparing the Data\n#divide the data into \"attributes\" and \"labels\". Attributes are the independent variables while labels are dependent variables whose values are to be predicted\n\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 1].values\n\n#------------------------------------------------------------------------------------------------------------------------------\n#converting to arrays of data\nX = np.array(X).reshape(-1, 1) \ny = np.array(X).reshape(-1, 1)\n\n#-----------------------------------------------------------------------------------------------------------------------------\n#we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split()\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) \n\n#The above script splits 80% of the data to training set while 20% of the data to test set. The test_size variable is where we actually specify the proportion of test set.\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n#Training the Algorithm\n\nregr = LinearRegression() \nregr.fit(X_train, y_train) \nprint(regr.score(X_test, y_test)) \n\n#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Making Predictions\n\ny_pred = regr.predict(X_test) \n\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Evaluating the Algorithm\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Plotting the scatter\nplt.scatter(X_test, y_test, color ='b') \nplt.plot(X_test, y_pred, color ='k') \nplt.show()\n\n","import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics\ndataset = pd.read_csv('./dataset/datafile.csv')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 1].values\nX = np.array(X).reshape(-1, 1)\ny = np.array(X).reshape(-1, 1)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\nregr = LinearRegression()\nregr.fit(X_train, y_train)\nprint(regr.score(X_test, y_test))\ny_pred = regr.predict(X_test)\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test,\n y_pred)))\nplt.scatter(X_test, y_test, color='b')\nplt.plot(X_test, y_pred, color='k')\nplt.show()\n","<import token>\ndataset = pd.read_csv('./dataset/datafile.csv')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 1].values\nX = np.array(X).reshape(-1, 1)\ny = np.array(X).reshape(-1, 1)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\nregr = LinearRegression()\nregr.fit(X_train, y_train)\nprint(regr.score(X_test, y_test))\ny_pred = regr.predict(X_test)\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test,\n y_pred)))\nplt.scatter(X_test, y_test, color='b')\nplt.plot(X_test, y_pred, color='k')\nplt.show()\n","<import token>\n<assignment token>\nregr.fit(X_train, y_train)\nprint(regr.score(X_test, y_test))\n<assignment token>\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test,\n y_pred)))\nplt.scatter(X_test, y_test, color='b')\nplt.plot(X_test, y_pred, color='k')\nplt.show()\n","<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"],"string":"[\n \"#Testing Operating System Descriptions\\n\\n#OS : LMDE 4 Debbie\\n#Version: 4.6.7\\n#Kernal Version : 4.19.0-8-amd64\\n\\n#Scripting Langages : Python3\\n\\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\\n#Libraries used\\n\\n#pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\\nimport pandas as pd\\n\\n#NumPy is a Python library used for working with arrays.\\n#NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.Arrays are very frequently used in data science, where speed and resources are very important.\\nimport numpy as np\\n\\n#Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python\\n#matplotlib.pyplot is a state-based interface to matplotlib. It provides a MATLAB-like way of plotting.\\n#pyplot is mainly intended for interactive plots and simple cases of programmatic plot generation\\nimport matplotlib.pyplot as plt\\n\\n#Simple and efficient tools for predictive data analysis. Built on NumPy, SciPy, and matplotlib. Scikit-learn is probably the most useful library for machine learning in Python. \\n#The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LinearRegression\\nfrom sklearn import metrics\\n\\n#-----------------------------------------------------------------------------------------------------------------------------\\n#Reading the dataset\\ndataset = pd.read_csv('./dataset/datafile.csv')\\n\\n#----------------------------------------------------------------------------------------------------------------------------\\n#Extracting X and Y values\\n#Preparing the Data\\n#divide the data into \\\"attributes\\\" and \\\"labels\\\". Attributes are the independent variables while labels are dependent variables whose values are to be predicted\\n\\nX = dataset.iloc[:, :-1].values\\ny = dataset.iloc[:, 1].values\\n\\n#------------------------------------------------------------------------------------------------------------------------------\\n#converting to arrays of data\\nX = np.array(X).reshape(-1, 1) \\ny = np.array(X).reshape(-1, 1)\\n\\n#-----------------------------------------------------------------------------------------------------------------------------\\n#we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split()\\n\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) \\n\\n#The above script splits 80% of the data to training set while 20% of the data to test set. The test_size variable is where we actually specify the proportion of test set.\\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\\n\\n#Training the Algorithm\\n\\nregr = LinearRegression() \\nregr.fit(X_train, y_train) \\nprint(regr.score(X_test, y_test)) \\n\\n#---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\\n#Making Predictions\\n\\ny_pred = regr.predict(X_test) \\n\\n#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\\n#Evaluating the Algorithm\\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\\n\\n#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\\n#Plotting the scatter\\nplt.scatter(X_test, y_test, color ='b') \\nplt.plot(X_test, y_pred, color ='k') \\nplt.show()\\n\\n\",\n \"import pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LinearRegression\\nfrom sklearn import metrics\\ndataset = pd.read_csv('./dataset/datafile.csv')\\nX = dataset.iloc[:, :-1].values\\ny = dataset.iloc[:, 1].values\\nX = np.array(X).reshape(-1, 1)\\ny = np.array(X).reshape(-1, 1)\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\\nregr = LinearRegression()\\nregr.fit(X_train, y_train)\\nprint(regr.score(X_test, y_test))\\ny_pred = regr.predict(X_test)\\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test,\\n y_pred)))\\nplt.scatter(X_test, y_test, color='b')\\nplt.plot(X_test, y_pred, color='k')\\nplt.show()\\n\",\n \"<import token>\\ndataset = pd.read_csv('./dataset/datafile.csv')\\nX = dataset.iloc[:, :-1].values\\ny = dataset.iloc[:, 1].values\\nX = np.array(X).reshape(-1, 1)\\ny = np.array(X).reshape(-1, 1)\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\\nregr = LinearRegression()\\nregr.fit(X_train, y_train)\\nprint(regr.score(X_test, y_test))\\ny_pred = regr.predict(X_test)\\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test,\\n y_pred)))\\nplt.scatter(X_test, y_test, color='b')\\nplt.plot(X_test, y_pred, color='k')\\nplt.show()\\n\",\n \"<import token>\\n<assignment token>\\nregr.fit(X_train, y_train)\\nprint(regr.score(X_test, y_test))\\n<assignment token>\\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\\nprint('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test,\\n y_pred)))\\nplt.scatter(X_test, y_test, color='b')\\nplt.plot(X_test, y_pred, color='k')\\nplt.show()\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7574,"cells":{"index":{"kind":"number","value":7574,"string":"7,574"},"blob_id":{"kind":"string","value":"e8daf03f987c7512ff245bfbe16c447acd6b5986"},"code":{"kind":"string","value":"import API.enum as enum\nimport re\n\nclass ObjectValidator():\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({\n 'data': data,\n 'validator': validatorFunction\n })\n\n def _check_with_authenticationValidator(self, data):\n if not data['user'].is_authenticated:\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter': filter},\n self._check_with_nonDuplicateObjectValidator)\n return self\n\n def checkObjectExistence(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter': filter},\n self._check_with_ObjectExistenceValidator)\n return self\n\n def checkUserAuthentication(self, field, user):\n self.addValidation({'field': field, 'user': user},\n self._check_with_authenticationValidator)\n return self\n\n\n#\\b(?!(\\d)\\1{3})[13-9]{4}[1346-9][013-9]{5}\\b\n# postal code validation\n\n\nclass FieldValidator():\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.INVALID_FIELD_DATA.value)\n return self\n def addValidation(self, data, validatorFunction):\n if (data['value'] == 'unAssigned') and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({\n 'data': data,\n 'validator': validatorFunction\n })\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in [\"پلتفرم پرداخت در محل\", \"باشگاه مشتریان\", \"درگاه پرداخت اینترنتی\"]:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match(r\"^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$\", data[\"value\"])\n if 'admin' in data['value'] or 'zibal' in data['value'] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(r\"(^09[0-9]{9}$)\", mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(r\"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$)\", email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == \"\":\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban)!=26 or not iban.startswith(\"IR\"):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:]+iban[:4]\n code = code.replace('I','18').replace('R','27')\n if int(code)%97!=1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID=data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7]=='062' and data['value'][-13:-10]=='080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value)\n\n def _check_with_username(self, data):\n username = re.match(r\"^[a-zA-Z0-9_.-]+$\", data[\"value\"])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n #############################################################################\n\n def checkType(self, field, type, value=\"unAssigned\"):\n self.addValidation({'field': field, 'type': type, 'value': value}, self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features=\"unAssigned\"):\n self.addValidation({'field': field, 'value': features}, self._check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username=\"unAssigned\"):\n self.addValidation({'field': field, 'value': username}, self._check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options': options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {\n 'userId': userId,\n 'subId': subId\n }\n self.addValidation({'field': field, 'value': data}, self._check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length,mode='equal', data=\"unAssigned\"):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n\n self.addValidation({'field': field, 'value': data, 'length': length}, validatorFunction)\n\n return self\n\n def checkInputData(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data=\"unAssigned\"): ##TODO\n self.addValidation({'field': field, 'value': data}, self._check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []) + self.objectValidator.getErrors()[key]\n self.statusCode = self.objectValidator.statusCode if self.objectValidator.statusCode != 200 else self.statusCode\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n\n if \"name\" in errorKeys :\n messages.append('نام را وارد کنید')\n\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n\n if 'phone' in errorKeys:\n messages.append('تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append('شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله')\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n\n return messages"},"steps":{"kind":"list like","value":["import API.enum as enum\nimport re\n\nclass ObjectValidator():\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({\n 'data': data,\n 'validator': validatorFunction\n })\n\n def _check_with_authenticationValidator(self, data):\n if not data['user'].is_authenticated:\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter': filter},\n self._check_with_nonDuplicateObjectValidator)\n return self\n\n def checkObjectExistence(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter': filter},\n self._check_with_ObjectExistenceValidator)\n return self\n\n def checkUserAuthentication(self, field, user):\n self.addValidation({'field': field, 'user': user},\n self._check_with_authenticationValidator)\n return self\n\n\n#\\b(?!(\\d)\\1{3})[13-9]{4}[1346-9][013-9]{5}\\b\n# postal code validation\n\n\nclass FieldValidator():\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.INVALID_FIELD_DATA.value)\n return self\n def addValidation(self, data, validatorFunction):\n if (data['value'] == 'unAssigned') and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({\n 'data': data,\n 'validator': validatorFunction\n })\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in [\"پلتفرم پرداخت در محل\", \"باشگاه مشتریان\", \"درگاه پرداخت اینترنتی\"]:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match(r\"^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$\", data[\"value\"])\n if 'admin' in data['value'] or 'zibal' in data['value'] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(r\"(^09[0-9]{9}$)\", mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(r\"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$)\", email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == \"\":\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban)!=26 or not iban.startswith(\"IR\"):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:]+iban[:4]\n code = code.replace('I','18').replace('R','27')\n if int(code)%97!=1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID=data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7]=='062' and data['value'][-13:-10]=='080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value)\n\n def _check_with_username(self, data):\n username = re.match(r\"^[a-zA-Z0-9_.-]+$\", data[\"value\"])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n #############################################################################\n\n def checkType(self, field, type, value=\"unAssigned\"):\n self.addValidation({'field': field, 'type': type, 'value': value}, self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code=\"unAssigned\"):\n self.addValidation({'field': field, 'value': code}, self._check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features=\"unAssigned\"):\n self.addValidation({'field': field, 'value': features}, self._check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username=\"unAssigned\"):\n self.addValidation({'field': field, 'value': username}, self._check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options': options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {\n 'userId': userId,\n 'subId': subId\n }\n self.addValidation({'field': field, 'value': data}, self._check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length,mode='equal', data=\"unAssigned\"):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n\n self.addValidation({'field': field, 'value': data, 'length': length}, validatorFunction)\n\n return self\n\n def checkInputData(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data=\"unAssigned\"): ##TODO\n self.addValidation({'field': field, 'value': data}, self._check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data=\"unAssigned\"):\n self.addValidation({'field': field, 'value': data}, self._check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []) + self.objectValidator.getErrors()[key]\n self.statusCode = self.objectValidator.statusCode if self.objectValidator.statusCode != 200 else self.statusCode\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n\n if \"name\" in errorKeys :\n messages.append('نام را وارد کنید')\n\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n\n if 'phone' in errorKeys:\n messages.append('تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append('شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله')\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n\n return messages","import API.enum as enum\nimport re\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_authenticationValidator(self, data):\n if not data['user'].is_authenticated:\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n\n def checkObjectExistence(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_ObjectExistenceValidator)\n return self\n\n def checkUserAuthentication(self, field, user):\n self.addValidation({'field': field, 'user': user}, self.\n _check_with_authenticationValidator)\n return self\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_authenticationValidator(self, data):\n if not data['user'].is_authenticated:\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n\n def checkObjectExistence(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_ObjectExistenceValidator)\n return self\n\n def checkUserAuthentication(self, field, user):\n self.addValidation({'field': field, 'user': user}, self.\n _check_with_authenticationValidator)\n return self\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_authenticationValidator(self, data):\n if not data['user'].is_authenticated:\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n\n def checkObjectExistence(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_ObjectExistenceValidator)\n return self\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n\n def checkObjectExistence(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_ObjectExistenceValidator)\n return self\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n\n def checkObjectExistence(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_ObjectExistenceValidator)\n return self\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = ObjectValidator()\n return self\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n <function token>\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n <function token>\n <function token>\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n\n def _check_with_ObjectExistenceValidator(self, data):\n model = data['model']\n if not model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.\n GENERIC_OBJECT_NOT_FOUND.value)\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n <function token>\n <function token>\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n\n def _check_with_nonDuplicateObjectValidator(self, data):\n model = data['model']\n if model.objects.filter(**data['filter']):\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\n <function token>\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n <function token>\n <function token>\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n <function token>\n <function token>\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n <function token>\n <function token>\n\n def checkNonDuplicateObject(self, field, model, **filter):\n self.addValidation({'field': field, 'model': model, 'filter':\n filter}, self._check_with_nonDuplicateObjectValidator)\n return self\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n\n def addValidation(self, data, validatorFunction):\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n\n\nclass ObjectValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def checkType(self, field, type, value='unAssigned'):\n self.addValidation({'field': field, 'type': type, 'value': value},\n self._check_with_typeValidator)\n return self\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n\n def checkEmail(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_emailValidator)\n return self\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\n if mode == 'equal':\n validatorFunction = self._check_with_equalDataLengthValidator\n if mode == 'min':\n validatorFunction = self._check_with_minDataLengthValidator\n if mode == 'max':\n validatorFunction = self._check_with_minDataLengthValidator\n self.addValidation({'field': field, 'value': data, 'length': length\n }, validatorFunction)\n return self\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n\n def checkFeatures(self, field, features='unAssigned'):\n self.addValidation({'field': field, 'value': features}, self.\n _check_with_featuresValidator)\n return self\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\n data = {'userId': userId, 'subId': subId}\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_subMerchantBankAccountValidator)\n return self\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n\n def _check_with_phoneNumberValidator(self, data):\n if data['value'] is None or len(data) < 1:\n self.setError(data['field'], enum.Error.\n PHONE_INCORRECT_TEMPLATE.value)\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n\n def _check_with_minDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) < data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.validationPipeline = []\n self.statusCode = 200\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_featuresValidator(self, data):\n for i in data['value']:\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\n 'درگاه پرداخت اینترنتی']:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\n value)\n break\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n\n def setError(self, field, error):\n if field not in self.invalidFields:\n fieldErrors = self.errors.get(field, [])\n if error[0] not in fieldErrors:\n self.errors[field] = fieldErrors + [error[0]]\n self.statusCode = error[1]\n self.invalidFields.append(field)\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n\n def _check_with_IBANValidator(self, data):\n iban = data['value']\n if len(iban) != 26 or not iban.startswith('IR'):\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n return\n code = iban[4:] + iban[:4]\n code = code.replace('I', '18').replace('R', '27')\n if int(code) % 97 != 1:\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_nationalLegalCodeValidator(self, data):\n nationalLegalCode = data['value']\n result = 0\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\n if len(nationalLegalCode) != 11:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n return\n for i in range(10):\n result += (int(nationalLegalCode[-2]) + 2 + int(\n nationalLegalCode[i])) * validationList[i]\n if result % 11 == 10:\n reminder = 0\n else:\n reminder = result % 11\n if reminder == int(nationalLegalCode[-1]):\n valid = True\n else:\n valid = False\n if valid is False:\n self.setError(data['field'], enum.Error.\n INVALID_NATIONAL_LEGAL_CODE.value)\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n\n def checkNationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalCodeValidator)\n return self\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer2NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n\n def checkIBAN(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IBANValidator)\n return self\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n\n def checkInputData(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_inputValidator)\n return self\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_noneValidator(self, data):\n if data['value'] is None or data['value'] == '':\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n\n def _check_with_username(self, data):\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\n if username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n\n def validate(self):\n for validation in self.validationPipeline:\n try:\n validation['validator'](validation['data'])\n except:\n self.setError(validation['data']['field'], enum.Error.\n INVALID_FIELD_DATA.value)\n return self\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_nationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n valid = False\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n\n def flush(self):\n self = FieldValidator()\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer2NationalCodeValidator)\n return self\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n\n def _check_with_subMerchantBankAccountValidator(self, data):\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\n =data['value']['subId'], status=1).exists():\n self.setError(data['field'], enum.Error.\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_equalDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) != data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkIsIbanTransferable(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_IbanTransferable)\n return self\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkTelephone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n <function token>\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_officer1NationalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n\n def checkFile(self, field, data, **options):\n self.addValidation({'field': field, 'value': data, 'options':\n options}, self._check_with_fileValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n\n def checkMobile(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_mobileValidator)\n return self\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n <function token>\n <function token>\n\n def checkNotNone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_noneValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUsername(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_username())\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n\n def checkPhone(self, field, data='unAssigned'):\n self.addValidation({'field': field, 'value': data}, self.\n _check_with_phoneNumberValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n\n def checkUserName(self, field, username='unAssigned'):\n self.addValidation({'field': field, 'value': username}, self.\n _check_with_userNameValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n\n def getErrors(self):\n return self.errors\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_officer1NationalCodeValidator(self, data):\n nCode = data['value']\n valid = True\n if len(nCode) != 10:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n return\n sum = 0\n for i in range(9):\n sum += int(nCode[i]) * (10 - i)\n r = sum % 11\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\n valid = valid and True\n if valid is False:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def addValidation(self, data, validatorFunction):\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\n data['value'] = self.data[data['field']]\n elif data['value'] == 'unAssigned' and data['field'\n ] not in self.data.keys():\n data['value'] = None\n self.validationPipeline.append({'data': data, 'validator':\n validatorFunction})\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n\n def _check_with_inputValidator(self, data):\n if data['value'] is None or len(data['value']) < 1:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_userNameValidator(self, data):\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\n if 'admin' in data['value'] or 'zibal' in data['value'\n ] or username is None:\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n <function token>\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_mobileValidator(self, data):\n mobileNumber = data['value']\n if mobileNumber is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\n if match_object is None or mobileNumber is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n <function token>\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n <function token>\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n\n def checkNationalLegalCode(self, field, code='unAssigned'):\n self.addValidation({'field': field, 'value': code}, self.\n _check_with_nationalLegalCodeValidator)\n return self\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n\n def _check_with_maxDataLengthValidator(self, data):\n if data['value'] is None or len(data['value']) > data['length']:\n self.setError(data['field'], (enum.Error.\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\n <function token>\n <function token>\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_typeValidator(self, data):\n if not isinstance(data['value'], data['type']):\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_emailValidator(self, data):\n email = data['value']\n if email is None:\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\n return\n match_object = re.match(\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)', email)\n if match_object is None or email is None:\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_IbanTransferable(self, data):\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\n )\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def _check_with_fileValidator(self, data):\n file = data['value']\n field = data['field']\n if file is None:\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\n return\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\n types = data['options'].get('types', None)\n valid = False\n if types is not None:\n for type in types:\n valid = valid or type in file.content_type\n if valid is False:\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n\n\nclass FieldValidator:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n<class token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n\n def generateMessage(self):\n messages = []\n errorKeys = self.errors.keys()\n if 'email' in errorKeys:\n messages.append(' آدرس ایمیل نامعتبر است')\n if 'name' in errorKeys:\n messages.append('نام را وارد کنید')\n if 'username' in errorKeys:\n messages.append('نام کاربری را وارد کنید')\n if 'password' in errorKeys:\n messages.append('رمز عبور را وارد کنید')\n if 'mobile' in errorKeys:\n messages.append('تلفن همراه خود را وارد کنید.')\n if 'phone' in errorKeys:\n messages.append(\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\n messages.append(\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\n )\n if 'user' in errorKeys:\n messages.append('لطفا وارد شوید')\n return messages\n","<import token>\n<class token>\n<class token>\n\n\nclass DataValidator:\n\n def __init__(self, data={}):\n self.fieldValidator = FieldValidator(data)\n self.objectValidator = ObjectValidator()\n self.errors = {}\n self.statusCode = 200\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n <function token>\n","<import token>\n<class token>\n<class token>\n\n\nclass DataValidator:\n <function token>\n\n def getValidatorsErrors(self):\n self.objectValidator.validate()\n self.fieldValidator.validate()\n for key in self.fieldValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.fieldValidator.getErrors()[key]\n self.statusCode = self.fieldValidator.statusCode\n for key in self.objectValidator.getErrors().keys():\n self.errors[key] = self.errors.get(key, []\n ) + self.objectValidator.getErrors()[key]\n self.statusCode = (self.objectValidator.statusCode if self.\n objectValidator.statusCode != 200 else self.statusCode)\n return self.errors\n <function token>\n","<import token>\n<class token>\n<class token>\n\n\nclass DataValidator:\n <function token>\n <function token>\n <function token>\n","<import token>\n<class token>\n<class token>\n<class token>\n"],"string":"[\n \"import API.enum as enum\\nimport re\\n\\nclass ObjectValidator():\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = ObjectValidator()\\n return self\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({\\n 'data': data,\\n 'validator': validatorFunction\\n })\\n\\n def _check_with_authenticationValidator(self, data):\\n if not data['user'].is_authenticated:\\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter': filter},\\n self._check_with_nonDuplicateObjectValidator)\\n return self\\n\\n def checkObjectExistence(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter': filter},\\n self._check_with_ObjectExistenceValidator)\\n return self\\n\\n def checkUserAuthentication(self, field, user):\\n self.addValidation({'field': field, 'user': user},\\n self._check_with_authenticationValidator)\\n return self\\n\\n\\n#\\\\b(?!(\\\\d)\\\\1{3})[13-9]{4}[1346-9][013-9]{5}\\\\b\\n# postal code validation\\n\\n\\nclass FieldValidator():\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return self\\n def addValidation(self, data, validatorFunction):\\n if (data['value'] == 'unAssigned') and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({\\n 'data': data,\\n 'validator': validatorFunction\\n })\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if (r < 2 and r == int(nCode[9])) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in [\\\"پلتفرم پرداخت در محل\\\", \\\"باشگاه مشتریان\\\", \\\"درگاه پرداخت اینترنتی\\\"]:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match(r\\\"^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$\\\", data[\\\"value\\\"])\\n if 'admin' in data['value'] or 'zibal' in data['value'] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(r\\\"(^09[0-9]{9}$)\\\", mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(r\\\"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\.[a-zA-Z0-9-.]+$)\\\", email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == \\\"\\\":\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban)!=26 or not iban.startswith(\\\"IR\\\"):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:]+iban[:4]\\n code = code.replace('I','18').replace('R','27')\\n if int(code)%97!=1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID=data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7]=='062' and data['value'][-13:-10]=='080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value)\\n\\n def _check_with_username(self, data):\\n username = re.match(r\\\"^[a-zA-Z0-9_.-]+$\\\", data[\\\"value\\\"])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n #############################################################################\\n\\n def checkType(self, field, type, value=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'type': type, 'value': value}, self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': code}, self._check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': code}, self._check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': code}, self._check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': code}, self._check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': features}, self._check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': username}, self._check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options': options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {\\n 'userId': userId,\\n 'subId': subId\\n }\\n self.addValidation({'field': field, 'value': data}, self._check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length,mode='equal', data=\\\"unAssigned\\\"):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n\\n self.addValidation({'field': field, 'value': data, 'length': length}, validatorFunction)\\n\\n return self\\n\\n def checkInputData(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data=\\\"unAssigned\\\"): ##TODO\\n self.addValidation({'field': field, 'value': data}, self._check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data=\\\"unAssigned\\\"):\\n self.addValidation({'field': field, 'value': data}, self._check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []) + self.objectValidator.getErrors()[key]\\n self.statusCode = self.objectValidator.statusCode if self.objectValidator.statusCode != 200 else self.statusCode\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n\\n if \\\"name\\\" in errorKeys :\\n messages.append('نام را وارد کنید')\\n\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n\\n if 'phone' in errorKeys:\\n messages.append('تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append('شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله')\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n\\n return messages\",\n \"import API.enum as enum\\nimport re\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = ObjectValidator()\\n return self\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_authenticationValidator(self, data):\\n if not data['user'].is_authenticated:\\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n\\n def checkObjectExistence(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_ObjectExistenceValidator)\\n return self\\n\\n def checkUserAuthentication(self, field, user):\\n self.addValidation({'field': field, 'user': user}, self.\\n _check_with_authenticationValidator)\\n return self\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = ObjectValidator()\\n return self\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_authenticationValidator(self, data):\\n if not data['user'].is_authenticated:\\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n\\n def checkObjectExistence(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_ObjectExistenceValidator)\\n return self\\n\\n def checkUserAuthentication(self, field, user):\\n self.addValidation({'field': field, 'user': user}, self.\\n _check_with_authenticationValidator)\\n return self\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = ObjectValidator()\\n return self\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_authenticationValidator(self, data):\\n if not data['user'].is_authenticated:\\n self.setError(data['field'], enum.Error.UNAUTHORIZED.value)\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n\\n def checkObjectExistence(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_ObjectExistenceValidator)\\n return self\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = ObjectValidator()\\n return self\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n\\n def checkObjectExistence(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_ObjectExistenceValidator)\\n return self\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = ObjectValidator()\\n return self\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n\\n def checkObjectExistence(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_ObjectExistenceValidator)\\n return self\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = ObjectValidator()\\n return self\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n <function token>\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n <function token>\\n <function token>\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n\\n def _check_with_ObjectExistenceValidator(self, data):\\n model = data['model']\\n if not model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.\\n GENERIC_OBJECT_NOT_FOUND.value)\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n <function token>\\n <function token>\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n\\n def _check_with_nonDuplicateObjectValidator(self, data):\\n model = data['model']\\n if model.objects.filter(**data['filter']):\\n self.setError(data['field'], enum.Error.DUPLICATE_FIELDS.value)\\n <function token>\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.statusCode = 200\\n self.validationPipeline = []\\n self.errors = {}\\n self.invalidFields = []\\n <function token>\\n <function token>\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkNonDuplicateObject(self, field, model, **filter):\\n self.addValidation({'field': field, 'model': model, 'filter':\\n filter}, self._check_with_nonDuplicateObjectValidator)\\n return self\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n\\n def addValidation(self, data, validatorFunction):\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n\\n\\nclass ObjectValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def checkType(self, field, type, value='unAssigned'):\\n self.addValidation({'field': field, 'type': type, 'value': value},\\n self._check_with_typeValidator)\\n return self\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n\\n def checkEmail(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_emailValidator)\\n return self\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n\\n def checkDataLength(self, field, length, mode='equal', data='unAssigned'):\\n if mode == 'equal':\\n validatorFunction = self._check_with_equalDataLengthValidator\\n if mode == 'min':\\n validatorFunction = self._check_with_minDataLengthValidator\\n if mode == 'max':\\n validatorFunction = self._check_with_minDataLengthValidator\\n self.addValidation({'field': field, 'value': data, 'length': length\\n }, validatorFunction)\\n return self\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n\\n def checkFeatures(self, field, features='unAssigned'):\\n self.addValidation({'field': field, 'value': features}, self.\\n _check_with_featuresValidator)\\n return self\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n\\n def checkBankAccountDestinationForSubmerchant(self, field, userId, subId):\\n data = {'userId': userId, 'subId': subId}\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_subMerchantBankAccountValidator)\\n return self\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n\\n def _check_with_phoneNumberValidator(self, data):\\n if data['value'] is None or len(data) < 1:\\n self.setError(data['field'], enum.Error.\\n PHONE_INCORRECT_TEMPLATE.value)\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n\\n def _check_with_minDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) < data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n\\n def __init__(self, validationData={}, *args, **kwargs):\\n self.data = validationData\\n self.validationPipeline = []\\n self.statusCode = 200\\n self.errors = {}\\n self.invalidFields = []\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_featuresValidator(self, data):\\n for i in data['value']:\\n if i not in ['پلتفرم پرداخت در محل', 'باشگاه مشتریان',\\n 'درگاه پرداخت اینترنتی']:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.\\n value)\\n break\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n\\n def setError(self, field, error):\\n if field not in self.invalidFields:\\n fieldErrors = self.errors.get(field, [])\\n if error[0] not in fieldErrors:\\n self.errors[field] = fieldErrors + [error[0]]\\n self.statusCode = error[1]\\n self.invalidFields.append(field)\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n\\n def _check_with_IBANValidator(self, data):\\n iban = data['value']\\n if len(iban) != 26 or not iban.startswith('IR'):\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n return\\n code = iban[4:] + iban[:4]\\n code = code.replace('I', '18').replace('R', '27')\\n if int(code) % 97 != 1:\\n self.setError(data['field'], enum.Error.IBAN_ERROR.value)\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_nationalLegalCodeValidator(self, data):\\n nationalLegalCode = data['value']\\n result = 0\\n validationList = [29, 27, 23, 19, 17, 29, 27, 23, 19, 17]\\n if len(nationalLegalCode) != 11:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n return\\n for i in range(10):\\n result += (int(nationalLegalCode[-2]) + 2 + int(\\n nationalLegalCode[i])) * validationList[i]\\n if result % 11 == 10:\\n reminder = 0\\n else:\\n reminder = result % 11\\n if reminder == int(nationalLegalCode[-1]):\\n valid = True\\n else:\\n valid = False\\n if valid is False:\\n self.setError(data['field'], enum.Error.\\n INVALID_NATIONAL_LEGAL_CODE.value)\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n\\n def checkNationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalCodeValidator)\\n return self\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer2NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n\\n def checkIBAN(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IBANValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkInputData(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_inputValidator)\\n return self\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_noneValidator(self, data):\\n if data['value'] is None or data['value'] == '':\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n\\n def _check_with_username(self, data):\\n username = re.match('^[a-zA-Z0-9_.-]+$', data['value'])\\n if username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n\\n def validate(self):\\n for validation in self.validationPipeline:\\n try:\\n validation['validator'](validation['data'])\\n except:\\n self.setError(validation['data']['field'], enum.Error.\\n INVALID_FIELD_DATA.value)\\n return self\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_nationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n valid = False\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n\\n def flush(self):\\n self = FieldValidator()\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n\\n def checkOfficer2NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer2NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n\\n def _check_with_subMerchantBankAccountValidator(self, data):\\n if not SubMerchant.objects.filter(idsql=data['value']['userId'], ID\\n =data['value']['subId'], status=1).exists():\\n self.setError(data['field'], enum.Error.\\n IMPOSSIBLE_BANK_ACCOUNT_DESTINATION.value)\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_equalDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) != data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkIsIbanTransferable(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_IbanTransferable)\\n return self\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkTelephone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n <function token>\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n\\n def checkOfficer1NationalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_officer1NationalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n\\n def checkFile(self, field, data, **options):\\n self.addValidation({'field': field, 'value': data, 'options':\\n options}, self._check_with_fileValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n\\n def checkMobile(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_mobileValidator)\\n return self\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n <function token>\\n <function token>\\n\\n def checkNotNone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_noneValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUsername(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_username())\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n\\n def checkPhone(self, field, data='unAssigned'):\\n self.addValidation({'field': field, 'value': data}, self.\\n _check_with_phoneNumberValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def checkUserName(self, field, username='unAssigned'):\\n self.addValidation({'field': field, 'value': username}, self.\\n _check_with_userNameValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n\\n def getErrors(self):\\n return self.errors\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_officer1NationalCodeValidator(self, data):\\n nCode = data['value']\\n valid = True\\n if len(nCode) != 10:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n return\\n sum = 0\\n for i in range(9):\\n sum += int(nCode[i]) * (10 - i)\\n r = sum % 11\\n if r < 2 and r == int(nCode[9]) or r >= 2 and r == 11 - int(nCode[9]):\\n valid = valid and True\\n if valid is False:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def addValidation(self, data, validatorFunction):\\n if data['value'] == 'unAssigned' and data['field'] in self.data.keys():\\n data['value'] = self.data[data['field']]\\n elif data['value'] == 'unAssigned' and data['field'\\n ] not in self.data.keys():\\n data['value'] = None\\n self.validationPipeline.append({'data': data, 'validator':\\n validatorFunction})\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n\\n def _check_with_inputValidator(self, data):\\n if data['value'] is None or len(data['value']) < 1:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_userNameValidator(self, data):\\n username = re.match('^[A-Za-z]+(?:[ _-][A-Za-z0-9]+)*$', data['value'])\\n if 'admin' in data['value'] or 'zibal' in data['value'\\n ] or username is None:\\n self.setError(data['field'], enum.Error.INVALID_USERNAME.value)\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n <function token>\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_mobileValidator(self, data):\\n mobileNumber = data['value']\\n if mobileNumber is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match('(^09[0-9]{9}$)', mobileNumber)\\n if match_object is None or mobileNumber is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n <function token>\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n <function token>\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n\\n def checkNationalLegalCode(self, field, code='unAssigned'):\\n self.addValidation({'field': field, 'value': code}, self.\\n _check_with_nationalLegalCodeValidator)\\n return self\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_maxDataLengthValidator(self, data):\\n if data['value'] is None or len(data['value']) > data['length']:\\n self.setError(data['field'], (enum.Error.\\n MESSAGE_INSUFFICIENT_LENGTH.value[0].format(data['length']),\\n enum.Error.MESSAGE_INSUFFICIENT_LENGTH.value[1]))\\n <function token>\\n <function token>\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_typeValidator(self, data):\\n if not isinstance(data['value'], data['type']):\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_emailValidator(self, data):\\n email = data['value']\\n if email is None:\\n self.setError(data['field'], enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n match_object = re.match(\\n '(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\\\\\\\.[a-zA-Z0-9-.]+$)', email)\\n if match_object is None or email is None:\\n self.setError(data['field'], enum.Error.INVALID_FIELD_DATA.value)\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_IbanTransferable(self, data):\\n if data['value'][4:7] == '062' and data['value'][-13:-10] == '080':\\n self.setError(data['field'], enum.Error.NOT_IBAN_TRANSFERABLE.value\\n )\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def _check_with_fileValidator(self, data):\\n file = data['value']\\n field = data['field']\\n if file is None:\\n self.setError(field, enum.Error.EMPTY_INPUT_FIELD.value)\\n return\\n elif file.size > enum.Limits.FILE_SIZE_LIMIT.value:\\n self.setError(field, enum.Error.FILE_SIZE_EXCEED.value)\\n types = data['options'].get('types', None)\\n valid = False\\n if types is not None:\\n for type in types:\\n valid = valid or type in file.content_type\\n if valid is False:\\n self.setError(field, enum.Error.REQUEST_TYPE_ERROR.value)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass FieldValidator:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n\\n def generateMessage(self):\\n messages = []\\n errorKeys = self.errors.keys()\\n if 'email' in errorKeys:\\n messages.append(' آدرس ایمیل نامعتبر است')\\n if 'name' in errorKeys:\\n messages.append('نام را وارد کنید')\\n if 'username' in errorKeys:\\n messages.append('نام کاربری را وارد کنید')\\n if 'password' in errorKeys:\\n messages.append('رمز عبور را وارد کنید')\\n if 'mobile' in errorKeys:\\n messages.append('تلفن همراه خود را وارد کنید.')\\n if 'phone' in errorKeys:\\n messages.append(\\n 'تلفن ثابت را به فرمت 02122407556 و 11 رقمی وارد کنید')\\n if 'iban' in errorKeys or 'IBAN' in errorKeys:\\n messages.append(\\n 'شماره شبای وارد شده معتبر نیست. 26 کاراکتر و شروع با IR و بدون خط تیره (-) و فاصله'\\n )\\n if 'user' in errorKeys:\\n messages.append('لطفا وارد شوید')\\n return messages\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass DataValidator:\\n\\n def __init__(self, data={}):\\n self.fieldValidator = FieldValidator(data)\\n self.objectValidator = ObjectValidator()\\n self.errors = {}\\n self.statusCode = 200\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass DataValidator:\\n <function token>\\n\\n def getValidatorsErrors(self):\\n self.objectValidator.validate()\\n self.fieldValidator.validate()\\n for key in self.fieldValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.fieldValidator.getErrors()[key]\\n self.statusCode = self.fieldValidator.statusCode\\n for key in self.objectValidator.getErrors().keys():\\n self.errors[key] = self.errors.get(key, []\\n ) + self.objectValidator.getErrors()[key]\\n self.statusCode = (self.objectValidator.statusCode if self.\\n objectValidator.statusCode != 200 else self.statusCode)\\n return self.errors\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass DataValidator:\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7575,"cells":{"index":{"kind":"number","value":7575,"string":"7,575"},"blob_id":{"kind":"string","value":"74e3f4cd7b09d9b96feb3f927a509b113481eaed"},"code":{"kind":"string","value":"from django.apps import AppConfig\n\n\nclass CheckoutConfig(AppConfig):\n name = \"checkout\"\n\n # Override the ready method and import the signals module\n # so that update_on_save and update_on_delete will be called\n # after an OrderLineItem model instance is saved or deleted\n def ready(self):\n import checkout.signals\n"},"steps":{"kind":"list like","value":["from django.apps import AppConfig\n\n\nclass CheckoutConfig(AppConfig):\n name = \"checkout\"\n\n # Override the ready method and import the signals module\n # so that update_on_save and update_on_delete will be called\n # after an OrderLineItem model instance is saved or deleted\n def ready(self):\n import checkout.signals\n","from django.apps import AppConfig\n\n\nclass CheckoutConfig(AppConfig):\n name = 'checkout'\n\n def ready(self):\n import checkout.signals\n","<import token>\n\n\nclass CheckoutConfig(AppConfig):\n name = 'checkout'\n\n def ready(self):\n import checkout.signals\n","<import token>\n\n\nclass CheckoutConfig(AppConfig):\n <assignment token>\n\n def ready(self):\n import checkout.signals\n","<import token>\n\n\nclass CheckoutConfig(AppConfig):\n <assignment token>\n <function token>\n","<import token>\n<class token>\n"],"string":"[\n \"from django.apps import AppConfig\\n\\n\\nclass CheckoutConfig(AppConfig):\\n name = \\\"checkout\\\"\\n\\n # Override the ready method and import the signals module\\n # so that update_on_save and update_on_delete will be called\\n # after an OrderLineItem model instance is saved or deleted\\n def ready(self):\\n import checkout.signals\\n\",\n \"from django.apps import AppConfig\\n\\n\\nclass CheckoutConfig(AppConfig):\\n name = 'checkout'\\n\\n def ready(self):\\n import checkout.signals\\n\",\n \"<import token>\\n\\n\\nclass CheckoutConfig(AppConfig):\\n name = 'checkout'\\n\\n def ready(self):\\n import checkout.signals\\n\",\n \"<import token>\\n\\n\\nclass CheckoutConfig(AppConfig):\\n <assignment token>\\n\\n def ready(self):\\n import checkout.signals\\n\",\n \"<import token>\\n\\n\\nclass CheckoutConfig(AppConfig):\\n <assignment token>\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7576,"cells":{"index":{"kind":"number","value":7576,"string":"7,576"},"blob_id":{"kind":"string","value":"0a19efea0c8d7e5e248ca3265ffcb55604dc500c"},"code":{"kind":"string","value":"__author__ = 'Administrator'\n\nimport socket,os,time\n\nserver = socket.socket()\n\nserver.bind((\"localhost\",9999))\n\nserver.listen()\n\nwhile True:\n conn,addr = server.accept()\n\n while True:\n data = conn.recv(1024)\n if not data:\n break\n\n cmd,filename = data.decode().split()\n\n if os.path.isfile(filename)\n f = open(filename,\"rb\")\n m = hashlib.md5()\n file_size = os.stat(filename).st_size\n conn.send(str(file_size).encode())\n conn.recv(1024)\n for line in f:\n m.update(line)\n conn.send(line)\n print(\"file_md5\",m.hexdigest())\n f.close()\nserver.close()\n"},"steps":{"kind":"list like","value":["__author__ = 'Administrator'\n\nimport socket,os,time\n\nserver = socket.socket()\n\nserver.bind((\"localhost\",9999))\n\nserver.listen()\n\nwhile True:\n conn,addr = server.accept()\n\n while True:\n data = conn.recv(1024)\n if not data:\n break\n\n cmd,filename = data.decode().split()\n\n if os.path.isfile(filename)\n f = open(filename,\"rb\")\n m = hashlib.md5()\n file_size = os.stat(filename).st_size\n conn.send(str(file_size).encode())\n conn.recv(1024)\n for line in f:\n m.update(line)\n conn.send(line)\n print(\"file_md5\",m.hexdigest())\n f.close()\nserver.close()\n"],"string":"[\n \"__author__ = 'Administrator'\\n\\nimport socket,os,time\\n\\nserver = socket.socket()\\n\\nserver.bind((\\\"localhost\\\",9999))\\n\\nserver.listen()\\n\\nwhile True:\\n conn,addr = server.accept()\\n\\n while True:\\n data = conn.recv(1024)\\n if not data:\\n break\\n\\n cmd,filename = data.decode().split()\\n\\n if os.path.isfile(filename)\\n f = open(filename,\\\"rb\\\")\\n m = hashlib.md5()\\n file_size = os.stat(filename).st_size\\n conn.send(str(file_size).encode())\\n conn.recv(1024)\\n for line in f:\\n m.update(line)\\n conn.send(line)\\n print(\\\"file_md5\\\",m.hexdigest())\\n f.close()\\nserver.close()\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":7577,"cells":{"index":{"kind":"number","value":7577,"string":"7,577"},"blob_id":{"kind":"string","value":"4c8a873c816678532b029af409be13258757eae1"},"code":{"kind":"string","value":"# Напишите программу, которая вводит с клавиатуры последовательность чисел и выводит её\n# отсортированной в порядке возрастания.\n\n\ndef is_numb_val(val):\n try:\n x = float(val)\n except ValueError:\n return False\n else:\n return True\n\ndef main():\n num_seq = input(\"Введите последовательность чисел через пробел: \").split()\n num_lst = [float(s) for s in num_seq if is_numb_val(s)]\n print(sorted(num_lst))\n\n\nif __name__ == '__main__':\n main()\n"},"steps":{"kind":"list like","value":["# Напишите программу, которая вводит с клавиатуры последовательность чисел и выводит её\n# отсортированной в порядке возрастания.\n\n\ndef is_numb_val(val):\n try:\n x = float(val)\n except ValueError:\n return False\n else:\n return True\n\ndef main():\n num_seq = input(\"Введите последовательность чисел через пробел: \").split()\n num_lst = [float(s) for s in num_seq if is_numb_val(s)]\n print(sorted(num_lst))\n\n\nif __name__ == '__main__':\n main()\n","def is_numb_val(val):\n try:\n x = float(val)\n except ValueError:\n return False\n else:\n return True\n\n\ndef main():\n num_seq = input('Введите последовательность чисел через пробел: ').split()\n num_lst = [float(s) for s in num_seq if is_numb_val(s)]\n print(sorted(num_lst))\n\n\nif __name__ == '__main__':\n main()\n","def is_numb_val(val):\n try:\n x = float(val)\n except ValueError:\n return False\n else:\n return True\n\n\ndef main():\n num_seq = input('Введите последовательность чисел через пробел: ').split()\n num_lst = [float(s) for s in num_seq if is_numb_val(s)]\n print(sorted(num_lst))\n\n\n<code token>\n","def is_numb_val(val):\n try:\n x = float(val)\n except ValueError:\n return False\n else:\n return True\n\n\n<function token>\n<code token>\n","<function token>\n<function token>\n<code token>\n"],"string":"[\n \"# Напишите программу, которая вводит с клавиатуры последовательность чисел и выводит её\\n# отсортированной в порядке возрастания.\\n\\n\\ndef is_numb_val(val):\\n try:\\n x = float(val)\\n except ValueError:\\n return False\\n else:\\n return True\\n\\ndef main():\\n num_seq = input(\\\"Введите последовательность чисел через пробел: \\\").split()\\n num_lst = [float(s) for s in num_seq if is_numb_val(s)]\\n print(sorted(num_lst))\\n\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"def is_numb_val(val):\\n try:\\n x = float(val)\\n except ValueError:\\n return False\\n else:\\n return True\\n\\n\\ndef main():\\n num_seq = input('Введите последовательность чисел через пробел: ').split()\\n num_lst = [float(s) for s in num_seq if is_numb_val(s)]\\n print(sorted(num_lst))\\n\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"def is_numb_val(val):\\n try:\\n x = float(val)\\n except ValueError:\\n return False\\n else:\\n return True\\n\\n\\ndef main():\\n num_seq = input('Введите последовательность чисел через пробел: ').split()\\n num_lst = [float(s) for s in num_seq if is_numb_val(s)]\\n print(sorted(num_lst))\\n\\n\\n<code token>\\n\",\n \"def is_numb_val(val):\\n try:\\n x = float(val)\\n except ValueError:\\n return False\\n else:\\n return True\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<function token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7578,"cells":{"index":{"kind":"number","value":7578,"string":"7,578"},"blob_id":{"kind":"string","value":"20d363f5d02cc0b1069aa8951999c0cb22b85613"},"code":{"kind":"string","value":"# This is a module\n\nclass MyMath:\n def isEven(num):\n if(num%2==0):\n return True\n return False\n \n def isOdd(num):\n if(num%2==0):\n return False\n return True\n \n def isPrime(num):\n for i in range(2,num):\n if num%i==0:\n return False\n return True\n \nclass Calsi:\n def add(num1, num2):\n return num1+num2\n \n def sub(num1, num2):\n return num1-num2\n \n def mul(num1,num2):\n return num1*num2\n "},"steps":{"kind":"list like","value":["# This is a module\n\nclass MyMath:\n def isEven(num):\n if(num%2==0):\n return True\n return False\n \n def isOdd(num):\n if(num%2==0):\n return False\n return True\n \n def isPrime(num):\n for i in range(2,num):\n if num%i==0:\n return False\n return True\n \nclass Calsi:\n def add(num1, num2):\n return num1+num2\n \n def sub(num1, num2):\n return num1-num2\n \n def mul(num1,num2):\n return num1*num2\n ","class MyMath:\n\n def isEven(num):\n if num % 2 == 0:\n return True\n return False\n\n def isOdd(num):\n if num % 2 == 0:\n return False\n return True\n\n def isPrime(num):\n for i in range(2, num):\n if num % i == 0:\n return False\n return True\n\n\nclass Calsi:\n\n def add(num1, num2):\n return num1 + num2\n\n def sub(num1, num2):\n return num1 - num2\n\n def mul(num1, num2):\n return num1 * num2\n","class MyMath:\n\n def isEven(num):\n if num % 2 == 0:\n return True\n return False\n <function token>\n\n def isPrime(num):\n for i in range(2, num):\n if num % i == 0:\n return False\n return True\n\n\nclass Calsi:\n\n def add(num1, num2):\n return num1 + num2\n\n def sub(num1, num2):\n return num1 - num2\n\n def mul(num1, num2):\n return num1 * num2\n","class MyMath:\n <function token>\n <function token>\n\n def isPrime(num):\n for i in range(2, num):\n if num % i == 0:\n return False\n return True\n\n\nclass Calsi:\n\n def add(num1, num2):\n return num1 + num2\n\n def sub(num1, num2):\n return num1 - num2\n\n def mul(num1, num2):\n return num1 * num2\n","class MyMath:\n <function token>\n <function token>\n <function token>\n\n\nclass Calsi:\n\n def add(num1, num2):\n return num1 + num2\n\n def sub(num1, num2):\n return num1 - num2\n\n def mul(num1, num2):\n return num1 * num2\n","<class token>\n\n\nclass Calsi:\n\n def add(num1, num2):\n return num1 + num2\n\n def sub(num1, num2):\n return num1 - num2\n\n def mul(num1, num2):\n return num1 * num2\n","<class token>\n\n\nclass Calsi:\n <function token>\n\n def sub(num1, num2):\n return num1 - num2\n\n def mul(num1, num2):\n return num1 * num2\n","<class token>\n\n\nclass Calsi:\n <function token>\n <function token>\n\n def mul(num1, num2):\n return num1 * num2\n","<class token>\n\n\nclass Calsi:\n <function token>\n <function token>\n <function token>\n","<class token>\n<class token>\n"],"string":"[\n \"# This is a module\\n\\nclass MyMath:\\n def isEven(num):\\n if(num%2==0):\\n return True\\n return False\\n \\n def isOdd(num):\\n if(num%2==0):\\n return False\\n return True\\n \\n def isPrime(num):\\n for i in range(2,num):\\n if num%i==0:\\n return False\\n return True\\n \\nclass Calsi:\\n def add(num1, num2):\\n return num1+num2\\n \\n def sub(num1, num2):\\n return num1-num2\\n \\n def mul(num1,num2):\\n return num1*num2\\n \",\n \"class MyMath:\\n\\n def isEven(num):\\n if num % 2 == 0:\\n return True\\n return False\\n\\n def isOdd(num):\\n if num % 2 == 0:\\n return False\\n return True\\n\\n def isPrime(num):\\n for i in range(2, num):\\n if num % i == 0:\\n return False\\n return True\\n\\n\\nclass Calsi:\\n\\n def add(num1, num2):\\n return num1 + num2\\n\\n def sub(num1, num2):\\n return num1 - num2\\n\\n def mul(num1, num2):\\n return num1 * num2\\n\",\n \"class MyMath:\\n\\n def isEven(num):\\n if num % 2 == 0:\\n return True\\n return False\\n <function token>\\n\\n def isPrime(num):\\n for i in range(2, num):\\n if num % i == 0:\\n return False\\n return True\\n\\n\\nclass Calsi:\\n\\n def add(num1, num2):\\n return num1 + num2\\n\\n def sub(num1, num2):\\n return num1 - num2\\n\\n def mul(num1, num2):\\n return num1 * num2\\n\",\n \"class MyMath:\\n <function token>\\n <function token>\\n\\n def isPrime(num):\\n for i in range(2, num):\\n if num % i == 0:\\n return False\\n return True\\n\\n\\nclass Calsi:\\n\\n def add(num1, num2):\\n return num1 + num2\\n\\n def sub(num1, num2):\\n return num1 - num2\\n\\n def mul(num1, num2):\\n return num1 * num2\\n\",\n \"class MyMath:\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass Calsi:\\n\\n def add(num1, num2):\\n return num1 + num2\\n\\n def sub(num1, num2):\\n return num1 - num2\\n\\n def mul(num1, num2):\\n return num1 * num2\\n\",\n \"<class token>\\n\\n\\nclass Calsi:\\n\\n def add(num1, num2):\\n return num1 + num2\\n\\n def sub(num1, num2):\\n return num1 - num2\\n\\n def mul(num1, num2):\\n return num1 * num2\\n\",\n \"<class token>\\n\\n\\nclass Calsi:\\n <function token>\\n\\n def sub(num1, num2):\\n return num1 - num2\\n\\n def mul(num1, num2):\\n return num1 * num2\\n\",\n \"<class token>\\n\\n\\nclass Calsi:\\n <function token>\\n <function token>\\n\\n def mul(num1, num2):\\n return num1 * num2\\n\",\n \"<class token>\\n\\n\\nclass Calsi:\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<class token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7579,"cells":{"index":{"kind":"number","value":7579,"string":"7,579"},"blob_id":{"kind":"string","value":"31b87a3ceca1f48665ecc9754d5f87bb9b7bbf13"},"code":{"kind":"string","value":"import psycopg2\n\nfrom .configuration import ConfigurationException\n\nDB_CONNECT_STRING = \"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\"\n\nclass DBItemCompany:\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore, ranking, location, url, categoryId):\n self.id = _id\n self.twitterAccount = tweeter\n self.category = category\n self.categoryUrl = categoryUrl\n self.provenScore = provenScore\n self.ranking = ranking\n self.location = location\n self.url = url\n self.categoryId = categoryId\n\n @property\n def invalidScore(self):\n return self.provenScore is None or self.provenScore < 1\n\n @property\n def twitter(self):\n return '@' + self.twitterAccount\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\nclass DBManager:\n def __init__(self, cfg):\n self.cfg = cfg\n\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1],\n user=dbSettings[2], passwd=dbSettings[3]\n ))\n self.cursor = self.connection.cursor()\n\n self.__loggedIn = True\n\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\".format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain=self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain=self.__getDomainName(schemaName),\n categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.__getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\".format(schema=schema))\n data = self.cursor.fetchall()\n\n companies = []\n for entry in data:\n self.cursor.execute('SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'.format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n\n if cities is None:\n continue\n\n city = ''\n\n for cityId in cities:\n self.cursor.execute('SELECT city FROM {schema}.locations_location WHERE id = {city}'.format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n\n if cityName is not None:\n city += cityName[0]\n\n self.cursor.execute('SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'.format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n\n if catId is not None:\n self.cursor.execute('SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'.format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n\n companies.append(DBItemCompany(\n _id = entry[0],\n tweeter = entry[1],\n category = catData[0] if catData is not None else None,\n categoryUrl = self.__buildCategoryUrl(catId, schema) if catId is not None else None,\n provenScore = entry[2],\n ranking = rank,\n location = city,\n url = self.__buildProfileUrl(catData[1], entry[3], schema) if catData is not None else self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId = catId\n ))\n\n self.__companies[schema] = companies\n\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n\n for schema in schemas:\n self.__getCompaniesData(schema)\n\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n"},"steps":{"kind":"list like","value":["import psycopg2\n\nfrom .configuration import ConfigurationException\n\nDB_CONNECT_STRING = \"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\"\n\nclass DBItemCompany:\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore, ranking, location, url, categoryId):\n self.id = _id\n self.twitterAccount = tweeter\n self.category = category\n self.categoryUrl = categoryUrl\n self.provenScore = provenScore\n self.ranking = ranking\n self.location = location\n self.url = url\n self.categoryId = categoryId\n\n @property\n def invalidScore(self):\n return self.provenScore is None or self.provenScore < 1\n\n @property\n def twitter(self):\n return '@' + self.twitterAccount\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\nclass DBManager:\n def __init__(self, cfg):\n self.cfg = cfg\n\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1],\n user=dbSettings[2], passwd=dbSettings[3]\n ))\n self.cursor = self.connection.cursor()\n\n self.__loggedIn = True\n\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\".format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain=self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain=self.__getDomainName(schemaName),\n categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.__getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\".format(schema=schema))\n data = self.cursor.fetchall()\n\n companies = []\n for entry in data:\n self.cursor.execute('SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'.format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n\n if cities is None:\n continue\n\n city = ''\n\n for cityId in cities:\n self.cursor.execute('SELECT city FROM {schema}.locations_location WHERE id = {city}'.format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n\n if cityName is not None:\n city += cityName[0]\n\n self.cursor.execute('SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'.format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n\n if catId is not None:\n self.cursor.execute('SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'.format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n\n companies.append(DBItemCompany(\n _id = entry[0],\n tweeter = entry[1],\n category = catData[0] if catData is not None else None,\n categoryUrl = self.__buildCategoryUrl(catId, schema) if catId is not None else None,\n provenScore = entry[2],\n ranking = rank,\n location = city,\n url = self.__buildProfileUrl(catData[1], entry[3], schema) if catData is not None else self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId = catId\n ))\n\n self.__companies[schema] = companies\n\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n\n for schema in schemas:\n self.__getCompaniesData(schema)\n\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","import psycopg2\nfrom .configuration import ConfigurationException\nDB_CONNECT_STRING = (\n \"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\")\n\n\nclass DBItemCompany:\n\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore,\n ranking, location, url, categoryId):\n self.id = _id\n self.twitterAccount = tweeter\n self.category = category\n self.categoryUrl = categoryUrl\n self.provenScore = provenScore\n self.ranking = ranking\n self.location = location\n self.url = url\n self.categoryId = categoryId\n\n @property\n def invalidScore(self):\n return self.provenScore is None or self.provenScore < 1\n\n @property\n def twitter(self):\n return '@' + self.twitterAccount\n\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\nDB_CONNECT_STRING = (\n \"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\")\n\n\nclass DBItemCompany:\n\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore,\n ranking, location, url, categoryId):\n self.id = _id\n self.twitterAccount = tweeter\n self.category = category\n self.categoryUrl = categoryUrl\n self.provenScore = provenScore\n self.ranking = ranking\n self.location = location\n self.url = url\n self.categoryId = categoryId\n\n @property\n def invalidScore(self):\n return self.provenScore is None or self.provenScore < 1\n\n @property\n def twitter(self):\n return '@' + self.twitterAccount\n\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n\n\nclass DBItemCompany:\n\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore,\n ranking, location, url, categoryId):\n self.id = _id\n self.twitterAccount = tweeter\n self.category = category\n self.categoryUrl = categoryUrl\n self.provenScore = provenScore\n self.ranking = ranking\n self.location = location\n self.url = url\n self.categoryId = categoryId\n\n @property\n def invalidScore(self):\n return self.provenScore is None or self.provenScore < 1\n\n @property\n def twitter(self):\n return '@' + self.twitterAccount\n\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n\n\nclass DBItemCompany:\n <function token>\n\n @property\n def invalidScore(self):\n return self.provenScore is None or self.provenScore < 1\n\n @property\n def twitter(self):\n return '@' + self.twitterAccount\n\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n\n\nclass DBItemCompany:\n <function token>\n\n @property\n def invalidScore(self):\n return self.provenScore is None or self.provenScore < 1\n <function token>\n\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n\n\nclass DBItemCompany:\n <function token>\n <function token>\n <function token>\n\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n\n\nclass DBException(Exception):\n \"\"\"\n Represents a generic exception thrown by the Database Manager\n \"\"\"\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n\n\nclass DBException(Exception):\n <docstring token>\n pass\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n\n def __getCompaniesData(self, schema):\n \"\"\"\n Load Companies list from database\n \"\"\"\n try:\n self.cursor.execute(\n \"\"\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\n twitter <> ''\"\"\"\n .format(schema=schema))\n data = self.cursor.fetchall()\n companies = []\n for entry in data:\n self.cursor.execute(\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\n .format(schema=schema, vendor=entry[0]))\n cities = self.cursor.fetchall()\n if cities is None:\n continue\n city = ''\n for cityId in cities:\n self.cursor.execute(\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\n .format(schema=schema, city=cityId[0]))\n cityName = self.cursor.fetchone()\n if cityName is not None:\n city += cityName[0]\n self.cursor.execute(\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \"primary\" is true'\n .format(schema=schema, vendor=entry[0]))\n customKind = self.cursor.fetchone()\n if customKind is None:\n catId = rank = None\n else:\n catId, rank = customKind\n if catId is not None:\n self.cursor.execute(\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\n .format(schema=schema, cat=catId))\n catData = self.cursor.fetchone()\n else:\n catData = None\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\n 1], category=catData[0] if catData is not None else\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\n ) if catId is not None else None, provenScore=entry[2],\n ranking=rank, location=city, url=self.__buildProfileUrl\n (catData[1], entry[3], schema) if catData is not None else\n self.__buildProfileUrlWOCategory(entry[3], schema),\n categoryId=catId))\n self.__companies[schema] = companies\n except psycopg2.DatabaseError as err:\n raise DBException(err.args[0])\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n <function token>\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n\n def refreshData(self, schemas):\n if not self.__loggedIn:\n if not self.__logInDb():\n return False\n for schema in schemas:\n self.__getCompaniesData(schema)\n return True\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n\n def __getDomainName(self, schema):\n try:\n self.cursor.execute(\n \"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\"\n .format(schemaname=schema))\n return 'http://' + self.cursor.fetchone()[0]\n except psycopg2.DatabaseError:\n raise DBException('Failed to extract domain name from database')\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n <function token>\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n <function token>\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\n =self.__getDomainName(schemaName), categorySlug=catSlug,\n profileSlug=profSlug)\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n <function token>\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n\n def __logInDb(self):\n try:\n dbSettings = self.cfg.databaseSettings\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\n ], passwd=dbSettings[3]))\n self.cursor = self.connection.cursor()\n self.__loggedIn = True\n return True\n except (psycopg2.OperationalError, ConfigurationException):\n return False\n <function token>\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n <function token>\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n <function token>\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n <function token>\n <function token>\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n <function token>\n\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\n __getDomainName(schemaName), profileSlug=profSlug)\n <function token>\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n <function token>\n <function token>\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n <function token>\n <function token>\n <function token>\n\n def domainUrl(self, schema):\n return self.__getDomainName(schema)\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n <function token>\n <function token>\n\n def __buildCategoryUrl(self, catId, schemaName):\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\n =self.__getDomainName(schemaName), categoryId=catId)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n\n def __del__(self):\n try:\n self.connection.close()\n except psycopg2.Error:\n pass\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n @property\n def companies(self):\n return self.__companies\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n\n def __init__(self, cfg):\n self.cfg = cfg\n self.__companies = {}\n self.__loggedIn = False\n self.connection = None\n self.cursor = None\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n @property\n def isConnected(self):\n return self.__loggedIn\n","<import token>\n<assignment token>\n<class token>\n<class token>\n\n\nclass DBManager:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n","<import token>\n<assignment token>\n<class token>\n<class token>\n<class token>\n"],"string":"[\n \"import psycopg2\\n\\nfrom .configuration import ConfigurationException\\n\\nDB_CONNECT_STRING = \\\"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\\\"\\n\\nclass DBItemCompany:\\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore, ranking, location, url, categoryId):\\n self.id = _id\\n self.twitterAccount = tweeter\\n self.category = category\\n self.categoryUrl = categoryUrl\\n self.provenScore = provenScore\\n self.ranking = ranking\\n self.location = location\\n self.url = url\\n self.categoryId = categoryId\\n\\n @property\\n def invalidScore(self):\\n return self.provenScore is None or self.provenScore < 1\\n\\n @property\\n def twitter(self):\\n return '@' + self.twitterAccount\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\nclass DBManager:\\n def __init__(self, cfg):\\n self.cfg = cfg\\n\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1],\\n user=dbSettings[2], passwd=dbSettings[3]\\n ))\\n self.cursor = self.connection.cursor()\\n\\n self.__loggedIn = True\\n\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\".format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain=self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain=self.__getDomainName(schemaName),\\n categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.__getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\".format(schema=schema))\\n data = self.cursor.fetchall()\\n\\n companies = []\\n for entry in data:\\n self.cursor.execute('SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'.format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n\\n if cities is None:\\n continue\\n\\n city = ''\\n\\n for cityId in cities:\\n self.cursor.execute('SELECT city FROM {schema}.locations_location WHERE id = {city}'.format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n\\n if cityName is not None:\\n city += cityName[0]\\n\\n self.cursor.execute('SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'.format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n\\n if catId is not None:\\n self.cursor.execute('SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'.format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n\\n companies.append(DBItemCompany(\\n _id = entry[0],\\n tweeter = entry[1],\\n category = catData[0] if catData is not None else None,\\n categoryUrl = self.__buildCategoryUrl(catId, schema) if catId is not None else None,\\n provenScore = entry[2],\\n ranking = rank,\\n location = city,\\n url = self.__buildProfileUrl(catData[1], entry[3], schema) if catData is not None else self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId = catId\\n ))\\n\\n self.__companies[schema] = companies\\n\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"import psycopg2\\nfrom .configuration import ConfigurationException\\nDB_CONNECT_STRING = (\\n \\\"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\\\")\\n\\n\\nclass DBItemCompany:\\n\\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore,\\n ranking, location, url, categoryId):\\n self.id = _id\\n self.twitterAccount = tweeter\\n self.category = category\\n self.categoryUrl = categoryUrl\\n self.provenScore = provenScore\\n self.ranking = ranking\\n self.location = location\\n self.url = url\\n self.categoryId = categoryId\\n\\n @property\\n def invalidScore(self):\\n return self.provenScore is None or self.provenScore < 1\\n\\n @property\\n def twitter(self):\\n return '@' + self.twitterAccount\\n\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\nDB_CONNECT_STRING = (\\n \\\"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\\\")\\n\\n\\nclass DBItemCompany:\\n\\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore,\\n ranking, location, url, categoryId):\\n self.id = _id\\n self.twitterAccount = tweeter\\n self.category = category\\n self.categoryUrl = categoryUrl\\n self.provenScore = provenScore\\n self.ranking = ranking\\n self.location = location\\n self.url = url\\n self.categoryId = categoryId\\n\\n @property\\n def invalidScore(self):\\n return self.provenScore is None or self.provenScore < 1\\n\\n @property\\n def twitter(self):\\n return '@' + self.twitterAccount\\n\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n\\n\\nclass DBItemCompany:\\n\\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore,\\n ranking, location, url, categoryId):\\n self.id = _id\\n self.twitterAccount = tweeter\\n self.category = category\\n self.categoryUrl = categoryUrl\\n self.provenScore = provenScore\\n self.ranking = ranking\\n self.location = location\\n self.url = url\\n self.categoryId = categoryId\\n\\n @property\\n def invalidScore(self):\\n return self.provenScore is None or self.provenScore < 1\\n\\n @property\\n def twitter(self):\\n return '@' + self.twitterAccount\\n\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n\\n\\nclass DBItemCompany:\\n <function token>\\n\\n @property\\n def invalidScore(self):\\n return self.provenScore is None or self.provenScore < 1\\n\\n @property\\n def twitter(self):\\n return '@' + self.twitterAccount\\n\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n\\n\\nclass DBItemCompany:\\n <function token>\\n\\n @property\\n def invalidScore(self):\\n return self.provenScore is None or self.provenScore < 1\\n <function token>\\n\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n\\n\\nclass DBItemCompany:\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n\\n\\nclass DBException(Exception):\\n \\\"\\\"\\\"\\n Represents a generic exception thrown by the Database Manager\\n \\\"\\\"\\\"\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n\\n\\nclass DBException(Exception):\\n <docstring token>\\n pass\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n\\n def __getCompaniesData(self, schema):\\n \\\"\\\"\\\"\\n Load Companies list from database\\n \\\"\\\"\\\"\\n try:\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT id, twitter, proven_score, slug FROM {schema}.vendors_vendor WHERE\\n twitter <> ''\\\"\\\"\\\"\\n .format(schema=schema))\\n data = self.cursor.fetchall()\\n companies = []\\n for entry in data:\\n self.cursor.execute(\\n 'SELECT location_id FROM {schema}.vendors_vendorlocation WHERE vendor_id = {vendor}'\\n .format(schema=schema, vendor=entry[0]))\\n cities = self.cursor.fetchall()\\n if cities is None:\\n continue\\n city = ''\\n for cityId in cities:\\n self.cursor.execute(\\n 'SELECT city FROM {schema}.locations_location WHERE id = {city}'\\n .format(schema=schema, city=cityId[0]))\\n cityName = self.cursor.fetchone()\\n if cityName is not None:\\n city += cityName[0]\\n self.cursor.execute(\\n 'SELECT category_id, rank FROM {schema}.vendors_vendorcustomkind WHERE vendor_id = {vendor} AND \\\"primary\\\" is true'\\n .format(schema=schema, vendor=entry[0]))\\n customKind = self.cursor.fetchone()\\n if customKind is None:\\n catId = rank = None\\n else:\\n catId, rank = customKind\\n if catId is not None:\\n self.cursor.execute(\\n 'SELECT name, slug FROM {schema}.categories_category WHERE id = {cat}'\\n .format(schema=schema, cat=catId))\\n catData = self.cursor.fetchone()\\n else:\\n catData = None\\n companies.append(DBItemCompany(_id=entry[0], tweeter=entry[\\n 1], category=catData[0] if catData is not None else\\n None, categoryUrl=self.__buildCategoryUrl(catId, schema\\n ) if catId is not None else None, provenScore=entry[2],\\n ranking=rank, location=city, url=self.__buildProfileUrl\\n (catData[1], entry[3], schema) if catData is not None else\\n self.__buildProfileUrlWOCategory(entry[3], schema),\\n categoryId=catId))\\n self.__companies[schema] = companies\\n except psycopg2.DatabaseError as err:\\n raise DBException(err.args[0])\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n <function token>\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n\\n def refreshData(self, schemas):\\n if not self.__loggedIn:\\n if not self.__logInDb():\\n return False\\n for schema in schemas:\\n self.__getCompaniesData(schema)\\n return True\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n\\n def __getDomainName(self, schema):\\n try:\\n self.cursor.execute(\\n \\\"SELECT domain_url FROM customers_customer WHERE schema_name='{schemaname}'\\\"\\n .format(schemaname=schema))\\n return 'http://' + self.cursor.fetchone()[0]\\n except psycopg2.DatabaseError:\\n raise DBException('Failed to extract domain name from database')\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n <function token>\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n <function token>\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n\\n def __buildProfileUrl(self, catSlug, profSlug, schemaName):\\n return '{domain}/vendors/{categorySlug}/{profileSlug}'.format(domain\\n =self.__getDomainName(schemaName), categorySlug=catSlug,\\n profileSlug=profSlug)\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n <function token>\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n\\n def __logInDb(self):\\n try:\\n dbSettings = self.cfg.databaseSettings\\n self.connection = psycopg2.connect(DB_CONNECT_STRING.format(\\n host=dbSettings[0], dbname=dbSettings[1], user=dbSettings[2\\n ], passwd=dbSettings[3]))\\n self.cursor = self.connection.cursor()\\n self.__loggedIn = True\\n return True\\n except (psycopg2.OperationalError, ConfigurationException):\\n return False\\n <function token>\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n <function token>\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n <function token>\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n <function token>\\n <function token>\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n <function token>\\n\\n def __buildProfileUrlWOCategory(self, profSlug, schemaName):\\n return '{domain}/vendors/{profileSlug}'.format(domain=self.\\n __getDomainName(schemaName), profileSlug=profSlug)\\n <function token>\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n <function token>\\n <function token>\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n <function token>\\n <function token>\\n <function token>\\n\\n def domainUrl(self, schema):\\n return self.__getDomainName(schema)\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n <function token>\\n <function token>\\n\\n def __buildCategoryUrl(self, catId, schemaName):\\n return '{domain}/vendors/?find=category-{categoryId}'.format(domain\\n =self.__getDomainName(schemaName), categoryId=catId)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n\\n def __del__(self):\\n try:\\n self.connection.close()\\n except psycopg2.Error:\\n pass\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n @property\\n def companies(self):\\n return self.__companies\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n\\n def __init__(self, cfg):\\n self.cfg = cfg\\n self.__companies = {}\\n self.__loggedIn = False\\n self.connection = None\\n self.cursor = None\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n @property\\n def isConnected(self):\\n return self.__loggedIn\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n\\n\\nclass DBManager:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n<assignment token>\\n<class token>\\n<class token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7580,"cells":{"index":{"kind":"number","value":7580,"string":"7,580"},"blob_id":{"kind":"string","value":"b34ad8d7fc8df0ab86c5930ab2b5aa1f86d13ae3"},"code":{"kind":"string","value":"from django.db import models\n\n\nclass Author(models.Model):\n author = models.CharField(\n \"Author\",\n max_length=30,\n blank=False,\n null=False\n )\n\n biography = models.TextField(\n \"About author\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.author\n\n\nclass Series(models.Model):\n title = models.CharField(\n \"Title of series\",\n max_length=100,\n blank=False,\n null=False\n )\n\n description = models.TextField(\n \"About this series\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField(\n \"Genre\",\n max_length=50,\n blank=False,\n null=False\n )\n\n description = models.TextField(\n \"About this genre\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField(\n \"Publishing House\",\n max_length=40,\n blank=False,\n null=False\n\n )\n\n history = models.TextField(\n \"Other books of this house\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.house\n"},"steps":{"kind":"list like","value":["from django.db import models\n\n\nclass Author(models.Model):\n author = models.CharField(\n \"Author\",\n max_length=30,\n blank=False,\n null=False\n )\n\n biography = models.TextField(\n \"About author\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.author\n\n\nclass Series(models.Model):\n title = models.CharField(\n \"Title of series\",\n max_length=100,\n blank=False,\n null=False\n )\n\n description = models.TextField(\n \"About this series\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField(\n \"Genre\",\n max_length=50,\n blank=False,\n null=False\n )\n\n description = models.TextField(\n \"About this genre\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField(\n \"Publishing House\",\n max_length=40,\n blank=False,\n null=False\n\n )\n\n history = models.TextField(\n \"Other books of this house\",\n max_length=500,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.house\n","from django.db import models\n\n\nclass Author(models.Model):\n author = models.CharField('Author', max_length=30, blank=False, null=False)\n biography = models.TextField('About author', max_length=500, blank=True,\n null=True)\n\n def __str__(self):\n return self.author\n\n\nclass Series(models.Model):\n title = models.CharField('Title of series', max_length=100, blank=False,\n null=False)\n description = models.TextField('About this series', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n\n\nclass Author(models.Model):\n author = models.CharField('Author', max_length=30, blank=False, null=False)\n biography = models.TextField('About author', max_length=500, blank=True,\n null=True)\n\n def __str__(self):\n return self.author\n\n\nclass Series(models.Model):\n title = models.CharField('Title of series', max_length=100, blank=False,\n null=False)\n description = models.TextField('About this series', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n\n\nclass Author(models.Model):\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.author\n\n\nclass Series(models.Model):\n title = models.CharField('Title of series', max_length=100, blank=False,\n null=False)\n description = models.TextField('About this series', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n\n\nclass Author(models.Model):\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Series(models.Model):\n title = models.CharField('Title of series', max_length=100, blank=False,\n null=False)\n description = models.TextField('About this series', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n\n\nclass Series(models.Model):\n title = models.CharField('Title of series', max_length=100, blank=False,\n null=False)\n description = models.TextField('About this series', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n\n\nclass Series(models.Model):\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.title\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n\n\nclass Series(models.Model):\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n<class token>\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = models.TextField('About this genre', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n<class token>\n\n\nclass Genre(models.Model):\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.genre\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n<class token>\n\n\nclass Genre(models.Model):\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass PublishingHouse(models.Model):\n house = models.CharField('Publishing House', max_length=40, blank=False,\n null=False)\n history = models.TextField('Other books of this house', max_length=500,\n blank=True, null=True)\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass PublishingHouse(models.Model):\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.house\n","<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass PublishingHouse(models.Model):\n <assignment token>\n <assignment token>\n <function token>\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n"],"string":"[\n \"from django.db import models\\n\\n\\nclass Author(models.Model):\\n author = models.CharField(\\n \\\"Author\\\",\\n max_length=30,\\n blank=False,\\n null=False\\n )\\n\\n biography = models.TextField(\\n \\\"About author\\\",\\n max_length=500,\\n blank=True,\\n null=True\\n )\\n\\n def __str__(self):\\n return self.author\\n\\n\\nclass Series(models.Model):\\n title = models.CharField(\\n \\\"Title of series\\\",\\n max_length=100,\\n blank=False,\\n null=False\\n )\\n\\n description = models.TextField(\\n \\\"About this series\\\",\\n max_length=500,\\n blank=True,\\n null=True\\n )\\n\\n def __str__(self):\\n return self.title\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField(\\n \\\"Genre\\\",\\n max_length=50,\\n blank=False,\\n null=False\\n )\\n\\n description = models.TextField(\\n \\\"About this genre\\\",\\n max_length=500,\\n blank=True,\\n null=True\\n )\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField(\\n \\\"Publishing House\\\",\\n max_length=40,\\n blank=False,\\n null=False\\n\\n )\\n\\n history = models.TextField(\\n \\\"Other books of this house\\\",\\n max_length=500,\\n blank=True,\\n null=True\\n )\\n\\n def __str__(self):\\n return self.house\\n\",\n \"from django.db import models\\n\\n\\nclass Author(models.Model):\\n author = models.CharField('Author', max_length=30, blank=False, null=False)\\n biography = models.TextField('About author', max_length=500, blank=True,\\n null=True)\\n\\n def __str__(self):\\n return self.author\\n\\n\\nclass Series(models.Model):\\n title = models.CharField('Title of series', max_length=100, blank=False,\\n null=False)\\n description = models.TextField('About this series', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.title\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n\\n\\nclass Author(models.Model):\\n author = models.CharField('Author', max_length=30, blank=False, null=False)\\n biography = models.TextField('About author', max_length=500, blank=True,\\n null=True)\\n\\n def __str__(self):\\n return self.author\\n\\n\\nclass Series(models.Model):\\n title = models.CharField('Title of series', max_length=100, blank=False,\\n null=False)\\n description = models.TextField('About this series', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.title\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n\\n\\nclass Author(models.Model):\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.author\\n\\n\\nclass Series(models.Model):\\n title = models.CharField('Title of series', max_length=100, blank=False,\\n null=False)\\n description = models.TextField('About this series', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.title\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n\\n\\nclass Author(models.Model):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Series(models.Model):\\n title = models.CharField('Title of series', max_length=100, blank=False,\\n null=False)\\n description = models.TextField('About this series', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.title\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass Series(models.Model):\\n title = models.CharField('Title of series', max_length=100, blank=False,\\n null=False)\\n description = models.TextField('About this series', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.title\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass Series(models.Model):\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.title\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass Series(models.Model):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass Genre(models.Model):\\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\\n description = models.TextField('About this genre', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass Genre(models.Model):\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.genre\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass Genre(models.Model):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass PublishingHouse(models.Model):\\n house = models.CharField('Publishing House', max_length=40, blank=False,\\n null=False)\\n history = models.TextField('Other books of this house', max_length=500,\\n blank=True, null=True)\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass PublishingHouse(models.Model):\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.house\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass PublishingHouse(models.Model):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7581,"cells":{"index":{"kind":"number","value":7581,"string":"7,581"},"blob_id":{"kind":"string","value":"2cef5311a9ff9497ad6611fe7b47e4f7c5b1b3c7"},"code":{"kind":"string","value":"#!/usr/bin/python\n\nfrom random import *\n\nprob = \"change\"\n\ncases = [ \n 10,\n 10,\n 10,\n 100,\n 100,\n 100000,\n 100000,\n 100000,\n 100000,\n 100000\n ]\ncur = 0\n\nst = [1,2,5,10,20,50,100,200,500,1000,2000,5000,10000]\n\nfor (n) in cases :\n cout = \"\"\n cur += 1\n print \"make %d...\" % cur\n##-----\n#TODO generate the data\n\n tot = 0\n stt = []\n for a in st :\n for b in st :\n if b < a and a <= n :\n tot = tot + 1\n stt.append( (a,b) )\n cout += \"%d\\n\" % tot\n for (a,b) in stt :\n cout += \"%d %d\\n\" % (a, b)\n\n\n##-----\n f = file( prob + str(cur) + \".in\", \"w\" )\n f.write( cout )\n f.close()\n\n"},"steps":{"kind":"list like","value":["#!/usr/bin/python\n\nfrom random import *\n\nprob = \"change\"\n\ncases = [ \n 10,\n 10,\n 10,\n 100,\n 100,\n 100000,\n 100000,\n 100000,\n 100000,\n 100000\n ]\ncur = 0\n\nst = [1,2,5,10,20,50,100,200,500,1000,2000,5000,10000]\n\nfor (n) in cases :\n cout = \"\"\n cur += 1\n print \"make %d...\" % cur\n##-----\n#TODO generate the data\n\n tot = 0\n stt = []\n for a in st :\n for b in st :\n if b < a and a <= n :\n tot = tot + 1\n stt.append( (a,b) )\n cout += \"%d\\n\" % tot\n for (a,b) in stt :\n cout += \"%d %d\\n\" % (a, b)\n\n\n##-----\n f = file( prob + str(cur) + \".in\", \"w\" )\n f.write( cout )\n f.close()\n\n"],"string":"[\n \"#!/usr/bin/python\\n\\nfrom random import *\\n\\nprob = \\\"change\\\"\\n\\ncases = [ \\n 10,\\n 10,\\n 10,\\n 100,\\n 100,\\n 100000,\\n 100000,\\n 100000,\\n 100000,\\n 100000\\n ]\\ncur = 0\\n\\nst = [1,2,5,10,20,50,100,200,500,1000,2000,5000,10000]\\n\\nfor (n) in cases :\\n cout = \\\"\\\"\\n cur += 1\\n print \\\"make %d...\\\" % cur\\n##-----\\n#TODO generate the data\\n\\n tot = 0\\n stt = []\\n for a in st :\\n for b in st :\\n if b < a and a <= n :\\n tot = tot + 1\\n stt.append( (a,b) )\\n cout += \\\"%d\\\\n\\\" % tot\\n for (a,b) in stt :\\n cout += \\\"%d %d\\\\n\\\" % (a, b)\\n\\n\\n##-----\\n f = file( prob + str(cur) + \\\".in\\\", \\\"w\\\" )\\n f.write( cout )\\n f.close()\\n\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":7582,"cells":{"index":{"kind":"number","value":7582,"string":"7,582"},"blob_id":{"kind":"string","value":"a28ece0db9bf0d4c3ab26207216b1da45f7aaa0f"},"code":{"kind":"string","value":"\"\"\"Proper parenthetics extra credit kata.\"\"\"\n\nfrom _que_structure import Q\n\n\ndef proper_parenthetics(string):\n \"\"\"Return if parentheses are matching or not.\"\"\"\n if isinstance(string, str):\n paren_q = Q()\n for i in range(len(string)):\n paren_q.enqueue(string[i])\n opening_parens = 0\n closing_parens = 0\n while paren_q.size() > 0 and paren_q.queue.head is not None:\n i = paren_q.dequeue()\n if i != '(' and i != ')':\n raise TypeError('proper_parenthetics takes only parentheses.')\n if i == '(' and closing_parens == 0:\n opening_parens += 1\n elif i == '(' and closing_parens > 0:\n closing_parens -= 1\n elif i == ')' and opening_parens == 0:\n return -1\n elif i == ')' and opening_parens > 0:\n opening_parens -= 1\n if opening_parens - closing_parens == 0:\n return 0\n if opening_parens - closing_parens > 0:\n return 1\n raise TypeError('proper_parenthetics takes only strings')\n\n"},"steps":{"kind":"list like","value":["\"\"\"Proper parenthetics extra credit kata.\"\"\"\n\nfrom _que_structure import Q\n\n\ndef proper_parenthetics(string):\n \"\"\"Return if parentheses are matching or not.\"\"\"\n if isinstance(string, str):\n paren_q = Q()\n for i in range(len(string)):\n paren_q.enqueue(string[i])\n opening_parens = 0\n closing_parens = 0\n while paren_q.size() > 0 and paren_q.queue.head is not None:\n i = paren_q.dequeue()\n if i != '(' and i != ')':\n raise TypeError('proper_parenthetics takes only parentheses.')\n if i == '(' and closing_parens == 0:\n opening_parens += 1\n elif i == '(' and closing_parens > 0:\n closing_parens -= 1\n elif i == ')' and opening_parens == 0:\n return -1\n elif i == ')' and opening_parens > 0:\n opening_parens -= 1\n if opening_parens - closing_parens == 0:\n return 0\n if opening_parens - closing_parens > 0:\n return 1\n raise TypeError('proper_parenthetics takes only strings')\n\n","<docstring token>\nfrom _que_structure import Q\n\n\ndef proper_parenthetics(string):\n \"\"\"Return if parentheses are matching or not.\"\"\"\n if isinstance(string, str):\n paren_q = Q()\n for i in range(len(string)):\n paren_q.enqueue(string[i])\n opening_parens = 0\n closing_parens = 0\n while paren_q.size() > 0 and paren_q.queue.head is not None:\n i = paren_q.dequeue()\n if i != '(' and i != ')':\n raise TypeError('proper_parenthetics takes only parentheses.')\n if i == '(' and closing_parens == 0:\n opening_parens += 1\n elif i == '(' and closing_parens > 0:\n closing_parens -= 1\n elif i == ')' and opening_parens == 0:\n return -1\n elif i == ')' and opening_parens > 0:\n opening_parens -= 1\n if opening_parens - closing_parens == 0:\n return 0\n if opening_parens - closing_parens > 0:\n return 1\n raise TypeError('proper_parenthetics takes only strings')\n","<docstring token>\n<import token>\n\n\ndef proper_parenthetics(string):\n \"\"\"Return if parentheses are matching or not.\"\"\"\n if isinstance(string, str):\n paren_q = Q()\n for i in range(len(string)):\n paren_q.enqueue(string[i])\n opening_parens = 0\n closing_parens = 0\n while paren_q.size() > 0 and paren_q.queue.head is not None:\n i = paren_q.dequeue()\n if i != '(' and i != ')':\n raise TypeError('proper_parenthetics takes only parentheses.')\n if i == '(' and closing_parens == 0:\n opening_parens += 1\n elif i == '(' and closing_parens > 0:\n closing_parens -= 1\n elif i == ')' and opening_parens == 0:\n return -1\n elif i == ')' and opening_parens > 0:\n opening_parens -= 1\n if opening_parens - closing_parens == 0:\n return 0\n if opening_parens - closing_parens > 0:\n return 1\n raise TypeError('proper_parenthetics takes only strings')\n","<docstring token>\n<import token>\n<function token>\n"],"string":"[\n \"\\\"\\\"\\\"Proper parenthetics extra credit kata.\\\"\\\"\\\"\\n\\nfrom _que_structure import Q\\n\\n\\ndef proper_parenthetics(string):\\n \\\"\\\"\\\"Return if parentheses are matching or not.\\\"\\\"\\\"\\n if isinstance(string, str):\\n paren_q = Q()\\n for i in range(len(string)):\\n paren_q.enqueue(string[i])\\n opening_parens = 0\\n closing_parens = 0\\n while paren_q.size() > 0 and paren_q.queue.head is not None:\\n i = paren_q.dequeue()\\n if i != '(' and i != ')':\\n raise TypeError('proper_parenthetics takes only parentheses.')\\n if i == '(' and closing_parens == 0:\\n opening_parens += 1\\n elif i == '(' and closing_parens > 0:\\n closing_parens -= 1\\n elif i == ')' and opening_parens == 0:\\n return -1\\n elif i == ')' and opening_parens > 0:\\n opening_parens -= 1\\n if opening_parens - closing_parens == 0:\\n return 0\\n if opening_parens - closing_parens > 0:\\n return 1\\n raise TypeError('proper_parenthetics takes only strings')\\n\\n\",\n \"<docstring token>\\nfrom _que_structure import Q\\n\\n\\ndef proper_parenthetics(string):\\n \\\"\\\"\\\"Return if parentheses are matching or not.\\\"\\\"\\\"\\n if isinstance(string, str):\\n paren_q = Q()\\n for i in range(len(string)):\\n paren_q.enqueue(string[i])\\n opening_parens = 0\\n closing_parens = 0\\n while paren_q.size() > 0 and paren_q.queue.head is not None:\\n i = paren_q.dequeue()\\n if i != '(' and i != ')':\\n raise TypeError('proper_parenthetics takes only parentheses.')\\n if i == '(' and closing_parens == 0:\\n opening_parens += 1\\n elif i == '(' and closing_parens > 0:\\n closing_parens -= 1\\n elif i == ')' and opening_parens == 0:\\n return -1\\n elif i == ')' and opening_parens > 0:\\n opening_parens -= 1\\n if opening_parens - closing_parens == 0:\\n return 0\\n if opening_parens - closing_parens > 0:\\n return 1\\n raise TypeError('proper_parenthetics takes only strings')\\n\",\n \"<docstring token>\\n<import token>\\n\\n\\ndef proper_parenthetics(string):\\n \\\"\\\"\\\"Return if parentheses are matching or not.\\\"\\\"\\\"\\n if isinstance(string, str):\\n paren_q = Q()\\n for i in range(len(string)):\\n paren_q.enqueue(string[i])\\n opening_parens = 0\\n closing_parens = 0\\n while paren_q.size() > 0 and paren_q.queue.head is not None:\\n i = paren_q.dequeue()\\n if i != '(' and i != ')':\\n raise TypeError('proper_parenthetics takes only parentheses.')\\n if i == '(' and closing_parens == 0:\\n opening_parens += 1\\n elif i == '(' and closing_parens > 0:\\n closing_parens -= 1\\n elif i == ')' and opening_parens == 0:\\n return -1\\n elif i == ')' and opening_parens > 0:\\n opening_parens -= 1\\n if opening_parens - closing_parens == 0:\\n return 0\\n if opening_parens - closing_parens > 0:\\n return 1\\n raise TypeError('proper_parenthetics takes only strings')\\n\",\n \"<docstring token>\\n<import token>\\n<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7583,"cells":{"index":{"kind":"number","value":7583,"string":"7,583"},"blob_id":{"kind":"string","value":"170716ccaaf45db2ee974de260883a8d70513f52"},"code":{"kind":"string","value":"from django.db import models\n\n\nclass Event(models.Model):\n name = models.TextField()\n host = models.TextField(null=True)\n fields = models.TextField(null=True)\n description = models.TextField(null=True)\n date = models.TextField()\n start_time = models.TextField()\n end_time = models.TextField()\n banner_path = models.TextField(null=True)\n status = models.IntegerField()\n reg = models.IntegerField()\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n"},"steps":{"kind":"list like","value":["from django.db import models\n\n\nclass Event(models.Model):\n name = models.TextField()\n host = models.TextField(null=True)\n fields = models.TextField(null=True)\n description = models.TextField(null=True)\n date = models.TextField()\n start_time = models.TextField()\n end_time = models.TextField()\n banner_path = models.TextField(null=True)\n status = models.IntegerField()\n reg = models.IntegerField()\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n","<import token>\n\n\nclass Event(models.Model):\n name = models.TextField()\n host = models.TextField(null=True)\n fields = models.TextField(null=True)\n description = models.TextField(null=True)\n date = models.TextField()\n start_time = models.TextField()\n end_time = models.TextField()\n banner_path = models.TextField(null=True)\n status = models.IntegerField()\n reg = models.IntegerField()\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n","<import token>\n\n\nclass Event(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n","<import token>\n<class token>\n"],"string":"[\n \"from django.db import models\\n\\n\\nclass Event(models.Model):\\n name = models.TextField()\\n host = models.TextField(null=True)\\n fields = models.TextField(null=True)\\n description = models.TextField(null=True)\\n date = models.TextField()\\n start_time = models.TextField()\\n end_time = models.TextField()\\n banner_path = models.TextField(null=True)\\n status = models.IntegerField()\\n reg = models.IntegerField()\\n created_at = models.DateTimeField(auto_now_add=True)\\n updated_at = models.DateTimeField(auto_now=True)\\n\",\n \"<import token>\\n\\n\\nclass Event(models.Model):\\n name = models.TextField()\\n host = models.TextField(null=True)\\n fields = models.TextField(null=True)\\n description = models.TextField(null=True)\\n date = models.TextField()\\n start_time = models.TextField()\\n end_time = models.TextField()\\n banner_path = models.TextField(null=True)\\n status = models.IntegerField()\\n reg = models.IntegerField()\\n created_at = models.DateTimeField(auto_now_add=True)\\n updated_at = models.DateTimeField(auto_now=True)\\n\",\n \"<import token>\\n\\n\\nclass Event(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\",\n \"<import token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7584,"cells":{"index":{"kind":"number","value":7584,"string":"7,584"},"blob_id":{"kind":"string","value":"0e19d7251db3382c34ad2d38a7984b65325ecfbf"},"code":{"kind":"string","value":"from django.db import models\nfrom django.db.models.base import Model\n\n# Create your models here.\nclass Categoria(models.Model):\n categoria = models.CharField(max_length=40)\n def __str__(self):\n return self.categoria\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\n nombre = models.CharField(max_length=40)\n precio = models.IntegerField()\n stock = models.IntegerField()\n descripcion = models.CharField(max_length=40)\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\n fecha = models.DateField()\n imagen = models.ImageField(null=True, blank=True)\n\n\n\n def __str__(self):\n return self.nombre\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n \nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n \n def __ (self):\n return self.numero_sucursal\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna= models.ForeignKey(Comuna,on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n \n def __str__(self):\n return self.nombre_proveedor\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()"},"steps":{"kind":"list like","value":["from django.db import models\nfrom django.db.models.base import Model\n\n# Create your models here.\nclass Categoria(models.Model):\n categoria = models.CharField(max_length=40)\n def __str__(self):\n return self.categoria\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\n nombre = models.CharField(max_length=40)\n precio = models.IntegerField()\n stock = models.IntegerField()\n descripcion = models.CharField(max_length=40)\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\n fecha = models.DateField()\n imagen = models.ImageField(null=True, blank=True)\n\n\n\n def __str__(self):\n return self.nombre\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n \nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n \n def __ (self):\n return self.numero_sucursal\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna= models.ForeignKey(Comuna,on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n \n def __str__(self):\n return self.nombre_proveedor\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()","from django.db import models\nfrom django.db.models.base import Model\n\n\nclass Categoria(models.Model):\n categoria = models.CharField(max_length=40)\n\n def __str__(self):\n return self.categoria\n\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\n nombre = models.CharField(max_length=40)\n precio = models.IntegerField()\n stock = models.IntegerField()\n descripcion = models.CharField(max_length=40)\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\n fecha = models.DateField()\n imagen = models.ImageField(null=True, blank=True)\n\n def __str__(self):\n return self.nombre\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n\n\nclass Categoria(models.Model):\n categoria = models.CharField(max_length=40)\n\n def __str__(self):\n return self.categoria\n\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\n nombre = models.CharField(max_length=40)\n precio = models.IntegerField()\n stock = models.IntegerField()\n descripcion = models.CharField(max_length=40)\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\n fecha = models.DateField()\n imagen = models.ImageField(null=True, blank=True)\n\n def __str__(self):\n return self.nombre\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n\n\nclass Categoria(models.Model):\n <assignment token>\n\n def __str__(self):\n return self.categoria\n\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\n nombre = models.CharField(max_length=40)\n precio = models.IntegerField()\n stock = models.IntegerField()\n descripcion = models.CharField(max_length=40)\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\n fecha = models.DateField()\n imagen = models.ImageField(null=True, blank=True)\n\n def __str__(self):\n return self.nombre\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n\n\nclass Categoria(models.Model):\n <assignment token>\n <function token>\n\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\n nombre = models.CharField(max_length=40)\n precio = models.IntegerField()\n stock = models.IntegerField()\n descripcion = models.CharField(max_length=40)\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\n fecha = models.DateField()\n imagen = models.ImageField(null=True, blank=True)\n\n def __str__(self):\n return self.nombre\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\n nombre = models.CharField(max_length=40)\n precio = models.IntegerField()\n stock = models.IntegerField()\n descripcion = models.CharField(max_length=40)\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\n fecha = models.DateField()\n imagen = models.ImageField(null=True, blank=True)\n\n def __str__(self):\n return self.nombre\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n\n\nclass Producto(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.nombre\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n\n\nclass Producto(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n\n\nclass Cliente(models.Model):\n nombre = models.CharField(max_length=41)\n paterno = models.CharField(max_length=40)\n rut = models.CharField(max_length=9)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n\n\nclass Cliente(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.nombre\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n\n\nclass Cliente(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass Usuario(models.Model):\n mail = models.CharField(max_length=100)\n contraseña = models.CharField(max_length=100)\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass Usuario(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.rut\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass Usuario(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass DetalleVenta(models.Model):\n tipo_comprovante = models.CharField(max_length=100)\n serie_comprovante = models.CharField(max_length=7)\n fecha_comprovante = models.DateField(max_length=100)\n iva = models.IntegerField()\n total = models.IntegerField()\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass DetalleVenta(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.serie_comprovante\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass DetalleVenta(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Descuento(models.Model):\n codigo_descuento = models.CharField(max_length=7)\n valor_descuento = models.IntegerField()\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Descuento(models.Model):\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.codigo_descuento\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Descuento(models.Model):\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Venta(models.Model):\n descripcion = models.CharField(max_length=100)\n total_venta = models.IntegerField()\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Venta(models.Model):\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.total_venta\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Venta(models.Model):\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Sucursal(models.Model):\n direccion = models.CharField(max_length=100)\n numero_sucursal = models.IntegerField()\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Sucursal(models.Model):\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.numero_sucursal\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Sucursal(models.Model):\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Comuna(models.Model):\n direccion = models.CharField(max_length=100)\n numero_comuna = models.IntegerField()\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Comuna(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __(self):\n return self.numero_sucursal\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Comuna(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Region(models.Model):\n direccion = models.CharField(max_length=100)\n numero_region = models.IntegerField()\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Region(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.numero_region\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Region(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Pedido(models.Model):\n numero_pedido = models.IntegerField()\n fecha_pedido = models.DateField(max_length=100)\n iva = models.IntegerField()\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Pedido(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __int__(self):\n return self.numero_pedido\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Pedido(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Proveedores(models.Model):\n nombre_proveedor = models.CharField(max_length=40)\n direccion = models.CharField(max_length=40)\n telefono = models.IntegerField()\n mail = models.CharField(max_length=100)\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Proveedores(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.nombre_proveedor\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Proveedores(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Suscripcion(models.Model):\n fecha_suscripcion = models.DateField\n valor_suscripcion = models.IntegerField()\n suscrito = models.IntegerField()\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass Suscripcion(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n","<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n"],"string":"[\n \"from django.db import models\\nfrom django.db.models.base import Model\\n\\n# Create your models here.\\nclass Categoria(models.Model):\\n categoria = models.CharField(max_length=40)\\n def __str__(self):\\n return self.categoria\\n\\nclass Producto(models.Model):\\n codigo = models.CharField(max_length=40)\\n nombre = models.CharField(max_length=40)\\n precio = models.IntegerField()\\n stock = models.IntegerField()\\n descripcion = models.CharField(max_length=40)\\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\\n fecha = models.DateField()\\n imagen = models.ImageField(null=True, blank=True)\\n\\n\\n\\n def __str__(self):\\n return self.nombre\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n \\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n \\n def __ (self):\\n return self.numero_sucursal\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna= models.ForeignKey(Comuna,on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n \\n def __str__(self):\\n return self.nombre_proveedor\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\",\n \"from django.db import models\\nfrom django.db.models.base import Model\\n\\n\\nclass Categoria(models.Model):\\n categoria = models.CharField(max_length=40)\\n\\n def __str__(self):\\n return self.categoria\\n\\n\\nclass Producto(models.Model):\\n codigo = models.CharField(max_length=40)\\n nombre = models.CharField(max_length=40)\\n precio = models.IntegerField()\\n stock = models.IntegerField()\\n descripcion = models.CharField(max_length=40)\\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\\n fecha = models.DateField()\\n imagen = models.ImageField(null=True, blank=True)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n\\n\\nclass Categoria(models.Model):\\n categoria = models.CharField(max_length=40)\\n\\n def __str__(self):\\n return self.categoria\\n\\n\\nclass Producto(models.Model):\\n codigo = models.CharField(max_length=40)\\n nombre = models.CharField(max_length=40)\\n precio = models.IntegerField()\\n stock = models.IntegerField()\\n descripcion = models.CharField(max_length=40)\\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\\n fecha = models.DateField()\\n imagen = models.ImageField(null=True, blank=True)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n\\n\\nclass Categoria(models.Model):\\n <assignment token>\\n\\n def __str__(self):\\n return self.categoria\\n\\n\\nclass Producto(models.Model):\\n codigo = models.CharField(max_length=40)\\n nombre = models.CharField(max_length=40)\\n precio = models.IntegerField()\\n stock = models.IntegerField()\\n descripcion = models.CharField(max_length=40)\\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\\n fecha = models.DateField()\\n imagen = models.ImageField(null=True, blank=True)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n\\n\\nclass Categoria(models.Model):\\n <assignment token>\\n <function token>\\n\\n\\nclass Producto(models.Model):\\n codigo = models.CharField(max_length=40)\\n nombre = models.CharField(max_length=40)\\n precio = models.IntegerField()\\n stock = models.IntegerField()\\n descripcion = models.CharField(max_length=40)\\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\\n fecha = models.DateField()\\n imagen = models.ImageField(null=True, blank=True)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass Producto(models.Model):\\n codigo = models.CharField(max_length=40)\\n nombre = models.CharField(max_length=40)\\n precio = models.IntegerField()\\n stock = models.IntegerField()\\n descripcion = models.CharField(max_length=40)\\n categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE)\\n fecha = models.DateField()\\n imagen = models.ImageField(null=True, blank=True)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass Producto(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass Producto(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass Cliente(models.Model):\\n nombre = models.CharField(max_length=41)\\n paterno = models.CharField(max_length=40)\\n rut = models.CharField(max_length=9)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass Cliente(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.nombre\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass Cliente(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Usuario(models.Model):\\n mail = models.CharField(max_length=100)\\n contraseña = models.CharField(max_length=100)\\n rut = models.ForeignKey(Cliente, on_delete=models.CASCADE)\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Usuario(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.rut\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Usuario(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass DetalleVenta(models.Model):\\n tipo_comprovante = models.CharField(max_length=100)\\n serie_comprovante = models.CharField(max_length=7)\\n fecha_comprovante = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n total = models.IntegerField()\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass DetalleVenta(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.serie_comprovante\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass DetalleVenta(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Descuento(models.Model):\\n codigo_descuento = models.CharField(max_length=7)\\n valor_descuento = models.IntegerField()\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Descuento(models.Model):\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.codigo_descuento\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Descuento(models.Model):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Venta(models.Model):\\n descripcion = models.CharField(max_length=100)\\n total_venta = models.IntegerField()\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Venta(models.Model):\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.total_venta\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Venta(models.Model):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Sucursal(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_sucursal = models.IntegerField()\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Sucursal(models.Model):\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.numero_sucursal\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Sucursal(models.Model):\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Comuna(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_comuna = models.IntegerField()\\n numero_sucursal = models.ForeignKey(Sucursal, on_delete=models.DO_NOTHING)\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Comuna(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __(self):\\n return self.numero_sucursal\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Comuna(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Region(models.Model):\\n direccion = models.CharField(max_length=100)\\n numero_region = models.IntegerField()\\n numero_comuna = models.ForeignKey(Comuna, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Region(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.numero_region\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Region(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Pedido(models.Model):\\n numero_pedido = models.IntegerField()\\n fecha_pedido = models.DateField(max_length=100)\\n iva = models.IntegerField()\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Pedido(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __int__(self):\\n return self.numero_pedido\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Pedido(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Proveedores(models.Model):\\n nombre_proveedor = models.CharField(max_length=40)\\n direccion = models.CharField(max_length=40)\\n telefono = models.IntegerField()\\n mail = models.CharField(max_length=100)\\n numero_pedido = models.ForeignKey(Pedido, on_delete=models.DO_NOTHING)\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Proveedores(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\\n def __str__(self):\\n return self.nombre_proveedor\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Proveedores(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Suscripcion(models.Model):\\n fecha_suscripcion = models.DateField\\n valor_suscripcion = models.IntegerField()\\n suscrito = models.IntegerField()\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\\n\\nclass Suscripcion(models.Model):\\n <assignment token>\\n <assignment token>\\n <assignment token>\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7585,"cells":{"index":{"kind":"number","value":7585,"string":"7,585"},"blob_id":{"kind":"string","value":"ba289bcdc0aa7c2ad70dba7fac541900d0b55387"},"code":{"kind":"string","value":"import os\n# Set a single thread per process for numpy with MKL/BLAS\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom copy import deepcopy\n\nfrom kadal.optim_tools.MOBO import MOBO\nfrom kadal.surrogate_models.kriging_model import Kriging\nfrom kadal.surrogate_models.supports.initinfo import initkriginfo\nfrom kadal.testcase.analyticalfcn.cases import evaluate\nfrom kadal.misc.sampling.samplingplan import sampling\n\n\ndef generate_kriging():\n # Sampling\n nsample = 20\n nvar = 2\n nobj = 2\n lb = -1 * np.ones(shape=[nvar])\n ub = 1 * np.ones(shape=[nvar])\n sampoption = \"halton\"\n samplenorm, sample = sampling(sampoption, nvar, nsample, result=\"real\",\n upbound=ub, lobound=lb)\n X = sample\n # Evaluate sample\n global y\n y = evaluate(X, \"schaffer\")\n\n # Initialize KrigInfo\n KrigInfo1 = initkriginfo()\n # Set KrigInfo\n KrigInfo1[\"X\"] = X\n KrigInfo1[\"y\"] = y[:, 0].reshape(-1, 1)\n KrigInfo1[\"problem\"] = \"schaffer\"\n KrigInfo1[\"nrestart\"] = 5\n KrigInfo1[\"ub\"] = ub\n KrigInfo1[\"lb\"] = lb\n KrigInfo1[\"optimizer\"] = \"lbfgsb\"\n\n # Initialize KrigInfo\n KrigInfo2 = deepcopy(KrigInfo1)\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\n\n # Run Kriging\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj1.train(n_cpu=n_cpu)\n loocverr1, _ = krigobj1.loocvcalc()\n print(\"LOOCV error of Kriging model: \", loocverr1, \"%\")\n\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj2.train(n_cpu=n_cpu)\n loocverr2, _ = krigobj2.loocvcalc()\n print(\"LOOCV error of Kriging model: \", loocverr2, \"%\")\n\n return krigobj1, krigobj2\n\n\ndef runopt(krigobj1, krigobj2):\n moboInfo = dict()\n moboInfo[\"nup\"] = 3\n moboInfo[\"nrestart\"] = 10\n moboInfo[\"acquifunc\"] = \"ehvi\"\n moboInfo[\"acquifuncopt\"] = \"lbfgsb\"\n\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5)\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\n\n return xupdate, yupdate, metricall\n\n\nif __name__ == '__main__':\n krigobj1, krigobj2 = generate_kriging()\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\n\n print(metricall)\n plt.scatter(y[:, 0], y[:, 1])\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\n plt.show()\n"},"steps":{"kind":"list like","value":["import os\n# Set a single thread per process for numpy with MKL/BLAS\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom copy import deepcopy\n\nfrom kadal.optim_tools.MOBO import MOBO\nfrom kadal.surrogate_models.kriging_model import Kriging\nfrom kadal.surrogate_models.supports.initinfo import initkriginfo\nfrom kadal.testcase.analyticalfcn.cases import evaluate\nfrom kadal.misc.sampling.samplingplan import sampling\n\n\ndef generate_kriging():\n # Sampling\n nsample = 20\n nvar = 2\n nobj = 2\n lb = -1 * np.ones(shape=[nvar])\n ub = 1 * np.ones(shape=[nvar])\n sampoption = \"halton\"\n samplenorm, sample = sampling(sampoption, nvar, nsample, result=\"real\",\n upbound=ub, lobound=lb)\n X = sample\n # Evaluate sample\n global y\n y = evaluate(X, \"schaffer\")\n\n # Initialize KrigInfo\n KrigInfo1 = initkriginfo()\n # Set KrigInfo\n KrigInfo1[\"X\"] = X\n KrigInfo1[\"y\"] = y[:, 0].reshape(-1, 1)\n KrigInfo1[\"problem\"] = \"schaffer\"\n KrigInfo1[\"nrestart\"] = 5\n KrigInfo1[\"ub\"] = ub\n KrigInfo1[\"lb\"] = lb\n KrigInfo1[\"optimizer\"] = \"lbfgsb\"\n\n # Initialize KrigInfo\n KrigInfo2 = deepcopy(KrigInfo1)\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\n\n # Run Kriging\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj1.train(n_cpu=n_cpu)\n loocverr1, _ = krigobj1.loocvcalc()\n print(\"LOOCV error of Kriging model: \", loocverr1, \"%\")\n\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj2.train(n_cpu=n_cpu)\n loocverr2, _ = krigobj2.loocvcalc()\n print(\"LOOCV error of Kriging model: \", loocverr2, \"%\")\n\n return krigobj1, krigobj2\n\n\ndef runopt(krigobj1, krigobj2):\n moboInfo = dict()\n moboInfo[\"nup\"] = 3\n moboInfo[\"nrestart\"] = 10\n moboInfo[\"acquifunc\"] = \"ehvi\"\n moboInfo[\"acquifuncopt\"] = \"lbfgsb\"\n\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5)\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\n\n return xupdate, yupdate, metricall\n\n\nif __name__ == '__main__':\n krigobj1, krigobj2 = generate_kriging()\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\n\n print(metricall)\n plt.scatter(y[:, 0], y[:, 1])\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\n plt.show()\n","import os\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom copy import deepcopy\nfrom kadal.optim_tools.MOBO import MOBO\nfrom kadal.surrogate_models.kriging_model import Kriging\nfrom kadal.surrogate_models.supports.initinfo import initkriginfo\nfrom kadal.testcase.analyticalfcn.cases import evaluate\nfrom kadal.misc.sampling.samplingplan import sampling\n\n\ndef generate_kriging():\n nsample = 20\n nvar = 2\n nobj = 2\n lb = -1 * np.ones(shape=[nvar])\n ub = 1 * np.ones(shape=[nvar])\n sampoption = 'halton'\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\n upbound=ub, lobound=lb)\n X = sample\n global y\n y = evaluate(X, 'schaffer')\n KrigInfo1 = initkriginfo()\n KrigInfo1['X'] = X\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\n KrigInfo1['problem'] = 'schaffer'\n KrigInfo1['nrestart'] = 5\n KrigInfo1['ub'] = ub\n KrigInfo1['lb'] = lb\n KrigInfo1['optimizer'] = 'lbfgsb'\n KrigInfo2 = deepcopy(KrigInfo1)\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj1.train(n_cpu=n_cpu)\n loocverr1, _ = krigobj1.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr1, '%')\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj2.train(n_cpu=n_cpu)\n loocverr2, _ = krigobj2.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr2, '%')\n return krigobj1, krigobj2\n\n\ndef runopt(krigobj1, krigobj2):\n moboInfo = dict()\n moboInfo['nup'] = 3\n moboInfo['nrestart'] = 10\n moboInfo['acquifunc'] = 'ehvi'\n moboInfo['acquifuncopt'] = 'lbfgsb'\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\n )\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\n return xupdate, yupdate, metricall\n\n\nif __name__ == '__main__':\n krigobj1, krigobj2 = generate_kriging()\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\n print(metricall)\n plt.scatter(y[:, 0], y[:, 1])\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\n plt.show()\n","<import token>\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\n<import token>\n\n\ndef generate_kriging():\n nsample = 20\n nvar = 2\n nobj = 2\n lb = -1 * np.ones(shape=[nvar])\n ub = 1 * np.ones(shape=[nvar])\n sampoption = 'halton'\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\n upbound=ub, lobound=lb)\n X = sample\n global y\n y = evaluate(X, 'schaffer')\n KrigInfo1 = initkriginfo()\n KrigInfo1['X'] = X\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\n KrigInfo1['problem'] = 'schaffer'\n KrigInfo1['nrestart'] = 5\n KrigInfo1['ub'] = ub\n KrigInfo1['lb'] = lb\n KrigInfo1['optimizer'] = 'lbfgsb'\n KrigInfo2 = deepcopy(KrigInfo1)\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj1.train(n_cpu=n_cpu)\n loocverr1, _ = krigobj1.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr1, '%')\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj2.train(n_cpu=n_cpu)\n loocverr2, _ = krigobj2.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr2, '%')\n return krigobj1, krigobj2\n\n\ndef runopt(krigobj1, krigobj2):\n moboInfo = dict()\n moboInfo['nup'] = 3\n moboInfo['nrestart'] = 10\n moboInfo['acquifunc'] = 'ehvi'\n moboInfo['acquifuncopt'] = 'lbfgsb'\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\n )\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\n return xupdate, yupdate, metricall\n\n\nif __name__ == '__main__':\n krigobj1, krigobj2 = generate_kriging()\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\n print(metricall)\n plt.scatter(y[:, 0], y[:, 1])\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\n plt.show()\n","<import token>\n<assignment token>\n<import token>\n\n\ndef generate_kriging():\n nsample = 20\n nvar = 2\n nobj = 2\n lb = -1 * np.ones(shape=[nvar])\n ub = 1 * np.ones(shape=[nvar])\n sampoption = 'halton'\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\n upbound=ub, lobound=lb)\n X = sample\n global y\n y = evaluate(X, 'schaffer')\n KrigInfo1 = initkriginfo()\n KrigInfo1['X'] = X\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\n KrigInfo1['problem'] = 'schaffer'\n KrigInfo1['nrestart'] = 5\n KrigInfo1['ub'] = ub\n KrigInfo1['lb'] = lb\n KrigInfo1['optimizer'] = 'lbfgsb'\n KrigInfo2 = deepcopy(KrigInfo1)\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj1.train(n_cpu=n_cpu)\n loocverr1, _ = krigobj1.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr1, '%')\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj2.train(n_cpu=n_cpu)\n loocverr2, _ = krigobj2.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr2, '%')\n return krigobj1, krigobj2\n\n\ndef runopt(krigobj1, krigobj2):\n moboInfo = dict()\n moboInfo['nup'] = 3\n moboInfo['nrestart'] = 10\n moboInfo['acquifunc'] = 'ehvi'\n moboInfo['acquifuncopt'] = 'lbfgsb'\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\n )\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\n return xupdate, yupdate, metricall\n\n\nif __name__ == '__main__':\n krigobj1, krigobj2 = generate_kriging()\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\n print(metricall)\n plt.scatter(y[:, 0], y[:, 1])\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\n plt.show()\n","<import token>\n<assignment token>\n<import token>\n\n\ndef generate_kriging():\n nsample = 20\n nvar = 2\n nobj = 2\n lb = -1 * np.ones(shape=[nvar])\n ub = 1 * np.ones(shape=[nvar])\n sampoption = 'halton'\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\n upbound=ub, lobound=lb)\n X = sample\n global y\n y = evaluate(X, 'schaffer')\n KrigInfo1 = initkriginfo()\n KrigInfo1['X'] = X\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\n KrigInfo1['problem'] = 'schaffer'\n KrigInfo1['nrestart'] = 5\n KrigInfo1['ub'] = ub\n KrigInfo1['lb'] = lb\n KrigInfo1['optimizer'] = 'lbfgsb'\n KrigInfo2 = deepcopy(KrigInfo1)\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj1.train(n_cpu=n_cpu)\n loocverr1, _ = krigobj1.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr1, '%')\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\n normy=False, trainvar=False)\n krigobj2.train(n_cpu=n_cpu)\n loocverr2, _ = krigobj2.loocvcalc()\n print('LOOCV error of Kriging model: ', loocverr2, '%')\n return krigobj1, krigobj2\n\n\ndef runopt(krigobj1, krigobj2):\n moboInfo = dict()\n moboInfo['nup'] = 3\n moboInfo['nrestart'] = 10\n moboInfo['acquifunc'] = 'ehvi'\n moboInfo['acquifuncopt'] = 'lbfgsb'\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\n )\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\n return xupdate, yupdate, metricall\n\n\n<code token>\n","<import token>\n<assignment token>\n<import token>\n<function token>\n\n\ndef runopt(krigobj1, krigobj2):\n moboInfo = dict()\n moboInfo['nup'] = 3\n moboInfo['nrestart'] = 10\n moboInfo['acquifunc'] = 'ehvi'\n moboInfo['acquifuncopt'] = 'lbfgsb'\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\n )\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\n return xupdate, yupdate, metricall\n\n\n<code token>\n","<import token>\n<assignment token>\n<import token>\n<function token>\n<function token>\n<code token>\n"],"string":"[\n \"import os\\n# Set a single thread per process for numpy with MKL/BLAS\\nos.environ['MKL_NUM_THREADS'] = '1'\\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\\n\\nimport numpy as np\\nfrom matplotlib import pyplot as plt\\nfrom copy import deepcopy\\n\\nfrom kadal.optim_tools.MOBO import MOBO\\nfrom kadal.surrogate_models.kriging_model import Kriging\\nfrom kadal.surrogate_models.supports.initinfo import initkriginfo\\nfrom kadal.testcase.analyticalfcn.cases import evaluate\\nfrom kadal.misc.sampling.samplingplan import sampling\\n\\n\\ndef generate_kriging():\\n # Sampling\\n nsample = 20\\n nvar = 2\\n nobj = 2\\n lb = -1 * np.ones(shape=[nvar])\\n ub = 1 * np.ones(shape=[nvar])\\n sampoption = \\\"halton\\\"\\n samplenorm, sample = sampling(sampoption, nvar, nsample, result=\\\"real\\\",\\n upbound=ub, lobound=lb)\\n X = sample\\n # Evaluate sample\\n global y\\n y = evaluate(X, \\\"schaffer\\\")\\n\\n # Initialize KrigInfo\\n KrigInfo1 = initkriginfo()\\n # Set KrigInfo\\n KrigInfo1[\\\"X\\\"] = X\\n KrigInfo1[\\\"y\\\"] = y[:, 0].reshape(-1, 1)\\n KrigInfo1[\\\"problem\\\"] = \\\"schaffer\\\"\\n KrigInfo1[\\\"nrestart\\\"] = 5\\n KrigInfo1[\\\"ub\\\"] = ub\\n KrigInfo1[\\\"lb\\\"] = lb\\n KrigInfo1[\\\"optimizer\\\"] = \\\"lbfgsb\\\"\\n\\n # Initialize KrigInfo\\n KrigInfo2 = deepcopy(KrigInfo1)\\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\\n\\n # Run Kriging\\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj1.train(n_cpu=n_cpu)\\n loocverr1, _ = krigobj1.loocvcalc()\\n print(\\\"LOOCV error of Kriging model: \\\", loocverr1, \\\"%\\\")\\n\\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj2.train(n_cpu=n_cpu)\\n loocverr2, _ = krigobj2.loocvcalc()\\n print(\\\"LOOCV error of Kriging model: \\\", loocverr2, \\\"%\\\")\\n\\n return krigobj1, krigobj2\\n\\n\\ndef runopt(krigobj1, krigobj2):\\n moboInfo = dict()\\n moboInfo[\\\"nup\\\"] = 3\\n moboInfo[\\\"nrestart\\\"] = 10\\n moboInfo[\\\"acquifunc\\\"] = \\\"ehvi\\\"\\n moboInfo[\\\"acquifuncopt\\\"] = \\\"lbfgsb\\\"\\n\\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5)\\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\\n\\n return xupdate, yupdate, metricall\\n\\n\\nif __name__ == '__main__':\\n krigobj1, krigobj2 = generate_kriging()\\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\\n\\n print(metricall)\\n plt.scatter(y[:, 0], y[:, 1])\\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\\n plt.show()\\n\",\n \"import os\\nos.environ['MKL_NUM_THREADS'] = '1'\\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\\nimport numpy as np\\nfrom matplotlib import pyplot as plt\\nfrom copy import deepcopy\\nfrom kadal.optim_tools.MOBO import MOBO\\nfrom kadal.surrogate_models.kriging_model import Kriging\\nfrom kadal.surrogate_models.supports.initinfo import initkriginfo\\nfrom kadal.testcase.analyticalfcn.cases import evaluate\\nfrom kadal.misc.sampling.samplingplan import sampling\\n\\n\\ndef generate_kriging():\\n nsample = 20\\n nvar = 2\\n nobj = 2\\n lb = -1 * np.ones(shape=[nvar])\\n ub = 1 * np.ones(shape=[nvar])\\n sampoption = 'halton'\\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\\n upbound=ub, lobound=lb)\\n X = sample\\n global y\\n y = evaluate(X, 'schaffer')\\n KrigInfo1 = initkriginfo()\\n KrigInfo1['X'] = X\\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\\n KrigInfo1['problem'] = 'schaffer'\\n KrigInfo1['nrestart'] = 5\\n KrigInfo1['ub'] = ub\\n KrigInfo1['lb'] = lb\\n KrigInfo1['optimizer'] = 'lbfgsb'\\n KrigInfo2 = deepcopy(KrigInfo1)\\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj1.train(n_cpu=n_cpu)\\n loocverr1, _ = krigobj1.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr1, '%')\\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj2.train(n_cpu=n_cpu)\\n loocverr2, _ = krigobj2.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr2, '%')\\n return krigobj1, krigobj2\\n\\n\\ndef runopt(krigobj1, krigobj2):\\n moboInfo = dict()\\n moboInfo['nup'] = 3\\n moboInfo['nrestart'] = 10\\n moboInfo['acquifunc'] = 'ehvi'\\n moboInfo['acquifuncopt'] = 'lbfgsb'\\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\\n )\\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\\n return xupdate, yupdate, metricall\\n\\n\\nif __name__ == '__main__':\\n krigobj1, krigobj2 = generate_kriging()\\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\\n print(metricall)\\n plt.scatter(y[:, 0], y[:, 1])\\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\\n plt.show()\\n\",\n \"<import token>\\nos.environ['MKL_NUM_THREADS'] = '1'\\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\\n<import token>\\n\\n\\ndef generate_kriging():\\n nsample = 20\\n nvar = 2\\n nobj = 2\\n lb = -1 * np.ones(shape=[nvar])\\n ub = 1 * np.ones(shape=[nvar])\\n sampoption = 'halton'\\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\\n upbound=ub, lobound=lb)\\n X = sample\\n global y\\n y = evaluate(X, 'schaffer')\\n KrigInfo1 = initkriginfo()\\n KrigInfo1['X'] = X\\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\\n KrigInfo1['problem'] = 'schaffer'\\n KrigInfo1['nrestart'] = 5\\n KrigInfo1['ub'] = ub\\n KrigInfo1['lb'] = lb\\n KrigInfo1['optimizer'] = 'lbfgsb'\\n KrigInfo2 = deepcopy(KrigInfo1)\\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj1.train(n_cpu=n_cpu)\\n loocverr1, _ = krigobj1.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr1, '%')\\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj2.train(n_cpu=n_cpu)\\n loocverr2, _ = krigobj2.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr2, '%')\\n return krigobj1, krigobj2\\n\\n\\ndef runopt(krigobj1, krigobj2):\\n moboInfo = dict()\\n moboInfo['nup'] = 3\\n moboInfo['nrestart'] = 10\\n moboInfo['acquifunc'] = 'ehvi'\\n moboInfo['acquifuncopt'] = 'lbfgsb'\\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\\n )\\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\\n return xupdate, yupdate, metricall\\n\\n\\nif __name__ == '__main__':\\n krigobj1, krigobj2 = generate_kriging()\\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\\n print(metricall)\\n plt.scatter(y[:, 0], y[:, 1])\\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\\n plt.show()\\n\",\n \"<import token>\\n<assignment token>\\n<import token>\\n\\n\\ndef generate_kriging():\\n nsample = 20\\n nvar = 2\\n nobj = 2\\n lb = -1 * np.ones(shape=[nvar])\\n ub = 1 * np.ones(shape=[nvar])\\n sampoption = 'halton'\\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\\n upbound=ub, lobound=lb)\\n X = sample\\n global y\\n y = evaluate(X, 'schaffer')\\n KrigInfo1 = initkriginfo()\\n KrigInfo1['X'] = X\\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\\n KrigInfo1['problem'] = 'schaffer'\\n KrigInfo1['nrestart'] = 5\\n KrigInfo1['ub'] = ub\\n KrigInfo1['lb'] = lb\\n KrigInfo1['optimizer'] = 'lbfgsb'\\n KrigInfo2 = deepcopy(KrigInfo1)\\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj1.train(n_cpu=n_cpu)\\n loocverr1, _ = krigobj1.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr1, '%')\\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj2.train(n_cpu=n_cpu)\\n loocverr2, _ = krigobj2.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr2, '%')\\n return krigobj1, krigobj2\\n\\n\\ndef runopt(krigobj1, krigobj2):\\n moboInfo = dict()\\n moboInfo['nup'] = 3\\n moboInfo['nrestart'] = 10\\n moboInfo['acquifunc'] = 'ehvi'\\n moboInfo['acquifuncopt'] = 'lbfgsb'\\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\\n )\\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\\n return xupdate, yupdate, metricall\\n\\n\\nif __name__ == '__main__':\\n krigobj1, krigobj2 = generate_kriging()\\n xupdate, yupdate, metricall = runopt(krigobj1, krigobj2)\\n print(metricall)\\n plt.scatter(y[:, 0], y[:, 1])\\n plt.scatter(yupdate[:, 0], yupdate[:, 1])\\n plt.show()\\n\",\n \"<import token>\\n<assignment token>\\n<import token>\\n\\n\\ndef generate_kriging():\\n nsample = 20\\n nvar = 2\\n nobj = 2\\n lb = -1 * np.ones(shape=[nvar])\\n ub = 1 * np.ones(shape=[nvar])\\n sampoption = 'halton'\\n samplenorm, sample = sampling(sampoption, nvar, nsample, result='real',\\n upbound=ub, lobound=lb)\\n X = sample\\n global y\\n y = evaluate(X, 'schaffer')\\n KrigInfo1 = initkriginfo()\\n KrigInfo1['X'] = X\\n KrigInfo1['y'] = y[:, 0].reshape(-1, 1)\\n KrigInfo1['problem'] = 'schaffer'\\n KrigInfo1['nrestart'] = 5\\n KrigInfo1['ub'] = ub\\n KrigInfo1['lb'] = lb\\n KrigInfo1['optimizer'] = 'lbfgsb'\\n KrigInfo2 = deepcopy(KrigInfo1)\\n KrigInfo2['y'] = y[:, 1].reshape(-1, 1)\\n krigobj1 = Kriging(KrigInfo1, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj1.train(n_cpu=n_cpu)\\n loocverr1, _ = krigobj1.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr1, '%')\\n krigobj2 = Kriging(KrigInfo2, standardization=True, standtype='default',\\n normy=False, trainvar=False)\\n krigobj2.train(n_cpu=n_cpu)\\n loocverr2, _ = krigobj2.loocvcalc()\\n print('LOOCV error of Kriging model: ', loocverr2, '%')\\n return krigobj1, krigobj2\\n\\n\\ndef runopt(krigobj1, krigobj2):\\n moboInfo = dict()\\n moboInfo['nup'] = 3\\n moboInfo['nrestart'] = 10\\n moboInfo['acquifunc'] = 'ehvi'\\n moboInfo['acquifuncopt'] = 'lbfgsb'\\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\\n )\\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\\n return xupdate, yupdate, metricall\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<assignment token>\\n<import token>\\n<function token>\\n\\n\\ndef runopt(krigobj1, krigobj2):\\n moboInfo = dict()\\n moboInfo['nup'] = 3\\n moboInfo['nrestart'] = 10\\n moboInfo['acquifunc'] = 'ehvi'\\n moboInfo['acquifuncopt'] = 'lbfgsb'\\n Optim = MOBO(moboInfo, [krigobj1, krigobj2], autoupdate=True, multiupdate=5\\n )\\n xupdate, yupdate, supdate, metricall = Optim.run(disp=True)\\n return xupdate, yupdate, metricall\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<assignment token>\\n<import token>\\n<function token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7586,"cells":{"index":{"kind":"number","value":7586,"string":"7,586"},"blob_id":{"kind":"string","value":"fa3ab879541c04e278317b11dd79e6e1b4319536"},"code":{"kind":"string","value":"FILE = \"Luke\"\n\nNAME = \"Luke Walker\"\n\nNATIONALITY = \"American\"\n\nCLASS = \"Manipulator\"\n\nWEAPON = \"\"\n\nBIRTH = \"\"\n\nBIRTH_LOCATION = \"\"\n\nLETTER = \"W\"\n\nRECRUITMENT_ORDER = 10\n\nSUMMARY = \"\"\n\nABILITIES = \"\"\n\nBACKSTORY = \"\"\n\nHIGHLIGHTS = \"\"\n\nSUMMONS = (\"Tonberry\", \"Grimnir\", \"Griever\", \"Starlet\")\n"},"steps":{"kind":"list like","value":["FILE = \"Luke\"\n\nNAME = \"Luke Walker\"\n\nNATIONALITY = \"American\"\n\nCLASS = \"Manipulator\"\n\nWEAPON = \"\"\n\nBIRTH = \"\"\n\nBIRTH_LOCATION = \"\"\n\nLETTER = \"W\"\n\nRECRUITMENT_ORDER = 10\n\nSUMMARY = \"\"\n\nABILITIES = \"\"\n\nBACKSTORY = \"\"\n\nHIGHLIGHTS = \"\"\n\nSUMMONS = (\"Tonberry\", \"Grimnir\", \"Griever\", \"Starlet\")\n","FILE = 'Luke'\nNAME = 'Luke Walker'\nNATIONALITY = 'American'\nCLASS = 'Manipulator'\nWEAPON = ''\nBIRTH = ''\nBIRTH_LOCATION = ''\nLETTER = 'W'\nRECRUITMENT_ORDER = 10\nSUMMARY = ''\nABILITIES = ''\nBACKSTORY = ''\nHIGHLIGHTS = ''\nSUMMONS = 'Tonberry', 'Grimnir', 'Griever', 'Starlet'\n","<assignment token>\n"],"string":"[\n \"FILE = \\\"Luke\\\"\\n\\nNAME = \\\"Luke Walker\\\"\\n\\nNATIONALITY = \\\"American\\\"\\n\\nCLASS = \\\"Manipulator\\\"\\n\\nWEAPON = \\\"\\\"\\n\\nBIRTH = \\\"\\\"\\n\\nBIRTH_LOCATION = \\\"\\\"\\n\\nLETTER = \\\"W\\\"\\n\\nRECRUITMENT_ORDER = 10\\n\\nSUMMARY = \\\"\\\"\\n\\nABILITIES = \\\"\\\"\\n\\nBACKSTORY = \\\"\\\"\\n\\nHIGHLIGHTS = \\\"\\\"\\n\\nSUMMONS = (\\\"Tonberry\\\", \\\"Grimnir\\\", \\\"Griever\\\", \\\"Starlet\\\")\\n\",\n \"FILE = 'Luke'\\nNAME = 'Luke Walker'\\nNATIONALITY = 'American'\\nCLASS = 'Manipulator'\\nWEAPON = ''\\nBIRTH = ''\\nBIRTH_LOCATION = ''\\nLETTER = 'W'\\nRECRUITMENT_ORDER = 10\\nSUMMARY = ''\\nABILITIES = ''\\nBACKSTORY = ''\\nHIGHLIGHTS = ''\\nSUMMONS = 'Tonberry', 'Grimnir', 'Griever', 'Starlet'\\n\",\n \"<assignment token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7587,"cells":{"index":{"kind":"number","value":7587,"string":"7,587"},"blob_id":{"kind":"string","value":"1574f034ff9b6ddb785e4c54758b2057009198ed"},"code":{"kind":"string","value":"alias_macro = {\r\n\r\n \"class\": \"Application\",\r\n \"method\": \"alias_macro\",\r\n \"doc\": \"\"\"\r\n Returns or modifies the macro of a command alias.\r\n \"\"\",\r\n\r\n \"syntax\": \"\"\"\r\n Rhino.AliasMacro (strAlias [, strMacro])\r\n \"\"\",\r\n\r\n \"params\": {\r\n 0: {\r\n \"name\": \"alias\",\r\n \"optional\": False,\r\n \"type_vb\": \"string\",\r\n \"type_string\": \"str\",\r\n \"doc\": \"\"\"\r\n The name of an existing command alias.\r\n \"\"\"\r\n },\r\n 1: {\r\n \"name\": \"macro\",\r\n \"optional\": True,\r\n \"type_vb\": \"string\",\r\n \"type_string\": \"str\",\r\n \"doc\": \"\"\"\r\n The new macro to run when the alias is executed.\r\n \"\"\"\r\n },\r\n },\r\n\r\n \"returns\": {\r\n 0: {\r\n \"type_vb\": \"String\",\r\n \"doc\": \"If a new macro is not specified, the existing macro if successful.\"\r\n },\r\n 1: {\r\n \"type_vb\": \"String\",\r\n \"doc\": \"If a new macro is specified, the previous macro if successful.\"\r\n },\r\n 2: {\r\n \"type_vb\": \"Null\",\r\n \"doc\": \"If not successful, or on error.\"\r\n },\r\n }\r\n\r\n}\r\n\r\n"},"steps":{"kind":"list like","value":["alias_macro = {\r\n\r\n \"class\": \"Application\",\r\n \"method\": \"alias_macro\",\r\n \"doc\": \"\"\"\r\n Returns or modifies the macro of a command alias.\r\n \"\"\",\r\n\r\n \"syntax\": \"\"\"\r\n Rhino.AliasMacro (strAlias [, strMacro])\r\n \"\"\",\r\n\r\n \"params\": {\r\n 0: {\r\n \"name\": \"alias\",\r\n \"optional\": False,\r\n \"type_vb\": \"string\",\r\n \"type_string\": \"str\",\r\n \"doc\": \"\"\"\r\n The name of an existing command alias.\r\n \"\"\"\r\n },\r\n 1: {\r\n \"name\": \"macro\",\r\n \"optional\": True,\r\n \"type_vb\": \"string\",\r\n \"type_string\": \"str\",\r\n \"doc\": \"\"\"\r\n The new macro to run when the alias is executed.\r\n \"\"\"\r\n },\r\n },\r\n\r\n \"returns\": {\r\n 0: {\r\n \"type_vb\": \"String\",\r\n \"doc\": \"If a new macro is not specified, the existing macro if successful.\"\r\n },\r\n 1: {\r\n \"type_vb\": \"String\",\r\n \"doc\": \"If a new macro is specified, the previous macro if successful.\"\r\n },\r\n 2: {\r\n \"type_vb\": \"Null\",\r\n \"doc\": \"If not successful, or on error.\"\r\n },\r\n }\r\n\r\n}\r\n\r\n","alias_macro = {'class': 'Application', 'method': 'alias_macro', 'doc':\n \"\"\"\n Returns or modifies the macro of a command alias.\n \"\"\",\n 'syntax': \"\"\"\n Rhino.AliasMacro (strAlias [, strMacro])\n \"\"\",\n 'params': {(0): {'name': 'alias', 'optional': False, 'type_vb':\n 'string', 'type_string': 'str', 'doc':\n \"\"\"\n The name of an existing command alias.\n \"\"\"}, (1\n ): {'name': 'macro', 'optional': True, 'type_vb': 'string',\n 'type_string': 'str', 'doc':\n \"\"\"\n The new macro to run when the alias is executed.\n \"\"\"\n }}, 'returns': {(0): {'type_vb': 'String', 'doc':\n 'If a new macro is not specified, the existing macro if successful.'},\n (1): {'type_vb': 'String', 'doc':\n 'If a new macro is specified, the previous macro if successful.'}, (2):\n {'type_vb': 'Null', 'doc': 'If not successful, or on error.'}}}\n","<assignment token>\n"],"string":"[\n \"alias_macro = {\\r\\n\\r\\n \\\"class\\\": \\\"Application\\\",\\r\\n \\\"method\\\": \\\"alias_macro\\\",\\r\\n \\\"doc\\\": \\\"\\\"\\\"\\r\\n Returns or modifies the macro of a command alias.\\r\\n \\\"\\\"\\\",\\r\\n\\r\\n \\\"syntax\\\": \\\"\\\"\\\"\\r\\n Rhino.AliasMacro (strAlias [, strMacro])\\r\\n \\\"\\\"\\\",\\r\\n\\r\\n \\\"params\\\": {\\r\\n 0: {\\r\\n \\\"name\\\": \\\"alias\\\",\\r\\n \\\"optional\\\": False,\\r\\n \\\"type_vb\\\": \\\"string\\\",\\r\\n \\\"type_string\\\": \\\"str\\\",\\r\\n \\\"doc\\\": \\\"\\\"\\\"\\r\\n The name of an existing command alias.\\r\\n \\\"\\\"\\\"\\r\\n },\\r\\n 1: {\\r\\n \\\"name\\\": \\\"macro\\\",\\r\\n \\\"optional\\\": True,\\r\\n \\\"type_vb\\\": \\\"string\\\",\\r\\n \\\"type_string\\\": \\\"str\\\",\\r\\n \\\"doc\\\": \\\"\\\"\\\"\\r\\n The new macro to run when the alias is executed.\\r\\n \\\"\\\"\\\"\\r\\n },\\r\\n },\\r\\n\\r\\n \\\"returns\\\": {\\r\\n 0: {\\r\\n \\\"type_vb\\\": \\\"String\\\",\\r\\n \\\"doc\\\": \\\"If a new macro is not specified, the existing macro if successful.\\\"\\r\\n },\\r\\n 1: {\\r\\n \\\"type_vb\\\": \\\"String\\\",\\r\\n \\\"doc\\\": \\\"If a new macro is specified, the previous macro if successful.\\\"\\r\\n },\\r\\n 2: {\\r\\n \\\"type_vb\\\": \\\"Null\\\",\\r\\n \\\"doc\\\": \\\"If not successful, or on error.\\\"\\r\\n },\\r\\n }\\r\\n\\r\\n}\\r\\n\\r\\n\",\n \"alias_macro = {'class': 'Application', 'method': 'alias_macro', 'doc':\\n \\\"\\\"\\\"\\n Returns or modifies the macro of a command alias.\\n \\\"\\\"\\\",\\n 'syntax': \\\"\\\"\\\"\\n Rhino.AliasMacro (strAlias [, strMacro])\\n \\\"\\\"\\\",\\n 'params': {(0): {'name': 'alias', 'optional': False, 'type_vb':\\n 'string', 'type_string': 'str', 'doc':\\n \\\"\\\"\\\"\\n The name of an existing command alias.\\n \\\"\\\"\\\"}, (1\\n ): {'name': 'macro', 'optional': True, 'type_vb': 'string',\\n 'type_string': 'str', 'doc':\\n \\\"\\\"\\\"\\n The new macro to run when the alias is executed.\\n \\\"\\\"\\\"\\n }}, 'returns': {(0): {'type_vb': 'String', 'doc':\\n 'If a new macro is not specified, the existing macro if successful.'},\\n (1): {'type_vb': 'String', 'doc':\\n 'If a new macro is specified, the previous macro if successful.'}, (2):\\n {'type_vb': 'Null', 'doc': 'If not successful, or on error.'}}}\\n\",\n \"<assignment token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7588,"cells":{"index":{"kind":"number","value":7588,"string":"7,588"},"blob_id":{"kind":"string","value":"3dc83168264fbb4f9b0ab2980b845dffdc4417bb"},"code":{"kind":"string","value":"import requests\nfrom bs4 import BeautifulSoup\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = \"\"\n self.category = \"\"\n self.upc=\"\"\n self.price_including_tax=\"\"\n self.price_excluding_tax=\"\"\n self.number_available=\"\"\n self.description=\"\"\n self.review_rating=\"\"\n self.image_url=\"\"\n self.tax=\"\"\n \n def scrap(self): \n book = requests.get(self.url) \n soup = BeautifulSoup(book.content, \"html.parser\")\n self.__fill_title(soup) \n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n \n def __fill_title(self,soup): \n title = soup.find(\"div\", {\"class\": \"col-sm-6 product_main\"}).find(\"h1\")\n self.title= title.text\n # return self.title\n \n \n def __fill_category(self,soup):\n category = soup.findAll(\"li\")\n category2 = category[2].text\n self.category = category2.replace(\"\\n\", \"\")\n # return self.category\n\n def __fill_upc(self,soup):\n tds = soup.findAll(\"td\")\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self,soup):\n tds = soup.findAll(\"td\")\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self,soup):\n tds = soup.findAll(\"td\")\n self.price_excluding_tax = tds[2].text\n\n def __fill_number_available(self,soup):\n tds = soup.findAll(\"td\")\n self.number_available = tds[5].text\n\n def __fill_description(self,soup):\n div = soup.find(\"div\", class_=\"sub-header\")\n p = div.find_next_sibling()\n self.description = p.text\n # return self.description\n\n def __fill_review_rating(self,soup):\n p = soup.find(\"div\", {\"class\": \"col-sm-6 product_main\"}).find(\n \"p\", class_=\"star-rating\"\n )\n rating = str(p[\"class\"])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n \n\n def __fill_image_url(self,soup):\n image = soup.find(\"div\", {\"class\": \"item active\"}).find(\"img\")\n image_url = image[\"src\"]\n image_clean_url = image_url.replace(\"../../\", \"http://books.toscrape.com/\")\n self.image_url = image_clean_url\n\n def __fill_tax(self,soup):\n tds = soup.findAll(\"td\")\n self.tax = tds[4].text\n\n def __str__(self):\n output = f\"url : {self.url} \\ntitle : {self.title} \\ncategory : {self.category} \\nupc : {self.upc} \\nprice_including_tax : {self.price_including_tax} \\nprice_excluding_tax : {self.price_excluding_tax} \\nnumber_available : {self.number_available} \\ndescription : {self.description} \\nreview_rating : {self.review_rating} \\nimage_url : {self.image_url} \\ntax : {self.tax} \"\n return output\n \n \n\n \n# book = Book(\"http://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html\")\n# book.scrap(\"http://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html\")\n# print(book)\n"},"steps":{"kind":"list like","value":["import requests\nfrom bs4 import BeautifulSoup\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = \"\"\n self.category = \"\"\n self.upc=\"\"\n self.price_including_tax=\"\"\n self.price_excluding_tax=\"\"\n self.number_available=\"\"\n self.description=\"\"\n self.review_rating=\"\"\n self.image_url=\"\"\n self.tax=\"\"\n \n def scrap(self): \n book = requests.get(self.url) \n soup = BeautifulSoup(book.content, \"html.parser\")\n self.__fill_title(soup) \n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n \n def __fill_title(self,soup): \n title = soup.find(\"div\", {\"class\": \"col-sm-6 product_main\"}).find(\"h1\")\n self.title= title.text\n # return self.title\n \n \n def __fill_category(self,soup):\n category = soup.findAll(\"li\")\n category2 = category[2].text\n self.category = category2.replace(\"\\n\", \"\")\n # return self.category\n\n def __fill_upc(self,soup):\n tds = soup.findAll(\"td\")\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self,soup):\n tds = soup.findAll(\"td\")\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self,soup):\n tds = soup.findAll(\"td\")\n self.price_excluding_tax = tds[2].text\n\n def __fill_number_available(self,soup):\n tds = soup.findAll(\"td\")\n self.number_available = tds[5].text\n\n def __fill_description(self,soup):\n div = soup.find(\"div\", class_=\"sub-header\")\n p = div.find_next_sibling()\n self.description = p.text\n # return self.description\n\n def __fill_review_rating(self,soup):\n p = soup.find(\"div\", {\"class\": \"col-sm-6 product_main\"}).find(\n \"p\", class_=\"star-rating\"\n )\n rating = str(p[\"class\"])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n \n\n def __fill_image_url(self,soup):\n image = soup.find(\"div\", {\"class\": \"item active\"}).find(\"img\")\n image_url = image[\"src\"]\n image_clean_url = image_url.replace(\"../../\", \"http://books.toscrape.com/\")\n self.image_url = image_clean_url\n\n def __fill_tax(self,soup):\n tds = soup.findAll(\"td\")\n self.tax = tds[4].text\n\n def __str__(self):\n output = f\"url : {self.url} \\ntitle : {self.title} \\ncategory : {self.category} \\nupc : {self.upc} \\nprice_including_tax : {self.price_including_tax} \\nprice_excluding_tax : {self.price_excluding_tax} \\nnumber_available : {self.number_available} \\ndescription : {self.description} \\nreview_rating : {self.review_rating} \\nimage_url : {self.image_url} \\ntax : {self.tax} \"\n return output\n \n \n\n \n# book = Book(\"http://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html\")\n# book.scrap(\"http://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html\")\n# print(book)\n","import requests\nfrom bs4 import BeautifulSoup\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n\n def scrap(self):\n book = requests.get(self.url)\n soup = BeautifulSoup(book.content, 'html.parser')\n self.__fill_title(soup)\n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n\n def __fill_category(self, soup):\n category = soup.findAll('li')\n category2 = category[2].text\n self.category = category2.replace('\\n', '')\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n\n def __fill_number_available(self, soup):\n tds = soup.findAll('td')\n self.number_available = tds[5].text\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n\n def __fill_image_url(self, soup):\n image = soup.find('div', {'class': 'item active'}).find('img')\n image_url = image['src']\n image_clean_url = image_url.replace('../../',\n 'http://books.toscrape.com/')\n self.image_url = image_clean_url\n\n def __fill_tax(self, soup):\n tds = soup.findAll('td')\n self.tax = tds[4].text\n\n def __str__(self):\n output = f\"\"\"url : {self.url} \ntitle : {self.title} \ncategory : {self.category} \nupc : {self.upc} \nprice_including_tax : {self.price_including_tax} \nprice_excluding_tax : {self.price_excluding_tax} \nnumber_available : {self.number_available} \ndescription : {self.description} \nreview_rating : {self.review_rating} \nimage_url : {self.image_url} \ntax : {self.tax} \"\"\"\n return output\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n\n def scrap(self):\n book = requests.get(self.url)\n soup = BeautifulSoup(book.content, 'html.parser')\n self.__fill_title(soup)\n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n\n def __fill_category(self, soup):\n category = soup.findAll('li')\n category2 = category[2].text\n self.category = category2.replace('\\n', '')\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n\n def __fill_number_available(self, soup):\n tds = soup.findAll('td')\n self.number_available = tds[5].text\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n\n def __fill_image_url(self, soup):\n image = soup.find('div', {'class': 'item active'}).find('img')\n image_url = image['src']\n image_clean_url = image_url.replace('../../',\n 'http://books.toscrape.com/')\n self.image_url = image_clean_url\n\n def __fill_tax(self, soup):\n tds = soup.findAll('td')\n self.tax = tds[4].text\n\n def __str__(self):\n output = f\"\"\"url : {self.url} \ntitle : {self.title} \ncategory : {self.category} \nupc : {self.upc} \nprice_including_tax : {self.price_including_tax} \nprice_excluding_tax : {self.price_excluding_tax} \nnumber_available : {self.number_available} \ndescription : {self.description} \nreview_rating : {self.review_rating} \nimage_url : {self.image_url} \ntax : {self.tax} \"\"\"\n return output\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n\n def scrap(self):\n book = requests.get(self.url)\n soup = BeautifulSoup(book.content, 'html.parser')\n self.__fill_title(soup)\n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n\n def __fill_category(self, soup):\n category = soup.findAll('li')\n category2 = category[2].text\n self.category = category2.replace('\\n', '')\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n\n def __fill_number_available(self, soup):\n tds = soup.findAll('td')\n self.number_available = tds[5].text\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n\n def __fill_image_url(self, soup):\n image = soup.find('div', {'class': 'item active'}).find('img')\n image_url = image['src']\n image_clean_url = image_url.replace('../../',\n 'http://books.toscrape.com/')\n self.image_url = image_clean_url\n <function token>\n\n def __str__(self):\n output = f\"\"\"url : {self.url} \ntitle : {self.title} \ncategory : {self.category} \nupc : {self.upc} \nprice_including_tax : {self.price_including_tax} \nprice_excluding_tax : {self.price_excluding_tax} \nnumber_available : {self.number_available} \ndescription : {self.description} \nreview_rating : {self.review_rating} \nimage_url : {self.image_url} \ntax : {self.tax} \"\"\"\n return output\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n\n def scrap(self):\n book = requests.get(self.url)\n soup = BeautifulSoup(book.content, 'html.parser')\n self.__fill_title(soup)\n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n\n def __fill_category(self, soup):\n category = soup.findAll('li')\n category2 = category[2].text\n self.category = category2.replace('\\n', '')\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n\n def __fill_number_available(self, soup):\n tds = soup.findAll('td')\n self.number_available = tds[5].text\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n\n def __str__(self):\n output = f\"\"\"url : {self.url} \ntitle : {self.title} \ncategory : {self.category} \nupc : {self.upc} \nprice_including_tax : {self.price_including_tax} \nprice_excluding_tax : {self.price_excluding_tax} \nnumber_available : {self.number_available} \ndescription : {self.description} \nreview_rating : {self.review_rating} \nimage_url : {self.image_url} \ntax : {self.tax} \"\"\"\n return output\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n\n def scrap(self):\n book = requests.get(self.url)\n soup = BeautifulSoup(book.content, 'html.parser')\n self.__fill_title(soup)\n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n\n def __fill_category(self, soup):\n category = soup.findAll('li')\n category2 = category[2].text\n self.category = category2.replace('\\n', '')\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n\n def __fill_number_available(self, soup):\n tds = soup.findAll('td')\n self.number_available = tds[5].text\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n\n def scrap(self):\n book = requests.get(self.url)\n soup = BeautifulSoup(book.content, 'html.parser')\n self.__fill_title(soup)\n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n\n def __fill_category(self, soup):\n category = soup.findAll('li')\n category2 = category[2].text\n self.category = category2.replace('\\n', '')\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n <function token>\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n\n def scrap(self):\n book = requests.get(self.url)\n soup = BeautifulSoup(book.content, 'html.parser')\n self.__fill_title(soup)\n self.__fill_category(soup)\n self.__fill_upc(soup)\n self.__fill_price_including_tax(soup)\n self.__fill_price_excluding_tax(soup)\n self.__fill_number_available(soup)\n self.__fill_description(soup)\n self.__fill_review_rating(soup)\n self.__fill_image_url(soup)\n self.__fill_tax(soup)\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n <function token>\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n <function token>\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n <function token>\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n <function token>\n\n def __fill_upc(self, soup):\n tds = soup.findAll('td')\n self.upc = tds[0].text\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n <function token>\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n <function token>\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n <function token>\n <function token>\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n\n def __fill_price_excluding_tax(self, soup):\n tds = soup.findAll('td')\n self.price_excluding_tax = tds[2].text\n <function token>\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = ''\n self.category = ''\n self.upc = ''\n self.price_including_tax = ''\n self.price_excluding_tax = ''\n self.number_available = ''\n self.description = ''\n self.review_rating = ''\n self.image_url = ''\n self.tax = ''\n <function token>\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n <function token>\n <function token>\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n <function token>\n <function token>\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n <function token>\n <function token>\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n <function token>\n <function token>\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n <function token>\n <function token>\n\n def __fill_description(self, soup):\n div = soup.find('div', class_='sub-header')\n p = div.find_next_sibling()\n self.description = p.text\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n <function token>\n <function token>\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n <function token>\n <function token>\n\n def __fill_price_including_tax(self, soup):\n tds = soup.findAll('td')\n self.price_including_tax = tds[3].text\n <function token>\n <function token>\n <function token>\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n <function token>\n <function token>\n\n def __fill_title(self, soup):\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\n self.title = title.text\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def __fill_review_rating(self, soup):\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\n class_='star-rating')\n rating = str(p['class'])\n star = rating[15:-1]\n star_rating = eval(star)\n return star_rating\n <function token>\n <function token>\n <function token>\n","<import token>\n\n\nclass Book:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n","<import token>\n<class token>\n"],"string":"[\n \"import requests\\nfrom bs4 import BeautifulSoup\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = \\\"\\\"\\n self.category = \\\"\\\"\\n self.upc=\\\"\\\"\\n self.price_including_tax=\\\"\\\"\\n self.price_excluding_tax=\\\"\\\"\\n self.number_available=\\\"\\\"\\n self.description=\\\"\\\"\\n self.review_rating=\\\"\\\"\\n self.image_url=\\\"\\\"\\n self.tax=\\\"\\\"\\n \\n def scrap(self): \\n book = requests.get(self.url) \\n soup = BeautifulSoup(book.content, \\\"html.parser\\\")\\n self.__fill_title(soup) \\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n \\n def __fill_title(self,soup): \\n title = soup.find(\\\"div\\\", {\\\"class\\\": \\\"col-sm-6 product_main\\\"}).find(\\\"h1\\\")\\n self.title= title.text\\n # return self.title\\n \\n \\n def __fill_category(self,soup):\\n category = soup.findAll(\\\"li\\\")\\n category2 = category[2].text\\n self.category = category2.replace(\\\"\\\\n\\\", \\\"\\\")\\n # return self.category\\n\\n def __fill_upc(self,soup):\\n tds = soup.findAll(\\\"td\\\")\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self,soup):\\n tds = soup.findAll(\\\"td\\\")\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self,soup):\\n tds = soup.findAll(\\\"td\\\")\\n self.price_excluding_tax = tds[2].text\\n\\n def __fill_number_available(self,soup):\\n tds = soup.findAll(\\\"td\\\")\\n self.number_available = tds[5].text\\n\\n def __fill_description(self,soup):\\n div = soup.find(\\\"div\\\", class_=\\\"sub-header\\\")\\n p = div.find_next_sibling()\\n self.description = p.text\\n # return self.description\\n\\n def __fill_review_rating(self,soup):\\n p = soup.find(\\\"div\\\", {\\\"class\\\": \\\"col-sm-6 product_main\\\"}).find(\\n \\\"p\\\", class_=\\\"star-rating\\\"\\n )\\n rating = str(p[\\\"class\\\"])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n \\n\\n def __fill_image_url(self,soup):\\n image = soup.find(\\\"div\\\", {\\\"class\\\": \\\"item active\\\"}).find(\\\"img\\\")\\n image_url = image[\\\"src\\\"]\\n image_clean_url = image_url.replace(\\\"../../\\\", \\\"http://books.toscrape.com/\\\")\\n self.image_url = image_clean_url\\n\\n def __fill_tax(self,soup):\\n tds = soup.findAll(\\\"td\\\")\\n self.tax = tds[4].text\\n\\n def __str__(self):\\n output = f\\\"url : {self.url} \\\\ntitle : {self.title} \\\\ncategory : {self.category} \\\\nupc : {self.upc} \\\\nprice_including_tax : {self.price_including_tax} \\\\nprice_excluding_tax : {self.price_excluding_tax} \\\\nnumber_available : {self.number_available} \\\\ndescription : {self.description} \\\\nreview_rating : {self.review_rating} \\\\nimage_url : {self.image_url} \\\\ntax : {self.tax} \\\"\\n return output\\n \\n \\n\\n \\n# book = Book(\\\"http://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html\\\")\\n# book.scrap(\\\"http://books.toscrape.com/catalogue/a-light-in-the-attic_1000/index.html\\\")\\n# print(book)\\n\",\n \"import requests\\nfrom bs4 import BeautifulSoup\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n\\n def scrap(self):\\n book = requests.get(self.url)\\n soup = BeautifulSoup(book.content, 'html.parser')\\n self.__fill_title(soup)\\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n\\n def __fill_category(self, soup):\\n category = soup.findAll('li')\\n category2 = category[2].text\\n self.category = category2.replace('\\\\n', '')\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n\\n def __fill_number_available(self, soup):\\n tds = soup.findAll('td')\\n self.number_available = tds[5].text\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n\\n def __fill_image_url(self, soup):\\n image = soup.find('div', {'class': 'item active'}).find('img')\\n image_url = image['src']\\n image_clean_url = image_url.replace('../../',\\n 'http://books.toscrape.com/')\\n self.image_url = image_clean_url\\n\\n def __fill_tax(self, soup):\\n tds = soup.findAll('td')\\n self.tax = tds[4].text\\n\\n def __str__(self):\\n output = f\\\"\\\"\\\"url : {self.url} \\ntitle : {self.title} \\ncategory : {self.category} \\nupc : {self.upc} \\nprice_including_tax : {self.price_including_tax} \\nprice_excluding_tax : {self.price_excluding_tax} \\nnumber_available : {self.number_available} \\ndescription : {self.description} \\nreview_rating : {self.review_rating} \\nimage_url : {self.image_url} \\ntax : {self.tax} \\\"\\\"\\\"\\n return output\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n\\n def scrap(self):\\n book = requests.get(self.url)\\n soup = BeautifulSoup(book.content, 'html.parser')\\n self.__fill_title(soup)\\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n\\n def __fill_category(self, soup):\\n category = soup.findAll('li')\\n category2 = category[2].text\\n self.category = category2.replace('\\\\n', '')\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n\\n def __fill_number_available(self, soup):\\n tds = soup.findAll('td')\\n self.number_available = tds[5].text\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n\\n def __fill_image_url(self, soup):\\n image = soup.find('div', {'class': 'item active'}).find('img')\\n image_url = image['src']\\n image_clean_url = image_url.replace('../../',\\n 'http://books.toscrape.com/')\\n self.image_url = image_clean_url\\n\\n def __fill_tax(self, soup):\\n tds = soup.findAll('td')\\n self.tax = tds[4].text\\n\\n def __str__(self):\\n output = f\\\"\\\"\\\"url : {self.url} \\ntitle : {self.title} \\ncategory : {self.category} \\nupc : {self.upc} \\nprice_including_tax : {self.price_including_tax} \\nprice_excluding_tax : {self.price_excluding_tax} \\nnumber_available : {self.number_available} \\ndescription : {self.description} \\nreview_rating : {self.review_rating} \\nimage_url : {self.image_url} \\ntax : {self.tax} \\\"\\\"\\\"\\n return output\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n\\n def scrap(self):\\n book = requests.get(self.url)\\n soup = BeautifulSoup(book.content, 'html.parser')\\n self.__fill_title(soup)\\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n\\n def __fill_category(self, soup):\\n category = soup.findAll('li')\\n category2 = category[2].text\\n self.category = category2.replace('\\\\n', '')\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n\\n def __fill_number_available(self, soup):\\n tds = soup.findAll('td')\\n self.number_available = tds[5].text\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n\\n def __fill_image_url(self, soup):\\n image = soup.find('div', {'class': 'item active'}).find('img')\\n image_url = image['src']\\n image_clean_url = image_url.replace('../../',\\n 'http://books.toscrape.com/')\\n self.image_url = image_clean_url\\n <function token>\\n\\n def __str__(self):\\n output = f\\\"\\\"\\\"url : {self.url} \\ntitle : {self.title} \\ncategory : {self.category} \\nupc : {self.upc} \\nprice_including_tax : {self.price_including_tax} \\nprice_excluding_tax : {self.price_excluding_tax} \\nnumber_available : {self.number_available} \\ndescription : {self.description} \\nreview_rating : {self.review_rating} \\nimage_url : {self.image_url} \\ntax : {self.tax} \\\"\\\"\\\"\\n return output\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n\\n def scrap(self):\\n book = requests.get(self.url)\\n soup = BeautifulSoup(book.content, 'html.parser')\\n self.__fill_title(soup)\\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n\\n def __fill_category(self, soup):\\n category = soup.findAll('li')\\n category2 = category[2].text\\n self.category = category2.replace('\\\\n', '')\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n\\n def __fill_number_available(self, soup):\\n tds = soup.findAll('td')\\n self.number_available = tds[5].text\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n\\n def __str__(self):\\n output = f\\\"\\\"\\\"url : {self.url} \\ntitle : {self.title} \\ncategory : {self.category} \\nupc : {self.upc} \\nprice_including_tax : {self.price_including_tax} \\nprice_excluding_tax : {self.price_excluding_tax} \\nnumber_available : {self.number_available} \\ndescription : {self.description} \\nreview_rating : {self.review_rating} \\nimage_url : {self.image_url} \\ntax : {self.tax} \\\"\\\"\\\"\\n return output\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n\\n def scrap(self):\\n book = requests.get(self.url)\\n soup = BeautifulSoup(book.content, 'html.parser')\\n self.__fill_title(soup)\\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n\\n def __fill_category(self, soup):\\n category = soup.findAll('li')\\n category2 = category[2].text\\n self.category = category2.replace('\\\\n', '')\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n\\n def __fill_number_available(self, soup):\\n tds = soup.findAll('td')\\n self.number_available = tds[5].text\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n\\n def scrap(self):\\n book = requests.get(self.url)\\n soup = BeautifulSoup(book.content, 'html.parser')\\n self.__fill_title(soup)\\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n\\n def __fill_category(self, soup):\\n category = soup.findAll('li')\\n category2 = category[2].text\\n self.category = category2.replace('\\\\n', '')\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n <function token>\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n\\n def scrap(self):\\n book = requests.get(self.url)\\n soup = BeautifulSoup(book.content, 'html.parser')\\n self.__fill_title(soup)\\n self.__fill_category(soup)\\n self.__fill_upc(soup)\\n self.__fill_price_including_tax(soup)\\n self.__fill_price_excluding_tax(soup)\\n self.__fill_number_available(soup)\\n self.__fill_description(soup)\\n self.__fill_review_rating(soup)\\n self.__fill_image_url(soup)\\n self.__fill_tax(soup)\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n <function token>\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n <function token>\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n <function token>\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n <function token>\\n\\n def __fill_upc(self, soup):\\n tds = soup.findAll('td')\\n self.upc = tds[0].text\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n <function token>\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n <function token>\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n <function token>\\n <function token>\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n\\n def __fill_price_excluding_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_excluding_tax = tds[2].text\\n <function token>\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n\\n def __init__(self, url):\\n self.url = url\\n self.title = ''\\n self.category = ''\\n self.upc = ''\\n self.price_including_tax = ''\\n self.price_excluding_tax = ''\\n self.number_available = ''\\n self.description = ''\\n self.review_rating = ''\\n self.image_url = ''\\n self.tax = ''\\n <function token>\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n <function token>\\n <function token>\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n <function token>\\n <function token>\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n <function token>\\n <function token>\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n <function token>\\n <function token>\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n <function token>\\n <function token>\\n\\n def __fill_description(self, soup):\\n div = soup.find('div', class_='sub-header')\\n p = div.find_next_sibling()\\n self.description = p.text\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n <function token>\\n <function token>\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n <function token>\\n <function token>\\n\\n def __fill_price_including_tax(self, soup):\\n tds = soup.findAll('td')\\n self.price_including_tax = tds[3].text\\n <function token>\\n <function token>\\n <function token>\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n <function token>\\n <function token>\\n\\n def __fill_title(self, soup):\\n title = soup.find('div', {'class': 'col-sm-6 product_main'}).find('h1')\\n self.title = title.text\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def __fill_review_rating(self, soup):\\n p = soup.find('div', {'class': 'col-sm-6 product_main'}).find('p',\\n class_='star-rating')\\n rating = str(p['class'])\\n star = rating[15:-1]\\n star_rating = eval(star)\\n return star_rating\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass Book:\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7589,"cells":{"index":{"kind":"number","value":7589,"string":"7,589"},"blob_id":{"kind":"string","value":"e51c57f4487a3225936d073142f1f770815c0d47"},"code":{"kind":"string","value":"#!/usr/bin/env python\n# coding: utf-8\nimport sys,pysrt\nimport urllib2,urllib,json\nimport re\nfrom urlparse import urlparse\nimport os\nfrom mtranslate import translate\nfrom argparse import ArgumentParser\nreload(sys) \nsys.setdefaultencoding('utf8')\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef cleanhtml(raw_html):\n '''\n TODO: refactor this to make it as generic as possible\n '''\n cleanr = re.compile('<.*?>')\n cleantext = re.sub(cleanr, '', raw_html)\n cleantext = cleantext.replace('[vacilación]','...')\n cleantext = cleantext.replace(' ',' ')\n cleantext = urlparse(cleantext).path \n return cleantext\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSub(args,_subtitle,_filename): \n subs = pysrt.from_string(str(_subtitle).decode('utf-8')) \n output = args.OUTPUT + _filename\n #file = pysrt.SubRipFile() \n text = '' \n for index in range(len(subs)): \n if subs[index].text != '': \n if args.VERBOSE:\n print \"Translating line:\" + cleanhtml(subs[index].text) \n subs[index].text = translate(cleanhtml(subs[index].text).encode('utf-8'),args.LANG_TO,args.LANG_FROM) \n subs.save(output)\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSubMedia(args):\n subLangURL= 'https://media.upv.es/rest/plugins/admin-plugin-translectures/langs/'\n subUrl = 'https://media.upv.es/rest/plugins/admin-plugin-translectures/srt/' \n langlist =json.loads(urllib2.urlopen(subLangURL + args.SOURCE).read()) \n for lang in langlist: \n if lang['lang']==args.LANG_FROM: \n sub = urllib2.urlopen(subUrl + args.SOURCE +'/' + args.LANG_FROM).read() \n generateSub(args,sub,args.SOURCE+'_' + args.LANG_TO.lower() + '.srt') \n return 0\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSubFile(args,_filename=None): \n if _filename is None:\n _source = args.SOURCE\n else:\n _source = _filename\n if _source[-4:]=='.srt': \n substring = open(_source,'r').read() \n generateSub(args,substring,_source.replace('.srt','_' + args.LANG_TO + '.srt')) \n else:\n print \"Incorrect file format\"\n return -1\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSubFolder(args): \n _source = args.SOURCE if args.SOURCE[-1:]=='/' else args.SOURCE + '/'\n if os.path.isdir(args.SOURCE):\n for root, dirs, files in os.walk(args.SOURCE): \n for f in files: \n if f[-4:]=='.srt':\n substring = open(root + f if root[-1:]=='/' else root + '/' + f,'r').read() \n generateSub(args,substring,f.replace('.srt','_' + args.LANG_TO + '.srt')) \n else:\n print \"Incorrect file format\"\n return -1\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef main():\n parser = ArgumentParser(description='Translate subtitle from media id, file or folder', parents=[]) \n parser.add_argument('-v', '--verbose', action='store_true', dest='VERBOSE', default=False, help='Verbose') \n parser.add_argument('-t', '--sourceType', type=str, dest='SOURCE_TYPE', help='source type, pick between media|file|folder')\n parser.add_argument('-s', '--source', type=str, dest='SOURCE', help='source of the subtitle/s')\n parser.add_argument('-langf', '--langFrom', type=str, dest='LANG_FROM', default='es', help='Language that we want to translate')\n parser.add_argument('-langt', '--langTo', type=str, dest='LANG_TO', default='en', help='Language of the output subtitle') \n parser.add_argument('-o', '--output', type=str, dest='OUTPUT', default='./', help='Output folder to store the result') \n args = parser.parse_args() \n \n \n if (args.SOURCE_TYPE.lower()=='file'):\n try: \n generateSubFile(args)\n except:\n return -1 \n elif (args.SOURCE_TYPE.lower()=='folder'):\n try:\n generateSubFolder(args)\n except:\n return -1\n elif (args.SOURCE_TYPE.lower()=='media'):\n try: \n generateSubMedia(args)\n except: \n return -1\n else:\n print \"Choose a valid source type\" \n\n return 0\n \n#---------------------------------------------------------------------------------------------------------------------------------- \nif (__name__ == '__main__'):\n main() \n\n"},"steps":{"kind":"list like","value":["#!/usr/bin/env python\n# coding: utf-8\nimport sys,pysrt\nimport urllib2,urllib,json\nimport re\nfrom urlparse import urlparse\nimport os\nfrom mtranslate import translate\nfrom argparse import ArgumentParser\nreload(sys) \nsys.setdefaultencoding('utf8')\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef cleanhtml(raw_html):\n '''\n TODO: refactor this to make it as generic as possible\n '''\n cleanr = re.compile('<.*?>')\n cleantext = re.sub(cleanr, '', raw_html)\n cleantext = cleantext.replace('[vacilación]','...')\n cleantext = cleantext.replace(' ',' ')\n cleantext = urlparse(cleantext).path \n return cleantext\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSub(args,_subtitle,_filename): \n subs = pysrt.from_string(str(_subtitle).decode('utf-8')) \n output = args.OUTPUT + _filename\n #file = pysrt.SubRipFile() \n text = '' \n for index in range(len(subs)): \n if subs[index].text != '': \n if args.VERBOSE:\n print \"Translating line:\" + cleanhtml(subs[index].text) \n subs[index].text = translate(cleanhtml(subs[index].text).encode('utf-8'),args.LANG_TO,args.LANG_FROM) \n subs.save(output)\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSubMedia(args):\n subLangURL= 'https://media.upv.es/rest/plugins/admin-plugin-translectures/langs/'\n subUrl = 'https://media.upv.es/rest/plugins/admin-plugin-translectures/srt/' \n langlist =json.loads(urllib2.urlopen(subLangURL + args.SOURCE).read()) \n for lang in langlist: \n if lang['lang']==args.LANG_FROM: \n sub = urllib2.urlopen(subUrl + args.SOURCE +'/' + args.LANG_FROM).read() \n generateSub(args,sub,args.SOURCE+'_' + args.LANG_TO.lower() + '.srt') \n return 0\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSubFile(args,_filename=None): \n if _filename is None:\n _source = args.SOURCE\n else:\n _source = _filename\n if _source[-4:]=='.srt': \n substring = open(_source,'r').read() \n generateSub(args,substring,_source.replace('.srt','_' + args.LANG_TO + '.srt')) \n else:\n print \"Incorrect file format\"\n return -1\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef generateSubFolder(args): \n _source = args.SOURCE if args.SOURCE[-1:]=='/' else args.SOURCE + '/'\n if os.path.isdir(args.SOURCE):\n for root, dirs, files in os.walk(args.SOURCE): \n for f in files: \n if f[-4:]=='.srt':\n substring = open(root + f if root[-1:]=='/' else root + '/' + f,'r').read() \n generateSub(args,substring,f.replace('.srt','_' + args.LANG_TO + '.srt')) \n else:\n print \"Incorrect file format\"\n return -1\n\n#----------------------------------------------------------------------------------------------------------------------------------\ndef main():\n parser = ArgumentParser(description='Translate subtitle from media id, file or folder', parents=[]) \n parser.add_argument('-v', '--verbose', action='store_true', dest='VERBOSE', default=False, help='Verbose') \n parser.add_argument('-t', '--sourceType', type=str, dest='SOURCE_TYPE', help='source type, pick between media|file|folder')\n parser.add_argument('-s', '--source', type=str, dest='SOURCE', help='source of the subtitle/s')\n parser.add_argument('-langf', '--langFrom', type=str, dest='LANG_FROM', default='es', help='Language that we want to translate')\n parser.add_argument('-langt', '--langTo', type=str, dest='LANG_TO', default='en', help='Language of the output subtitle') \n parser.add_argument('-o', '--output', type=str, dest='OUTPUT', default='./', help='Output folder to store the result') \n args = parser.parse_args() \n \n \n if (args.SOURCE_TYPE.lower()=='file'):\n try: \n generateSubFile(args)\n except:\n return -1 \n elif (args.SOURCE_TYPE.lower()=='folder'):\n try:\n generateSubFolder(args)\n except:\n return -1\n elif (args.SOURCE_TYPE.lower()=='media'):\n try: \n generateSubMedia(args)\n except: \n return -1\n else:\n print \"Choose a valid source type\" \n\n return 0\n \n#---------------------------------------------------------------------------------------------------------------------------------- \nif (__name__ == '__main__'):\n main() \n\n"],"string":"[\n \"#!/usr/bin/env python\\n# coding: utf-8\\nimport sys,pysrt\\nimport urllib2,urllib,json\\nimport re\\nfrom urlparse import urlparse\\nimport os\\nfrom mtranslate import translate\\nfrom argparse import ArgumentParser\\nreload(sys) \\nsys.setdefaultencoding('utf8')\\n\\n#----------------------------------------------------------------------------------------------------------------------------------\\ndef cleanhtml(raw_html):\\n '''\\n TODO: refactor this to make it as generic as possible\\n '''\\n cleanr = re.compile('<.*?>')\\n cleantext = re.sub(cleanr, '', raw_html)\\n cleantext = cleantext.replace('[vacilación]','...')\\n cleantext = cleantext.replace(' ',' ')\\n cleantext = urlparse(cleantext).path \\n return cleantext\\n\\n#----------------------------------------------------------------------------------------------------------------------------------\\ndef generateSub(args,_subtitle,_filename): \\n subs = pysrt.from_string(str(_subtitle).decode('utf-8')) \\n output = args.OUTPUT + _filename\\n #file = pysrt.SubRipFile() \\n text = '' \\n for index in range(len(subs)): \\n if subs[index].text != '': \\n if args.VERBOSE:\\n print \\\"Translating line:\\\" + cleanhtml(subs[index].text) \\n subs[index].text = translate(cleanhtml(subs[index].text).encode('utf-8'),args.LANG_TO,args.LANG_FROM) \\n subs.save(output)\\n\\n#----------------------------------------------------------------------------------------------------------------------------------\\ndef generateSubMedia(args):\\n subLangURL= 'https://media.upv.es/rest/plugins/admin-plugin-translectures/langs/'\\n subUrl = 'https://media.upv.es/rest/plugins/admin-plugin-translectures/srt/' \\n langlist =json.loads(urllib2.urlopen(subLangURL + args.SOURCE).read()) \\n for lang in langlist: \\n if lang['lang']==args.LANG_FROM: \\n sub = urllib2.urlopen(subUrl + args.SOURCE +'/' + args.LANG_FROM).read() \\n generateSub(args,sub,args.SOURCE+'_' + args.LANG_TO.lower() + '.srt') \\n return 0\\n\\n#----------------------------------------------------------------------------------------------------------------------------------\\ndef generateSubFile(args,_filename=None): \\n if _filename is None:\\n _source = args.SOURCE\\n else:\\n _source = _filename\\n if _source[-4:]=='.srt': \\n substring = open(_source,'r').read() \\n generateSub(args,substring,_source.replace('.srt','_' + args.LANG_TO + '.srt')) \\n else:\\n print \\\"Incorrect file format\\\"\\n return -1\\n\\n#----------------------------------------------------------------------------------------------------------------------------------\\ndef generateSubFolder(args): \\n _source = args.SOURCE if args.SOURCE[-1:]=='/' else args.SOURCE + '/'\\n if os.path.isdir(args.SOURCE):\\n for root, dirs, files in os.walk(args.SOURCE): \\n for f in files: \\n if f[-4:]=='.srt':\\n substring = open(root + f if root[-1:]=='/' else root + '/' + f,'r').read() \\n generateSub(args,substring,f.replace('.srt','_' + args.LANG_TO + '.srt')) \\n else:\\n print \\\"Incorrect file format\\\"\\n return -1\\n\\n#----------------------------------------------------------------------------------------------------------------------------------\\ndef main():\\n parser = ArgumentParser(description='Translate subtitle from media id, file or folder', parents=[]) \\n parser.add_argument('-v', '--verbose', action='store_true', dest='VERBOSE', default=False, help='Verbose') \\n parser.add_argument('-t', '--sourceType', type=str, dest='SOURCE_TYPE', help='source type, pick between media|file|folder')\\n parser.add_argument('-s', '--source', type=str, dest='SOURCE', help='source of the subtitle/s')\\n parser.add_argument('-langf', '--langFrom', type=str, dest='LANG_FROM', default='es', help='Language that we want to translate')\\n parser.add_argument('-langt', '--langTo', type=str, dest='LANG_TO', default='en', help='Language of the output subtitle') \\n parser.add_argument('-o', '--output', type=str, dest='OUTPUT', default='./', help='Output folder to store the result') \\n args = parser.parse_args() \\n \\n \\n if (args.SOURCE_TYPE.lower()=='file'):\\n try: \\n generateSubFile(args)\\n except:\\n return -1 \\n elif (args.SOURCE_TYPE.lower()=='folder'):\\n try:\\n generateSubFolder(args)\\n except:\\n return -1\\n elif (args.SOURCE_TYPE.lower()=='media'):\\n try: \\n generateSubMedia(args)\\n except: \\n return -1\\n else:\\n print \\\"Choose a valid source type\\\" \\n\\n return 0\\n \\n#---------------------------------------------------------------------------------------------------------------------------------- \\nif (__name__ == '__main__'):\\n main() \\n\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":7590,"cells":{"index":{"kind":"number","value":7590,"string":"7,590"},"blob_id":{"kind":"string","value":"1a29b3138f6a33fbe2781f044c1bcccd03ecd48d"},"code":{"kind":"string","value":"\nd = {\n 1 : 'I',\n 5 : 'V',\n 10: 'X',\n 50: 'L',\n 100: 'C',\n 500: 'D',\n 1000: 'M' \n\n }\n\n\ne = {\n 'I': 1,\n 'V': 5,\n 'X': 10,\n 'L': 50,\n 'C': 100,\n 'D': 500,\n 'M': 1000\n} \n\ndef convert2roman(num,rom = \"\"):\n \n if num == 0:\n return rom\n\n digits = len(str(num))\n multiple = 10 ** (digits -1)\n \n cur = int(num / multiple)\n cur = cur * multiple\n num = num % multiple\n\n halfway = 5 * multiple\n fullway = 10 * multiple\n\n if cur + multiple == halfway:\n rom += d[multiple] + d[halfway]\n elif cur + multiple == fullway:\n rom += d[multiple] + d[fullway]\n else:\n if cur >= halfway:\n cur -= halfway\n rom += d[halfway]\n if cur > 0:\n rom += d[multiple] * int(cur / multiple)\n return convert2roman(num,rom)\n \n\ndef convert2numeral(rom):\n\n cur = 0\n num = 0\n \n while cur < len(rom):\n if cur+1 == len(rom):\n num += e[rom[cur]]\n elif e[rom[cur]] > e[rom[cur+1]]:\n num += e[rom[cur]]\n cur += 1\n elif e[rom[cur]] < e[rom[cur+1]]:\n num += (e[rom[cur + 1]] - e[rom[cur]])\n cur += 2\n return num\n\n\na = convert2roman(499)\nprint(a)\na = convert2numeral(a)\nprint(a) \n"},"steps":{"kind":"list like","value":["\nd = {\n 1 : 'I',\n 5 : 'V',\n 10: 'X',\n 50: 'L',\n 100: 'C',\n 500: 'D',\n 1000: 'M' \n\n }\n\n\ne = {\n 'I': 1,\n 'V': 5,\n 'X': 10,\n 'L': 50,\n 'C': 100,\n 'D': 500,\n 'M': 1000\n} \n\ndef convert2roman(num,rom = \"\"):\n \n if num == 0:\n return rom\n\n digits = len(str(num))\n multiple = 10 ** (digits -1)\n \n cur = int(num / multiple)\n cur = cur * multiple\n num = num % multiple\n\n halfway = 5 * multiple\n fullway = 10 * multiple\n\n if cur + multiple == halfway:\n rom += d[multiple] + d[halfway]\n elif cur + multiple == fullway:\n rom += d[multiple] + d[fullway]\n else:\n if cur >= halfway:\n cur -= halfway\n rom += d[halfway]\n if cur > 0:\n rom += d[multiple] * int(cur / multiple)\n return convert2roman(num,rom)\n \n\ndef convert2numeral(rom):\n\n cur = 0\n num = 0\n \n while cur < len(rom):\n if cur+1 == len(rom):\n num += e[rom[cur]]\n elif e[rom[cur]] > e[rom[cur+1]]:\n num += e[rom[cur]]\n cur += 1\n elif e[rom[cur]] < e[rom[cur+1]]:\n num += (e[rom[cur + 1]] - e[rom[cur]])\n cur += 2\n return num\n\n\na = convert2roman(499)\nprint(a)\na = convert2numeral(a)\nprint(a) \n","d = {(1): 'I', (5): 'V', (10): 'X', (50): 'L', (100): 'C', (500): 'D', (\n 1000): 'M'}\ne = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}\n\n\ndef convert2roman(num, rom=''):\n if num == 0:\n return rom\n digits = len(str(num))\n multiple = 10 ** (digits - 1)\n cur = int(num / multiple)\n cur = cur * multiple\n num = num % multiple\n halfway = 5 * multiple\n fullway = 10 * multiple\n if cur + multiple == halfway:\n rom += d[multiple] + d[halfway]\n elif cur + multiple == fullway:\n rom += d[multiple] + d[fullway]\n else:\n if cur >= halfway:\n cur -= halfway\n rom += d[halfway]\n if cur > 0:\n rom += d[multiple] * int(cur / multiple)\n return convert2roman(num, rom)\n\n\ndef convert2numeral(rom):\n cur = 0\n num = 0\n while cur < len(rom):\n if cur + 1 == len(rom):\n num += e[rom[cur]]\n elif e[rom[cur]] > e[rom[cur + 1]]:\n num += e[rom[cur]]\n cur += 1\n elif e[rom[cur]] < e[rom[cur + 1]]:\n num += e[rom[cur + 1]] - e[rom[cur]]\n cur += 2\n return num\n\n\na = convert2roman(499)\nprint(a)\na = convert2numeral(a)\nprint(a)\n","<assignment token>\n\n\ndef convert2roman(num, rom=''):\n if num == 0:\n return rom\n digits = len(str(num))\n multiple = 10 ** (digits - 1)\n cur = int(num / multiple)\n cur = cur * multiple\n num = num % multiple\n halfway = 5 * multiple\n fullway = 10 * multiple\n if cur + multiple == halfway:\n rom += d[multiple] + d[halfway]\n elif cur + multiple == fullway:\n rom += d[multiple] + d[fullway]\n else:\n if cur >= halfway:\n cur -= halfway\n rom += d[halfway]\n if cur > 0:\n rom += d[multiple] * int(cur / multiple)\n return convert2roman(num, rom)\n\n\ndef convert2numeral(rom):\n cur = 0\n num = 0\n while cur < len(rom):\n if cur + 1 == len(rom):\n num += e[rom[cur]]\n elif e[rom[cur]] > e[rom[cur + 1]]:\n num += e[rom[cur]]\n cur += 1\n elif e[rom[cur]] < e[rom[cur + 1]]:\n num += e[rom[cur + 1]] - e[rom[cur]]\n cur += 2\n return num\n\n\n<assignment token>\nprint(a)\n<assignment token>\nprint(a)\n","<assignment token>\n\n\ndef convert2roman(num, rom=''):\n if num == 0:\n return rom\n digits = len(str(num))\n multiple = 10 ** (digits - 1)\n cur = int(num / multiple)\n cur = cur * multiple\n num = num % multiple\n halfway = 5 * multiple\n fullway = 10 * multiple\n if cur + multiple == halfway:\n rom += d[multiple] + d[halfway]\n elif cur + multiple == fullway:\n rom += d[multiple] + d[fullway]\n else:\n if cur >= halfway:\n cur -= halfway\n rom += d[halfway]\n if cur > 0:\n rom += d[multiple] * int(cur / multiple)\n return convert2roman(num, rom)\n\n\ndef convert2numeral(rom):\n cur = 0\n num = 0\n while cur < len(rom):\n if cur + 1 == len(rom):\n num += e[rom[cur]]\n elif e[rom[cur]] > e[rom[cur + 1]]:\n num += e[rom[cur]]\n cur += 1\n elif e[rom[cur]] < e[rom[cur + 1]]:\n num += e[rom[cur + 1]] - e[rom[cur]]\n cur += 2\n return num\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n","<assignment token>\n\n\ndef convert2roman(num, rom=''):\n if num == 0:\n return rom\n digits = len(str(num))\n multiple = 10 ** (digits - 1)\n cur = int(num / multiple)\n cur = cur * multiple\n num = num % multiple\n halfway = 5 * multiple\n fullway = 10 * multiple\n if cur + multiple == halfway:\n rom += d[multiple] + d[halfway]\n elif cur + multiple == fullway:\n rom += d[multiple] + d[fullway]\n else:\n if cur >= halfway:\n cur -= halfway\n rom += d[halfway]\n if cur > 0:\n rom += d[multiple] * int(cur / multiple)\n return convert2roman(num, rom)\n\n\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n","<assignment token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"],"string":"[\n \"\\nd = {\\n 1 : 'I',\\n 5 : 'V',\\n 10: 'X',\\n 50: 'L',\\n 100: 'C',\\n 500: 'D',\\n 1000: 'M' \\n\\n }\\n\\n\\ne = {\\n 'I': 1,\\n 'V': 5,\\n 'X': 10,\\n 'L': 50,\\n 'C': 100,\\n 'D': 500,\\n 'M': 1000\\n} \\n\\ndef convert2roman(num,rom = \\\"\\\"):\\n \\n if num == 0:\\n return rom\\n\\n digits = len(str(num))\\n multiple = 10 ** (digits -1)\\n \\n cur = int(num / multiple)\\n cur = cur * multiple\\n num = num % multiple\\n\\n halfway = 5 * multiple\\n fullway = 10 * multiple\\n\\n if cur + multiple == halfway:\\n rom += d[multiple] + d[halfway]\\n elif cur + multiple == fullway:\\n rom += d[multiple] + d[fullway]\\n else:\\n if cur >= halfway:\\n cur -= halfway\\n rom += d[halfway]\\n if cur > 0:\\n rom += d[multiple] * int(cur / multiple)\\n return convert2roman(num,rom)\\n \\n\\ndef convert2numeral(rom):\\n\\n cur = 0\\n num = 0\\n \\n while cur < len(rom):\\n if cur+1 == len(rom):\\n num += e[rom[cur]]\\n elif e[rom[cur]] > e[rom[cur+1]]:\\n num += e[rom[cur]]\\n cur += 1\\n elif e[rom[cur]] < e[rom[cur+1]]:\\n num += (e[rom[cur + 1]] - e[rom[cur]])\\n cur += 2\\n return num\\n\\n\\na = convert2roman(499)\\nprint(a)\\na = convert2numeral(a)\\nprint(a) \\n\",\n \"d = {(1): 'I', (5): 'V', (10): 'X', (50): 'L', (100): 'C', (500): 'D', (\\n 1000): 'M'}\\ne = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}\\n\\n\\ndef convert2roman(num, rom=''):\\n if num == 0:\\n return rom\\n digits = len(str(num))\\n multiple = 10 ** (digits - 1)\\n cur = int(num / multiple)\\n cur = cur * multiple\\n num = num % multiple\\n halfway = 5 * multiple\\n fullway = 10 * multiple\\n if cur + multiple == halfway:\\n rom += d[multiple] + d[halfway]\\n elif cur + multiple == fullway:\\n rom += d[multiple] + d[fullway]\\n else:\\n if cur >= halfway:\\n cur -= halfway\\n rom += d[halfway]\\n if cur > 0:\\n rom += d[multiple] * int(cur / multiple)\\n return convert2roman(num, rom)\\n\\n\\ndef convert2numeral(rom):\\n cur = 0\\n num = 0\\n while cur < len(rom):\\n if cur + 1 == len(rom):\\n num += e[rom[cur]]\\n elif e[rom[cur]] > e[rom[cur + 1]]:\\n num += e[rom[cur]]\\n cur += 1\\n elif e[rom[cur]] < e[rom[cur + 1]]:\\n num += e[rom[cur + 1]] - e[rom[cur]]\\n cur += 2\\n return num\\n\\n\\na = convert2roman(499)\\nprint(a)\\na = convert2numeral(a)\\nprint(a)\\n\",\n \"<assignment token>\\n\\n\\ndef convert2roman(num, rom=''):\\n if num == 0:\\n return rom\\n digits = len(str(num))\\n multiple = 10 ** (digits - 1)\\n cur = int(num / multiple)\\n cur = cur * multiple\\n num = num % multiple\\n halfway = 5 * multiple\\n fullway = 10 * multiple\\n if cur + multiple == halfway:\\n rom += d[multiple] + d[halfway]\\n elif cur + multiple == fullway:\\n rom += d[multiple] + d[fullway]\\n else:\\n if cur >= halfway:\\n cur -= halfway\\n rom += d[halfway]\\n if cur > 0:\\n rom += d[multiple] * int(cur / multiple)\\n return convert2roman(num, rom)\\n\\n\\ndef convert2numeral(rom):\\n cur = 0\\n num = 0\\n while cur < len(rom):\\n if cur + 1 == len(rom):\\n num += e[rom[cur]]\\n elif e[rom[cur]] > e[rom[cur + 1]]:\\n num += e[rom[cur]]\\n cur += 1\\n elif e[rom[cur]] < e[rom[cur + 1]]:\\n num += e[rom[cur + 1]] - e[rom[cur]]\\n cur += 2\\n return num\\n\\n\\n<assignment token>\\nprint(a)\\n<assignment token>\\nprint(a)\\n\",\n \"<assignment token>\\n\\n\\ndef convert2roman(num, rom=''):\\n if num == 0:\\n return rom\\n digits = len(str(num))\\n multiple = 10 ** (digits - 1)\\n cur = int(num / multiple)\\n cur = cur * multiple\\n num = num % multiple\\n halfway = 5 * multiple\\n fullway = 10 * multiple\\n if cur + multiple == halfway:\\n rom += d[multiple] + d[halfway]\\n elif cur + multiple == fullway:\\n rom += d[multiple] + d[fullway]\\n else:\\n if cur >= halfway:\\n cur -= halfway\\n rom += d[halfway]\\n if cur > 0:\\n rom += d[multiple] * int(cur / multiple)\\n return convert2roman(num, rom)\\n\\n\\ndef convert2numeral(rom):\\n cur = 0\\n num = 0\\n while cur < len(rom):\\n if cur + 1 == len(rom):\\n num += e[rom[cur]]\\n elif e[rom[cur]] > e[rom[cur + 1]]:\\n num += e[rom[cur]]\\n cur += 1\\n elif e[rom[cur]] < e[rom[cur + 1]]:\\n num += e[rom[cur + 1]] - e[rom[cur]]\\n cur += 2\\n return num\\n\\n\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\",\n \"<assignment token>\\n\\n\\ndef convert2roman(num, rom=''):\\n if num == 0:\\n return rom\\n digits = len(str(num))\\n multiple = 10 ** (digits - 1)\\n cur = int(num / multiple)\\n cur = cur * multiple\\n num = num % multiple\\n halfway = 5 * multiple\\n fullway = 10 * multiple\\n if cur + multiple == halfway:\\n rom += d[multiple] + d[halfway]\\n elif cur + multiple == fullway:\\n rom += d[multiple] + d[fullway]\\n else:\\n if cur >= halfway:\\n cur -= halfway\\n rom += d[halfway]\\n if cur > 0:\\n rom += d[multiple] * int(cur / multiple)\\n return convert2roman(num, rom)\\n\\n\\n<function token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\",\n \"<assignment token>\\n<function token>\\n<function token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7591,"cells":{"index":{"kind":"number","value":7591,"string":"7,591"},"blob_id":{"kind":"string","value":"b6b3d94db62b47aac9bf78e8224a38ccff9335e3"},"code":{"kind":"string","value":"from . import utility\nfrom . import regular_gram_schmidt as RGSC\nfrom . import custom_gram_schmidt as CGSC\n\n\ndef RegularGramSchmidt():\n while True:\n vectors = utility.get_matrix_from_user(5)\n if len(vectors) > 0:\n calc = RGSC.RegularGramSchmidt()\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input(\"Start over? (Y/n)\")\n if answer.lower() == 'n':\n break\n else:\n continue\n\n\ndef CustomGramSchmidt():\n while True:\n print(\"Enter the inner product matrix 3x3\")\n inner_product_matrix = utility.get_matrix_from_user(3, True)\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\n print(\"Enter vectors from R(3)\")\n vectors = utility.get_matrix_from_user(3)\n if len(vectors) > 0:\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input(\"Start over? (Y/n)\")\n if answer.lower() == 'n':\n break\n else:\n continue\n"},"steps":{"kind":"list like","value":["from . import utility\nfrom . import regular_gram_schmidt as RGSC\nfrom . import custom_gram_schmidt as CGSC\n\n\ndef RegularGramSchmidt():\n while True:\n vectors = utility.get_matrix_from_user(5)\n if len(vectors) > 0:\n calc = RGSC.RegularGramSchmidt()\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input(\"Start over? (Y/n)\")\n if answer.lower() == 'n':\n break\n else:\n continue\n\n\ndef CustomGramSchmidt():\n while True:\n print(\"Enter the inner product matrix 3x3\")\n inner_product_matrix = utility.get_matrix_from_user(3, True)\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\n print(\"Enter vectors from R(3)\")\n vectors = utility.get_matrix_from_user(3)\n if len(vectors) > 0:\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input(\"Start over? (Y/n)\")\n if answer.lower() == 'n':\n break\n else:\n continue\n","from . import utility\nfrom . import regular_gram_schmidt as RGSC\nfrom . import custom_gram_schmidt as CGSC\n\n\ndef RegularGramSchmidt():\n while True:\n vectors = utility.get_matrix_from_user(5)\n if len(vectors) > 0:\n calc = RGSC.RegularGramSchmidt()\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input('Start over? (Y/n)')\n if answer.lower() == 'n':\n break\n else:\n continue\n\n\ndef CustomGramSchmidt():\n while True:\n print('Enter the inner product matrix 3x3')\n inner_product_matrix = utility.get_matrix_from_user(3, True)\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\n print('Enter vectors from R(3)')\n vectors = utility.get_matrix_from_user(3)\n if len(vectors) > 0:\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input('Start over? (Y/n)')\n if answer.lower() == 'n':\n break\n else:\n continue\n","<import token>\n\n\ndef RegularGramSchmidt():\n while True:\n vectors = utility.get_matrix_from_user(5)\n if len(vectors) > 0:\n calc = RGSC.RegularGramSchmidt()\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input('Start over? (Y/n)')\n if answer.lower() == 'n':\n break\n else:\n continue\n\n\ndef CustomGramSchmidt():\n while True:\n print('Enter the inner product matrix 3x3')\n inner_product_matrix = utility.get_matrix_from_user(3, True)\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\n print('Enter vectors from R(3)')\n vectors = utility.get_matrix_from_user(3)\n if len(vectors) > 0:\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input('Start over? (Y/n)')\n if answer.lower() == 'n':\n break\n else:\n continue\n","<import token>\n<function token>\n\n\ndef CustomGramSchmidt():\n while True:\n print('Enter the inner product matrix 3x3')\n inner_product_matrix = utility.get_matrix_from_user(3, True)\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\n print('Enter vectors from R(3)')\n vectors = utility.get_matrix_from_user(3)\n if len(vectors) > 0:\n result_matrix = calc.calc(vectors)\n if result_matrix is not None:\n print(result_matrix)\n utility.print_if_matrix_is_basis(result_matrix)\n answer = input('Start over? (Y/n)')\n if answer.lower() == 'n':\n break\n else:\n continue\n","<import token>\n<function token>\n<function token>\n"],"string":"[\n \"from . import utility\\nfrom . import regular_gram_schmidt as RGSC\\nfrom . import custom_gram_schmidt as CGSC\\n\\n\\ndef RegularGramSchmidt():\\n while True:\\n vectors = utility.get_matrix_from_user(5)\\n if len(vectors) > 0:\\n calc = RGSC.RegularGramSchmidt()\\n result_matrix = calc.calc(vectors)\\n if result_matrix is not None:\\n print(result_matrix)\\n utility.print_if_matrix_is_basis(result_matrix)\\n answer = input(\\\"Start over? (Y/n)\\\")\\n if answer.lower() == 'n':\\n break\\n else:\\n continue\\n\\n\\ndef CustomGramSchmidt():\\n while True:\\n print(\\\"Enter the inner product matrix 3x3\\\")\\n inner_product_matrix = utility.get_matrix_from_user(3, True)\\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\\n print(\\\"Enter vectors from R(3)\\\")\\n vectors = utility.get_matrix_from_user(3)\\n if len(vectors) > 0:\\n result_matrix = calc.calc(vectors)\\n if result_matrix is not None:\\n print(result_matrix)\\n utility.print_if_matrix_is_basis(result_matrix)\\n answer = input(\\\"Start over? (Y/n)\\\")\\n if answer.lower() == 'n':\\n break\\n else:\\n continue\\n\",\n \"from . import utility\\nfrom . import regular_gram_schmidt as RGSC\\nfrom . import custom_gram_schmidt as CGSC\\n\\n\\ndef RegularGramSchmidt():\\n while True:\\n vectors = utility.get_matrix_from_user(5)\\n if len(vectors) > 0:\\n calc = RGSC.RegularGramSchmidt()\\n result_matrix = calc.calc(vectors)\\n if result_matrix is not None:\\n print(result_matrix)\\n utility.print_if_matrix_is_basis(result_matrix)\\n answer = input('Start over? (Y/n)')\\n if answer.lower() == 'n':\\n break\\n else:\\n continue\\n\\n\\ndef CustomGramSchmidt():\\n while True:\\n print('Enter the inner product matrix 3x3')\\n inner_product_matrix = utility.get_matrix_from_user(3, True)\\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\\n print('Enter vectors from R(3)')\\n vectors = utility.get_matrix_from_user(3)\\n if len(vectors) > 0:\\n result_matrix = calc.calc(vectors)\\n if result_matrix is not None:\\n print(result_matrix)\\n utility.print_if_matrix_is_basis(result_matrix)\\n answer = input('Start over? (Y/n)')\\n if answer.lower() == 'n':\\n break\\n else:\\n continue\\n\",\n \"<import token>\\n\\n\\ndef RegularGramSchmidt():\\n while True:\\n vectors = utility.get_matrix_from_user(5)\\n if len(vectors) > 0:\\n calc = RGSC.RegularGramSchmidt()\\n result_matrix = calc.calc(vectors)\\n if result_matrix is not None:\\n print(result_matrix)\\n utility.print_if_matrix_is_basis(result_matrix)\\n answer = input('Start over? (Y/n)')\\n if answer.lower() == 'n':\\n break\\n else:\\n continue\\n\\n\\ndef CustomGramSchmidt():\\n while True:\\n print('Enter the inner product matrix 3x3')\\n inner_product_matrix = utility.get_matrix_from_user(3, True)\\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\\n print('Enter vectors from R(3)')\\n vectors = utility.get_matrix_from_user(3)\\n if len(vectors) > 0:\\n result_matrix = calc.calc(vectors)\\n if result_matrix is not None:\\n print(result_matrix)\\n utility.print_if_matrix_is_basis(result_matrix)\\n answer = input('Start over? (Y/n)')\\n if answer.lower() == 'n':\\n break\\n else:\\n continue\\n\",\n \"<import token>\\n<function token>\\n\\n\\ndef CustomGramSchmidt():\\n while True:\\n print('Enter the inner product matrix 3x3')\\n inner_product_matrix = utility.get_matrix_from_user(3, True)\\n calc = CGSC.CustomGramSchmidt(inner_product_matrix)\\n print('Enter vectors from R(3)')\\n vectors = utility.get_matrix_from_user(3)\\n if len(vectors) > 0:\\n result_matrix = calc.calc(vectors)\\n if result_matrix is not None:\\n print(result_matrix)\\n utility.print_if_matrix_is_basis(result_matrix)\\n answer = input('Start over? (Y/n)')\\n if answer.lower() == 'n':\\n break\\n else:\\n continue\\n\",\n \"<import token>\\n<function token>\\n<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7592,"cells":{"index":{"kind":"number","value":7592,"string":"7,592"},"blob_id":{"kind":"string","value":"ad024a2001dc6a6fa3a2a9c1b51f79132e914897"},"code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\nimport requests\nimport json\n\nurl = \"http://39.108.188.34:9090/spider/zhongdengdengji.go\"\n# url = \"http://localhost:9090/spider/zhongdengdengji.go\"\n\ninput = {\n \"timelimit\": \"1年\",\n \"title\": \"GD20190305001\",\n \"maincontractno\": \"YT20181228001\",\n \"maincontractcurrency\": \"人民币\",\n \"maincontractsum\": \"100000\",\n \"description\": \"Y0181228001测试供应商有限公司与测试项目有限公司就SW00002-20181226-1204，转让应收账款金额100000元T2，测试供应商有限公司已出具应收账款转让通知书，对应的发票号及金额为1111/50000，5555/50000，到期日2018-12-29。付款方万科企业股份有限公司已出具编号为ZB00002-20181226-1204的付款确认及授权书\",\n \"addDebtorList\": [\n {\n # 金融机构\n \"debtorType\": \"企业\",\n \"debtorName\": \"测试供应商有限公司\",\n \"orgCode\": \"9144030068375453XL\",\n \"businessCode\": \"9144030068375453XL\",\n \"lei\": \"#*¥#*（&¥#（*&¥（）\",\n \"responsiblePerson\": \"测试法人1\",\n \"country\": \"中国\",\n \"province\": \"黑龙江省\",\n \"city\": \"哈尔滨市\",\n \"address\": \"北京天安门\",\n }\n\n ]\n\n}\n\ndata = json.dumps(input)\n\nheaders = {\n 'User-Agent': 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36',\n \"Content-Type\": \"application/json\"}\nresponse = requests.post(url, data=data, headers=headers, timeout=(500, 500))\nprint(response.text)\n\n# testAccount = [{'account': 'ytbl0011', 'keyword': 'ytbl0011aDmin'}]\n"},"steps":{"kind":"list like","value":["# -*- coding: utf-8 -*-\n\nimport requests\nimport json\n\nurl = \"http://39.108.188.34:9090/spider/zhongdengdengji.go\"\n# url = \"http://localhost:9090/spider/zhongdengdengji.go\"\n\ninput = {\n \"timelimit\": \"1年\",\n \"title\": \"GD20190305001\",\n \"maincontractno\": \"YT20181228001\",\n \"maincontractcurrency\": \"人民币\",\n \"maincontractsum\": \"100000\",\n \"description\": \"Y0181228001测试供应商有限公司与测试项目有限公司就SW00002-20181226-1204，转让应收账款金额100000元T2，测试供应商有限公司已出具应收账款转让通知书，对应的发票号及金额为1111/50000，5555/50000，到期日2018-12-29。付款方万科企业股份有限公司已出具编号为ZB00002-20181226-1204的付款确认及授权书\",\n \"addDebtorList\": [\n {\n # 金融机构\n \"debtorType\": \"企业\",\n \"debtorName\": \"测试供应商有限公司\",\n \"orgCode\": \"9144030068375453XL\",\n \"businessCode\": \"9144030068375453XL\",\n \"lei\": \"#*¥#*（&¥#（*&¥（）\",\n \"responsiblePerson\": \"测试法人1\",\n \"country\": \"中国\",\n \"province\": \"黑龙江省\",\n \"city\": \"哈尔滨市\",\n \"address\": \"北京天安门\",\n }\n\n ]\n\n}\n\ndata = json.dumps(input)\n\nheaders = {\n 'User-Agent': 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36',\n \"Content-Type\": \"application/json\"}\nresponse = requests.post(url, data=data, headers=headers, timeout=(500, 500))\nprint(response.text)\n\n# testAccount = [{'account': 'ytbl0011', 'keyword': 'ytbl0011aDmin'}]\n","import requests\nimport json\nurl = 'http://39.108.188.34:9090/spider/zhongdengdengji.go'\ninput = {'timelimit': '1年', 'title': 'GD20190305001', 'maincontractno':\n 'YT20181228001', 'maincontractcurrency': '人民币', 'maincontractsum':\n '100000', 'description':\n 'Y0181228001测试供应商有限公司与测试项目有限公司就SW00002-20181226-1204，转让应收账款金额100000元T2，测试供应商有限公司已出具应收账款转让通知书，对应的发票号及金额为1111/50000，5555/50000，到期日2018-12-29。付款方万科企业股份有限公司已出具编号为ZB00002-20181226-1204的付款确认及授权书'\n , 'addDebtorList': [{'debtorType': '企业', 'debtorName': '测试供应商有限公司',\n 'orgCode': '9144030068375453XL', 'businessCode': '9144030068375453XL',\n 'lei': '#*¥#*（&¥#（*&¥（）', 'responsiblePerson': '测试法人1', 'country': '中国',\n 'province': '黑龙江省', 'city': '哈尔滨市', 'address': '北京天安门'}]}\ndata = json.dumps(input)\nheaders = {'User-Agent':\n 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'\n , 'Content-Type': 'application/json'}\nresponse = requests.post(url, data=data, headers=headers, timeout=(500, 500))\nprint(response.text)\n","<import token>\nurl = 'http://39.108.188.34:9090/spider/zhongdengdengji.go'\ninput = {'timelimit': '1年', 'title': 'GD20190305001', 'maincontractno':\n 'YT20181228001', 'maincontractcurrency': '人民币', 'maincontractsum':\n '100000', 'description':\n 'Y0181228001测试供应商有限公司与测试项目有限公司就SW00002-20181226-1204，转让应收账款金额100000元T2，测试供应商有限公司已出具应收账款转让通知书，对应的发票号及金额为1111/50000，5555/50000，到期日2018-12-29。付款方万科企业股份有限公司已出具编号为ZB00002-20181226-1204的付款确认及授权书'\n , 'addDebtorList': [{'debtorType': '企业', 'debtorName': '测试供应商有限公司',\n 'orgCode': '9144030068375453XL', 'businessCode': '9144030068375453XL',\n 'lei': '#*¥#*（&¥#（*&¥（）', 'responsiblePerson': '测试法人1', 'country': '中国',\n 'province': '黑龙江省', 'city': '哈尔滨市', 'address': '北京天安门'}]}\ndata = json.dumps(input)\nheaders = {'User-Agent':\n 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'\n , 'Content-Type': 'application/json'}\nresponse = requests.post(url, data=data, headers=headers, timeout=(500, 500))\nprint(response.text)\n","<import token>\n<assignment token>\nprint(response.text)\n","<import token>\n<assignment token>\n<code token>\n"],"string":"[\n \"# -*- coding: utf-8 -*-\\n\\nimport requests\\nimport json\\n\\nurl = \\\"http://39.108.188.34:9090/spider/zhongdengdengji.go\\\"\\n# url = \\\"http://localhost:9090/spider/zhongdengdengji.go\\\"\\n\\ninput = {\\n \\\"timelimit\\\": \\\"1年\\\",\\n \\\"title\\\": \\\"GD20190305001\\\",\\n \\\"maincontractno\\\": \\\"YT20181228001\\\",\\n \\\"maincontractcurrency\\\": \\\"人民币\\\",\\n \\\"maincontractsum\\\": \\\"100000\\\",\\n \\\"description\\\": \\\"Y0181228001测试供应商有限公司与测试项目有限公司就SW00002-20181226-1204，转让应收账款金额100000元T2，测试供应商有限公司已出具应收账款转让通知书，对应的发票号及金额为1111/50000，5555/50000，到期日2018-12-29。付款方万科企业股份有限公司已出具编号为ZB00002-20181226-1204的付款确认及授权书\\\",\\n \\\"addDebtorList\\\": [\\n {\\n # 金融机构\\n \\\"debtorType\\\": \\\"企业\\\",\\n \\\"debtorName\\\": \\\"测试供应商有限公司\\\",\\n \\\"orgCode\\\": \\\"9144030068375453XL\\\",\\n \\\"businessCode\\\": \\\"9144030068375453XL\\\",\\n \\\"lei\\\": \\\"#*¥#*（&¥#（*&¥（）\\\",\\n \\\"responsiblePerson\\\": \\\"测试法人1\\\",\\n \\\"country\\\": \\\"中国\\\",\\n \\\"province\\\": \\\"黑龙江省\\\",\\n \\\"city\\\": \\\"哈尔滨市\\\",\\n \\\"address\\\": \\\"北京天安门\\\",\\n }\\n\\n ]\\n\\n}\\n\\ndata = json.dumps(input)\\n\\nheaders = {\\n 'User-Agent': 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36',\\n \\\"Content-Type\\\": \\\"application/json\\\"}\\nresponse = requests.post(url, data=data, headers=headers, timeout=(500, 500))\\nprint(response.text)\\n\\n# testAccount = [{'account': 'ytbl0011', 'keyword': 'ytbl0011aDmin'}]\\n\",\n \"import requests\\nimport json\\nurl = 'http://39.108.188.34:9090/spider/zhongdengdengji.go'\\ninput = {'timelimit': '1年', 'title': 'GD20190305001', 'maincontractno':\\n 'YT20181228001', 'maincontractcurrency': '人民币', 'maincontractsum':\\n '100000', 'description':\\n 'Y0181228001测试供应商有限公司与测试项目有限公司就SW00002-20181226-1204，转让应收账款金额100000元T2，测试供应商有限公司已出具应收账款转让通知书，对应的发票号及金额为1111/50000，5555/50000，到期日2018-12-29。付款方万科企业股份有限公司已出具编号为ZB00002-20181226-1204的付款确认及授权书'\\n , 'addDebtorList': [{'debtorType': '企业', 'debtorName': '测试供应商有限公司',\\n 'orgCode': '9144030068375453XL', 'businessCode': '9144030068375453XL',\\n 'lei': '#*¥#*（&¥#（*&¥（）', 'responsiblePerson': '测试法人1', 'country': '中国',\\n 'province': '黑龙江省', 'city': '哈尔滨市', 'address': '北京天安门'}]}\\ndata = json.dumps(input)\\nheaders = {'User-Agent':\\n 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'\\n , 'Content-Type': 'application/json'}\\nresponse = requests.post(url, data=data, headers=headers, timeout=(500, 500))\\nprint(response.text)\\n\",\n \"<import token>\\nurl = 'http://39.108.188.34:9090/spider/zhongdengdengji.go'\\ninput = {'timelimit': '1年', 'title': 'GD20190305001', 'maincontractno':\\n 'YT20181228001', 'maincontractcurrency': '人民币', 'maincontractsum':\\n '100000', 'description':\\n 'Y0181228001测试供应商有限公司与测试项目有限公司就SW00002-20181226-1204，转让应收账款金额100000元T2，测试供应商有限公司已出具应收账款转让通知书，对应的发票号及金额为1111/50000，5555/50000，到期日2018-12-29。付款方万科企业股份有限公司已出具编号为ZB00002-20181226-1204的付款确认及授权书'\\n , 'addDebtorList': [{'debtorType': '企业', 'debtorName': '测试供应商有限公司',\\n 'orgCode': '9144030068375453XL', 'businessCode': '9144030068375453XL',\\n 'lei': '#*¥#*（&¥#（*&¥（）', 'responsiblePerson': '测试法人1', 'country': '中国',\\n 'province': '黑龙江省', 'city': '哈尔滨市', 'address': '北京天安门'}]}\\ndata = json.dumps(input)\\nheaders = {'User-Agent':\\n 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'\\n , 'Content-Type': 'application/json'}\\nresponse = requests.post(url, data=data, headers=headers, timeout=(500, 500))\\nprint(response.text)\\n\",\n \"<import token>\\n<assignment token>\\nprint(response.text)\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7593,"cells":{"index":{"kind":"number","value":7593,"string":"7,593"},"blob_id":{"kind":"string","value":"f51d85ff352d9c84a8ded29ad94b24ca6dda46ad"},"code":{"kind":"string","value":"\n'''\nIplNorm.py\nDescription: \n Normalizing 0 - 255 initial fingerprint to a normalized image.\n Using energy normalization.\n \n Input:\n -image\n \n Output:\n -norm_im\n@author: Edoardo Foco\n'''\n\nimport cv2\nimport numpy as np\n\ndef normalise(image):\n \n dbl_image = image.astype(float)\n # calculate the mean of the image.\n mean = np.mean(dbl_image)\n \n # converting numpy 8-bit image to 8- bit cv2.iplimage\n iplImage = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]), cv2.cv.IPL_DEPTH_8U, 1)\n cv2.cv.SetData(iplImage, image.tostring(), image.dtype.itemsize * 1 * image.shape[1])\n \n # initializing 32-bit floating point iplimage\n image_32F = cv2.cv.CreateImage(cv2.cv.GetSize(iplImage), cv2.cv.IPL_DEPTH_32F,1)\n \n # converting 8-bit unsigned integer image to 32-bit floating point image\n cv2.cv.CvtScale(iplImage,image_32F)\n \n # energy Normalization. Formula: image = image/mean(image)\n cv2.cv.ConvertScale(image_32F, image_32F, (1/mean), 0);\n \n # re-converting to numpy image\n norm_im = np.asarray(image_32F[:,:])\n \n return norm_im"},"steps":{"kind":"list like","value":["\n'''\nIplNorm.py\nDescription: \n Normalizing 0 - 255 initial fingerprint to a normalized image.\n Using energy normalization.\n \n Input:\n -image\n \n Output:\n -norm_im\n@author: Edoardo Foco\n'''\n\nimport cv2\nimport numpy as np\n\ndef normalise(image):\n \n dbl_image = image.astype(float)\n # calculate the mean of the image.\n mean = np.mean(dbl_image)\n \n # converting numpy 8-bit image to 8- bit cv2.iplimage\n iplImage = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]), cv2.cv.IPL_DEPTH_8U, 1)\n cv2.cv.SetData(iplImage, image.tostring(), image.dtype.itemsize * 1 * image.shape[1])\n \n # initializing 32-bit floating point iplimage\n image_32F = cv2.cv.CreateImage(cv2.cv.GetSize(iplImage), cv2.cv.IPL_DEPTH_32F,1)\n \n # converting 8-bit unsigned integer image to 32-bit floating point image\n cv2.cv.CvtScale(iplImage,image_32F)\n \n # energy Normalization. Formula: image = image/mean(image)\n cv2.cv.ConvertScale(image_32F, image_32F, (1/mean), 0);\n \n # re-converting to numpy image\n norm_im = np.asarray(image_32F[:,:])\n \n return norm_im","<docstring token>\nimport cv2\nimport numpy as np\n\n\ndef normalise(image):\n dbl_image = image.astype(float)\n mean = np.mean(dbl_image)\n iplImage = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]),\n cv2.cv.IPL_DEPTH_8U, 1)\n cv2.cv.SetData(iplImage, image.tostring(), image.dtype.itemsize * 1 *\n image.shape[1])\n image_32F = cv2.cv.CreateImage(cv2.cv.GetSize(iplImage), cv2.cv.\n IPL_DEPTH_32F, 1)\n cv2.cv.CvtScale(iplImage, image_32F)\n cv2.cv.ConvertScale(image_32F, image_32F, 1 / mean, 0)\n norm_im = np.asarray(image_32F[:, :])\n return norm_im\n","<docstring token>\n<import token>\n\n\ndef normalise(image):\n dbl_image = image.astype(float)\n mean = np.mean(dbl_image)\n iplImage = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]),\n cv2.cv.IPL_DEPTH_8U, 1)\n cv2.cv.SetData(iplImage, image.tostring(), image.dtype.itemsize * 1 *\n image.shape[1])\n image_32F = cv2.cv.CreateImage(cv2.cv.GetSize(iplImage), cv2.cv.\n IPL_DEPTH_32F, 1)\n cv2.cv.CvtScale(iplImage, image_32F)\n cv2.cv.ConvertScale(image_32F, image_32F, 1 / mean, 0)\n norm_im = np.asarray(image_32F[:, :])\n return norm_im\n","<docstring token>\n<import token>\n<function token>\n"],"string":"[\n \"\\n'''\\nIplNorm.py\\nDescription: \\n Normalizing 0 - 255 initial fingerprint to a normalized image.\\n Using energy normalization.\\n \\n Input:\\n -image\\n \\n Output:\\n -norm_im\\n@author: Edoardo Foco\\n'''\\n\\nimport cv2\\nimport numpy as np\\n\\ndef normalise(image):\\n \\n dbl_image = image.astype(float)\\n # calculate the mean of the image.\\n mean = np.mean(dbl_image)\\n \\n # converting numpy 8-bit image to 8- bit cv2.iplimage\\n iplImage = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]), cv2.cv.IPL_DEPTH_8U, 1)\\n cv2.cv.SetData(iplImage, image.tostring(), image.dtype.itemsize * 1 * image.shape[1])\\n \\n # initializing 32-bit floating point iplimage\\n image_32F = cv2.cv.CreateImage(cv2.cv.GetSize(iplImage), cv2.cv.IPL_DEPTH_32F,1)\\n \\n # converting 8-bit unsigned integer image to 32-bit floating point image\\n cv2.cv.CvtScale(iplImage,image_32F)\\n \\n # energy Normalization. Formula: image = image/mean(image)\\n cv2.cv.ConvertScale(image_32F, image_32F, (1/mean), 0);\\n \\n # re-converting to numpy image\\n norm_im = np.asarray(image_32F[:,:])\\n \\n return norm_im\",\n \"<docstring token>\\nimport cv2\\nimport numpy as np\\n\\n\\ndef normalise(image):\\n dbl_image = image.astype(float)\\n mean = np.mean(dbl_image)\\n iplImage = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]),\\n cv2.cv.IPL_DEPTH_8U, 1)\\n cv2.cv.SetData(iplImage, image.tostring(), image.dtype.itemsize * 1 *\\n image.shape[1])\\n image_32F = cv2.cv.CreateImage(cv2.cv.GetSize(iplImage), cv2.cv.\\n IPL_DEPTH_32F, 1)\\n cv2.cv.CvtScale(iplImage, image_32F)\\n cv2.cv.ConvertScale(image_32F, image_32F, 1 / mean, 0)\\n norm_im = np.asarray(image_32F[:, :])\\n return norm_im\\n\",\n \"<docstring token>\\n<import token>\\n\\n\\ndef normalise(image):\\n dbl_image = image.astype(float)\\n mean = np.mean(dbl_image)\\n iplImage = cv2.cv.CreateImageHeader((image.shape[1], image.shape[0]),\\n cv2.cv.IPL_DEPTH_8U, 1)\\n cv2.cv.SetData(iplImage, image.tostring(), image.dtype.itemsize * 1 *\\n image.shape[1])\\n image_32F = cv2.cv.CreateImage(cv2.cv.GetSize(iplImage), cv2.cv.\\n IPL_DEPTH_32F, 1)\\n cv2.cv.CvtScale(iplImage, image_32F)\\n cv2.cv.ConvertScale(image_32F, image_32F, 1 / mean, 0)\\n norm_im = np.asarray(image_32F[:, :])\\n return norm_im\\n\",\n \"<docstring token>\\n<import token>\\n<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7594,"cells":{"index":{"kind":"number","value":7594,"string":"7,594"},"blob_id":{"kind":"string","value":"9fd73e0a1dacc46c177f11ce4cf2351b3d622c0d"},"code":{"kind":"string","value":"# Improting Image class from PIL module\nfrom PIL import Image\n\n# Opens a image in RGB mode\nim = Image.open(\"data/frame1.jpg\")\n\n# Setting the points for cropped image\nleft = 155\ntop = 65\nright = 360\nbottom = 270\n\n# Cropped image of above dimension\n# (It will not change orginal image)\nim1 = im.crop((left, top, right, bottom))\n\n# Shows the image in image viewer\nim1.show()\nim.show()\n"},"steps":{"kind":"list like","value":["# Improting Image class from PIL module\nfrom PIL import Image\n\n# Opens a image in RGB mode\nim = Image.open(\"data/frame1.jpg\")\n\n# Setting the points for cropped image\nleft = 155\ntop = 65\nright = 360\nbottom = 270\n\n# Cropped image of above dimension\n# (It will not change orginal image)\nim1 = im.crop((left, top, right, bottom))\n\n# Shows the image in image viewer\nim1.show()\nim.show()\n","from PIL import Image\nim = Image.open('data/frame1.jpg')\nleft = 155\ntop = 65\nright = 360\nbottom = 270\nim1 = im.crop((left, top, right, bottom))\nim1.show()\nim.show()\n","<import token>\nim = Image.open('data/frame1.jpg')\nleft = 155\ntop = 65\nright = 360\nbottom = 270\nim1 = im.crop((left, top, right, bottom))\nim1.show()\nim.show()\n","<import token>\n<assignment token>\nim1.show()\nim.show()\n","<import token>\n<assignment token>\n<code token>\n"],"string":"[\n \"# Improting Image class from PIL module\\nfrom PIL import Image\\n\\n# Opens a image in RGB mode\\nim = Image.open(\\\"data/frame1.jpg\\\")\\n\\n# Setting the points for cropped image\\nleft = 155\\ntop = 65\\nright = 360\\nbottom = 270\\n\\n# Cropped image of above dimension\\n# (It will not change orginal image)\\nim1 = im.crop((left, top, right, bottom))\\n\\n# Shows the image in image viewer\\nim1.show()\\nim.show()\\n\",\n \"from PIL import Image\\nim = Image.open('data/frame1.jpg')\\nleft = 155\\ntop = 65\\nright = 360\\nbottom = 270\\nim1 = im.crop((left, top, right, bottom))\\nim1.show()\\nim.show()\\n\",\n \"<import token>\\nim = Image.open('data/frame1.jpg')\\nleft = 155\\ntop = 65\\nright = 360\\nbottom = 270\\nim1 = im.crop((left, top, right, bottom))\\nim1.show()\\nim.show()\\n\",\n \"<import token>\\n<assignment token>\\nim1.show()\\nim.show()\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7595,"cells":{"index":{"kind":"number","value":7595,"string":"7,595"},"blob_id":{"kind":"string","value":"d7570bbea1e8c7674d507f8e86ce04d22058b21b"},"code":{"kind":"string","value":"class Solution:\n def numIdenticalPairs(self, nums: List[int]) -> int:\n count = 0\n inputLength = len(nums)\n for i in range (0, inputLength):\n for j in range (i, inputLength - 1):\n if (nums[i] == nums[j + 1]): count += 1\n return count"},"steps":{"kind":"list like","value":["class Solution:\n def numIdenticalPairs(self, nums: List[int]) -> int:\n count = 0\n inputLength = len(nums)\n for i in range (0, inputLength):\n for j in range (i, inputLength - 1):\n if (nums[i] == nums[j + 1]): count += 1\n return count","class Solution:\n\n def numIdenticalPairs(self, nums: List[int]) ->int:\n count = 0\n inputLength = len(nums)\n for i in range(0, inputLength):\n for j in range(i, inputLength - 1):\n if nums[i] == nums[j + 1]:\n count += 1\n return count\n","class Solution:\n <function token>\n","<class token>\n"],"string":"[\n \"class Solution:\\n def numIdenticalPairs(self, nums: List[int]) -> int:\\n count = 0\\n inputLength = len(nums)\\n for i in range (0, inputLength):\\n for j in range (i, inputLength - 1):\\n if (nums[i] == nums[j + 1]): count += 1\\n return count\",\n \"class Solution:\\n\\n def numIdenticalPairs(self, nums: List[int]) ->int:\\n count = 0\\n inputLength = len(nums)\\n for i in range(0, inputLength):\\n for j in range(i, inputLength - 1):\\n if nums[i] == nums[j + 1]:\\n count += 1\\n return count\\n\",\n \"class Solution:\\n <function token>\\n\",\n \"<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7596,"cells":{"index":{"kind":"number","value":7596,"string":"7,596"},"blob_id":{"kind":"string","value":"ef7fad5019e79950e8fad56404e9ba5d302cfe1c"},"code":{"kind":"string","value":"\r\ndef convertEnEntier(nombre):\r\n\r\n result = \"\";\r\n if (nombre == 4):\r\n result = \"IV\"\r\n if (nombre == 3):\r\n result = \"III\"\r\n if (nombre == 2):\r\n result = \"II\"\r\n if (nombre == 1):\r\n result = \"I\"\r\n\r\n return result\r\n\r\n\r\nprint (convertEnEntier(1))\r\nprint (convertEnEntier(2))\r\nprint (convertEnEntier(3))\r\n\r\n"},"steps":{"kind":"list like","value":["\r\ndef convertEnEntier(nombre):\r\n\r\n result = \"\";\r\n if (nombre == 4):\r\n result = \"IV\"\r\n if (nombre == 3):\r\n result = \"III\"\r\n if (nombre == 2):\r\n result = \"II\"\r\n if (nombre == 1):\r\n result = \"I\"\r\n\r\n return result\r\n\r\n\r\nprint (convertEnEntier(1))\r\nprint (convertEnEntier(2))\r\nprint (convertEnEntier(3))\r\n\r\n","def convertEnEntier(nombre):\n result = ''\n if nombre == 4:\n result = 'IV'\n if nombre == 3:\n result = 'III'\n if nombre == 2:\n result = 'II'\n if nombre == 1:\n result = 'I'\n return result\n\n\nprint(convertEnEntier(1))\nprint(convertEnEntier(2))\nprint(convertEnEntier(3))\n","def convertEnEntier(nombre):\n result = ''\n if nombre == 4:\n result = 'IV'\n if nombre == 3:\n result = 'III'\n if nombre == 2:\n result = 'II'\n if nombre == 1:\n result = 'I'\n return result\n\n\n<code token>\n","<function token>\n<code token>\n"],"string":"[\n \"\\r\\ndef convertEnEntier(nombre):\\r\\n\\r\\n result = \\\"\\\";\\r\\n if (nombre == 4):\\r\\n result = \\\"IV\\\"\\r\\n if (nombre == 3):\\r\\n result = \\\"III\\\"\\r\\n if (nombre == 2):\\r\\n result = \\\"II\\\"\\r\\n if (nombre == 1):\\r\\n result = \\\"I\\\"\\r\\n\\r\\n return result\\r\\n\\r\\n\\r\\nprint (convertEnEntier(1))\\r\\nprint (convertEnEntier(2))\\r\\nprint (convertEnEntier(3))\\r\\n\\r\\n\",\n \"def convertEnEntier(nombre):\\n result = ''\\n if nombre == 4:\\n result = 'IV'\\n if nombre == 3:\\n result = 'III'\\n if nombre == 2:\\n result = 'II'\\n if nombre == 1:\\n result = 'I'\\n return result\\n\\n\\nprint(convertEnEntier(1))\\nprint(convertEnEntier(2))\\nprint(convertEnEntier(3))\\n\",\n \"def convertEnEntier(nombre):\\n result = ''\\n if nombre == 4:\\n result = 'IV'\\n if nombre == 3:\\n result = 'III'\\n if nombre == 2:\\n result = 'II'\\n if nombre == 1:\\n result = 'I'\\n return result\\n\\n\\n<code token>\\n\",\n \"<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7597,"cells":{"index":{"kind":"number","value":7597,"string":"7,597"},"blob_id":{"kind":"string","value":"8b0eed6d1f24b5dd30726ce08c97354a5d5ab69b"},"code":{"kind":"string","value":"# Generated by Django 2.1.2 on 2018-10-25 09:36\n\nimport django.contrib.auth.models\nimport django.contrib.auth.validators\nfrom django.db import migrations, models\nimport django.utils.timezone\nimport uuid\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('grafit', '0002_article'),\n ]\n\n operations = [\n migrations.RunSQL(\"\"\"\n INSERT INTO grafit_article (id, title, text) VALUES (2, 'MongoDB', 'MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemata. MongoDB is developed by MongoDB Inc., and is published under a combination of the Server Side Public License and the Apache License.\n 10gen software company began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[6]\n\n On October 20, 2017, MongoDB became a publicly-traded company, listed on NASDAQ as MDB with an IPO price of $24 per share.[7] Ad hoc queries\n\n MongoDB supports field, range query, and regular expression searches.[8] Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size.\n Indexing\n\n Fields in a MongoDB document can be indexed with primary and secondary indices.\n Replication\n\n MongoDB provides high availability with replica sets.[9] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.\n Load balancing[10]\n\n MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.\n\n MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. ');\n INSERT INTO grafit_article (id, title, text) VALUES (3, 'NoSQL', 'A NoSQL (originally referring to \"non SQL\" or \"non relational\")[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the \"NoSQL\" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called \"Not only SQL\" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.[7][8]\n\n Motivations for this approach include: simplicity of design, simpler \"horizontal\" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as \"more flexible\" than relational database tables.[9]\n\n Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)\n\n Instead, most NoSQL databases offer a concept of \"eventual consistency\" in which database changes are propagated to all nodes \"eventually\" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases \"do not allow referential integrity constraints to span databases.\"[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing. ');\n INSERT INTO grafit_article (id, title, text) VALUES (4, 'SQL', 'SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd[15] in the early 1970s.[16] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM''s original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s.[16]\n\n Chamberlin and Boyce''s first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973, they began work on SEQUEL.[15] The acronym SEQUEL was later changed to SQL because \"SEQUEL\" was a trademark of the UK-based Hawker Siddeley aircraft company.[17]\n\n In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce, and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. By 1986, ANSI and ISO standard groups officially adopted the standard \"Database Language SQL\" language definition. New versions of the standard were published in 1989, 1992, 1996, 1999, 2003, 2006, 2008, 2011,[15] and most recently, 2016. After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.[18] ');\n INSERT INTO grafit_article (id, title, text) VALUES (5, 'MySQL', 'Built on MySQL Enterprise Edition and powered by the Oracle Cloud, Oracle MySQL Cloud Service provides a simple, automated, integrated and enterprise ready MySQL cloud service, enabling organizations to increase business agility and reduce costs. \"Relying on the MySQL engine as the low-level storage layer has allowed us to very quickly build a robust system.\"\n \n\n \"We have successfully implemented MySQL Cluster Carrier Grade Edition for our highly mission critical XDMS application which will enable the next generation of converged services.\"\n \n\n \"We found that MySQL was the best database in terms of the price-point and functionality it offers up. The benefits that MySQL brings to our Brightmail product is its relaiability, robustness and very low-cost administration costs.\"');\n INSERT INTO grafit_article (id, title, text) VALUES (6, 'Critical Flaw Reported In phpMyAdmin Lets Attackers Damage Databases', 'A critical security vulnerability has been reported in phpMyAdmin—one of the most popular applications for managing the MySQL database—which could allow remote attackers to perform dangerous database operations just by tricking administrators into clicking a link.\n\n Discovered by an Indian security researcher, Ashutosh Barot, the vulnerability is a cross-site request forgery (CSRF) attack and affects phpMyAdmin versions 4.7.x (prior to 4.7.7).\n\n Cross-site request forgery vulnerability, also known as XSRF, is an attack wherein an attacker tricks an authenticated user into executing an unwanted action.\n\n According to an advisory released by phpMyAdmin, \"by deceiving a user to click on a crafted URL, it is possible to perform harmful database operations such as deleting records, dropping/truncating tables, etc.\"\n\n phpMyAdmin is a free and open source administration tool for MySQL and MariaDB and is widely used to manage the database for websites created with WordPress, Joomla, and many other content management platforms.\n\n Moreover, a lot of hosting providers use phpMyAdmin to offer their customers a convenient way to organize their databases.\n Barot has also released a video, as shown above, demonstrating how a remote attacker can make database admins unknowingly delete (DROP) an entire table from the database just by tricking them into clicking a specially crafted link.\n\n \"A feature of phpMyAdmin was using a GET request and after that POST request for Database operations such as DROP TABLE table_name; GET requests must be protected against CSRF attacks. In this case, POST requests were used which were sent through URL (for bookmarking purpose may be); it was possible for an attacker to trick a database admin into clicking a button and perform a drop table database query of the attacker’s choice.\" Barot explains in a blog post.\n\n However, performing this attack is not simple as it may sound. To prepare a CSRF attack URL, the attacker should be aware of the name of targeted database and table.\n\n \"If a user executes a query on the database by clicking insert, DROP, etc. buttons, the URL will contain database name and table name,\" Barot says. \"This vulnerability can result in the disclosure of sensitive information as the URL is stored at various places such as browser history, SIEM logs, Firewall Logs, ISP Logs, etc.\"\n\n Barot reported the vulnerability to phpMyAdmin developers, who confirmed his finding and released phpMyAdmin 4.7.7 to address this issue. So administrators are highly recommended to update their installations as soon as possible.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (25, 'Death By Database', 'The following is a true story, but with names changed.\n\n When I work with clients to build software, I take the usual steps of understanding their needs, gathering requirements, learning about their customers, and so on. At this point I have a model on paper of roughly what the software is intended to do, so they get surprised when I immediately turn to database design.\n\n \"Who care about database design? What about mockups? What about workflows?\"\n\n Let me tell you about \"Bob''s Luxury Goods.\" I worked for this company many years ago and they had a retail store selling ... you guessed it ... luxury goods. They''d ask all customers for a billing address and if they had a different delivery address. At the database level, they had a \"one-to-many\" relationship between customers and addresses.\n\n That was their first problem. A customer''s partner might come into Bob''s and order something and if the address was entered correctly it would be flagged as \"in use\" and we had to use a different address or deliberately enter a typo. Fortunately, addresses were case-sensitive, so many people had UPPER-CASE ADDRESSES.\n\n We should have had a many-to-many relationship between customers and addresses so we could handle the case where more than one person would share the same address, but we didn''t. Further, I was never allocated the time to fix the database because it was \"cheaper\" to remove the restriction on \"flagged\" addresses and allow a duplicate address to be used.\n\n Naturally, being a luxury goods company, we had many repeat customers and sometimes they would move and if we didn''t find the duplicate address, or the address with the \"typo\", we might update the address for one partner, but not the other. That was a headache, but it didn''t happen frequently enough for management to worry about it.\n\n That''s when the marketing department had a brilliant, inexpensive idea. You see, we periodically did mass mailings of special events to our customers. Since we had the software to do mass mailings, why not import a mailing list of all addresses in high net worth areas and mail everyone about upcoming special events? So the company went ahead and bought a database with all of these addresses, but forgot to mention to me that I was supposed to implement this.\n\n Except that every address record had the customer id embedded in it, so we couldn''t enter an address without a customer.\n\n \"Curtis,\" they said, \"just enter a dummy customer called ''Occupant'' and attach all addresses to that.\"\n\n Except you couldn''t enter a customer without an order.\n\n Except you couldn''t enter an order without at least one item on it.\n\n Except you couldn''t enter an item unless it was listed in inventory.\n\n Except that reserved the \"inventory\" item and made it unavailable.\n\n Except, except, except ...\n\n It came down to trying to create a fake customer, with a fake order, with a fake item, with a fake item category, with a \"paid\" invoice, with exceptions sprinkled throughout the codebase to handle all of these special cases and probably more that I no longer remember.\n\n Then, and only then, could I write the code to provide \"generic\" mass mailings. Management decided it was easier to hire an outside company to handle the mailing list for them.\n\n If they had simply had a proper database design up front, they could have reused their existing system with little trouble.\n\n That''s what bad database design costs you and why I usually start with that before writing my software.\n\n Note: if you''re not familiar with database design, here''s a talk I give where I make it fairly simple to understand. I mostly avoid big words.');\n INSERT INTO grafit_article (id, title, text) VALUES (33, 'GitHub Actions: built by you, run by us', 'Yesterday at GitHub Universe, we announced GitHub Actions, a new way to automate and customize your workflows. Configuring the apps and services that make up your development cycle takes significant time and effort. GitHub Actions applies open source principles to workflow automation, weaving together the tools you use from idea to production into one complete workflow. You can also create, share, and discover any actions your projects require, just as you would create, share, and discover code on GitHub.\n\n Learn more about actions\n\n As we prepared for Universe, we shared GitHub Actions with a group of customers, integrators, and open source maintainers to see what they could do. In just a few short weeks, talented teams and individuals alike have created hundreds of GitHub Actions. During today’s Universe keynote, we heard directly from developers, and we’re excited to share their work with you');\n INSERT INTO grafit_article (id, title, text) VALUES (34, 'Git Submodule Vulnerability Announced ', '\n\n The Git project has disclosed CVE-2018-17456, a vulnerability in Git that can cause arbitrary code to be executed when a user clones a malicious repository. Git v2.19.1 has been released with a fix, along with backports in v2.14.5, v2.15.3, v2.16.5, v2.17.2, and v2.18.1. We encourage all users to update their clients to protect themselves.\n\n Until you’ve updated, you can protect yourself by avoiding submodules from untrusted repositories. This includes commands such as git clone --recurse-submodules and git submodule update.\n Affected products\n GitHub Desktop\n\n GitHub Desktop versions 1.4.1 and older included an embedded version of Git that was affected by this vulnerability. We encourage all GitHub Desktop users to update to the newest version (1.4.2 and 1.4.3-beta0) available today in the Desktop app.\n Atom\n\n Atom included the same embedded Git and was also affected. Releases 1.31.2 and 1.32.0-beta3 include the patch.\n\n Ensure you’re on the latest Atom release by completing any of the following:\n\n Windows: From the toolbar, click Help -> Check for Updates\n MacOS: From the menu bar, click Atom -> Check for Update\n Linux: Update manually by downloading the latest release from atom.io\n\n Git on the command line and other clients\n\n In order to be protected from the vulnerability, you must update your command-line version of Git, and any other application that may include an embedded version of Git, as they are independent of each other.\n Additional notes\n\n Neither GitHub.com nor GitHub Enterprise are directly affected by the vulnerability. However, as with previously discovered vulnerabilities, GitHub.com will detect malicious repositories, and will reject pushes or API requests attempting to create them. Versions of GitHub Enterprise with this detection will ship on October 9.\n Details of the vulnerability\n\n This vulnerability is very similar to CVE-2017-1000117, as both are option-injection attacks related to submodules. In the earlier attack, a malicious repository would ship a .gitmodules file pointing one of its submodules to a remote repository with an SSH host starting with a dash (-). The ssh program—spawned by Git—would then interpret that as an option. This attack works in a similar way, except that the option-injection is against the child git clone itself.\n\n The problem was reported on September 23 by @joernchen, both to Git’s private security list, as well as to GitHub’s Bug Bounty program. Developers at GitHub worked with the Git community to develop a fix.\n\n The basic fix was clear from the report. However, due to to the similarity to CVE-2017-1000117, we also audited all of the .gitmodules values and implemented stricter checks as appropriate. These checks should prevent a similar vulnerability in another code path. We also implemented detection of potentially malicious submodules as part of Git’s object quality checks (which was made much easier by the infrastructure added during the last submodule-related vulnerability).\n\n The coordinated disclosure date of October 5 was selected by Git developers to allow packagers to prepare for the release. This also provided hosting sites (with custom implementations) ample time to detect and block the attack before it became public. Members of the Git community checked the JGit and libgit2 implementations. Those are not affected by the vulnerability because they clone submodules via function calls rather than separate commands.\n\n We were also able to use the time to scan all repositories on GitHub for evidence of the attack being used in the wild. We’re happy to report that no instances were found (and now, with our detection, none can be added).\n\n Please update your copy of Git soon, and happy cloning!\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (21, 'Hackers Targeting Servers Running Database Services for Mining Cryptocurrency', 'Security researchers have discovered multiple attack campaigns conducted by an established Chinese criminal group that operates worldwide, targeting database servers for mining cryptocurrencies, exfiltrating sensitive data and building a DDoS botnet.\n\n The researchers from security firm GuardiCore Labs have analyzed thousands of attacks launched in recent months and identified at least three attack variants—Hex, Hanako, and Taylor—targeting different MS SQL and MySQL servers for both Windows and Linux.\n\n The goals of all the three variants are different—Hex installs cryptocurrency miners and remote access trojans (RATs) on infected machines, Taylor installs a keylogger and a backdoor, and Hanako uses infected devices to build a DDoS botnet.\n\n So far, researchers have recorded hundreds of Hex and Hanako attacks and tens of thousands of Taylor attacks each month and found that most compromised machines are based in China, and some in Thailand, the United States, Japan and others.\n\n To gain unauthorized access to the targeted database servers, the attackers use brute force attacks and then run a series of predefined SQL commands to gain persistent access and evade audit logs.\n\n What''s interesting? To launch the attacks against database servers and serve malicious files, attackers use a network of already compromised systems, making their attack infrastructure modular and preventing takedown of their malicious activities.');\n INSERT INTO grafit_article (id, title, text) VALUES (22, 'RIP Open Source MySQL', ' This is an excellent opportunity for the Postgres community to step up an promote Postgres.\n\n \n \n rbanffy on Aug 18, 2012 [-]\n\n I think this would be a mistake.\n\n This is an excellent opportunity to demonstrate that anyone can fork the MySQL codebase and create other plug-in replacement databases with it, such as MariaDB and Drizzle.\n\n All that is lost is the MySQL name and brand.\n\n PostgreSQL users and developers must seize the opportunity to show businesses that free software cannot be killed, not even by mighty Oracle. They and, most notably, Microsoft, have been trying to kill it for more than a decade now.\n\n Because the anti-free-software FUD machine (fed in part by Oracle itself) is already having a wonderful time with this.\n\n \n \n Udo on Aug 18, 2012 [-]\n\n I wish I could mod this up a hundred times. PostgreSQL people themselves have been playing into the hands of corporate FUDders with their incessant and inappropriate peddling. MySQL is not your enemy, MS SQL Server is. Oracle''s software empire as a whole certainly is your enemy. Show some solidarity with a fellow open source project!\n\n MySQL and PostgreSQL represent two very different implementation philosophies, and being able to choose between them according to taste and merits is a good thing.\n\n Most of us have suspected that the MySQL project itself was going to die as it was acquired by Oracle, in the same way Open Office died when it was acquired by Oracle. This is a company where good software goes to expire, either due to a deliberate intention or gross incompetence I can''t say but I suspect it''s a mixture of both. However sad that may be for the MySQL (or OpenOffice) brand name, the code itself lives on and continues to evolve within a rich open source ecosystem.\n\n Hence, sensational and petulant \"RIP $PRODUCTNAME\" articles are unnecessary. There is no threat to existing projects based on MySQL or any other successful open source project for that matter. Not only will this stuff be free forever, it will also continue to grow and be developed on its own.\n\n The corporate assassination of open source projects will only work if we let it, it''s a purely psychological game. ');\n INSERT INTO grafit_article (id, title, text) VALUES (23, 'Free Text Sources', 'There are a few interesting things to talk about surrounding free and open textbooks. Quality is one. Usability is another. Why to write one (and/or, why not) is certainly critical. But where can you find these disruptive, open texts?\n\n Not all faculty know there are free and open texts they can use; finding free and/or open textbooks (or even knowing to look) can sometimes be a trick. I knew about one or two sources, and did a little bit more digging. Admittedly, many of the sources of free texts linked below have a technical bent. On one hand, this might be because math, computing, and the sciences are familiar with working openly and giving things away. On the other, it might be because I am a member of the computing faculty, and therefore am most familiar with resources in that space.');\n INSERT INTO grafit_article (id, title, text) VALUES (24, 'Apache Software Foundation Public Mail Archives', 'A collection of all publicly available mail archives from the Apache55 Software Foundation (ASF), taken on July 11, 2011. This collection contains all publicly available email archives from the ASF''s 80+ projects (http://mail-archives.apache.org/mod_mbox/), including mailing lists such as Apache HTTPD Server, Apache Tomcat, Apache Lucene and Solr, Apache Hadoop and many more. Generally speaking, most projects have at least three lists: user, dev and commits, but some have more, some have less. The user lists are where users of the software ask questions on usage, while the dev list usually contains discussions on the development of the project (code, releases, etc.) The commit lists usually consists of automated notifications sent by the various ASF version control tools, like Subversion or CVS, and contain information about changes made to the project''s source code.\n\n Both tarballs and per project sets are available in the snapshot. The tarballs are organized according to project name. Thus, a-d.tar.gz contains all ASF projects that begin with the letters a, b, c or d, such as abdera.apache.org. Files within the project are usually gzipped mbox files.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (26, 'PostgreSQL - Overview', 'PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development phase and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness.\n\n This tutorial will give you a quick start with PostgreSQL and make you comfortable with PostgreSQL programming.\n What is PostgreSQL?\n\n PostgreSQL (pronounced as post-gress-Q-L) is an open source relational database management system (DBMS) developed by a worldwide team of volunteers. PostgreSQL is not controlled by any corporation or other private entity and the source code is available free of charge.\n A Brief History of PostgreSQL\n\n PostgreSQL, originally called Postgres, was created at UCB by a computer science professor named Michael Stonebraker. Stonebraker started Postgres in 1986 as a follow-up project to its predecessor, Ingres, now owned by Computer Associates.\n\n 1977-1985 − A project called INGRES was developed.\n\n Proof-of-concept for relational databases\n\n Established the company Ingres in 1980\n\n Bought by Computer Associates in 1994\n\n 1986-1994 − POSTGRES\n\n Development of the concepts in INGRES with a focus on object orientation and the query language - Quel\n\n The code base of INGRES was not used as a basis for POSTGRES\n\n Commercialized as Illustra (bought by Informix, bought by IBM)\n\n 1994-1995 − Postgres95\n\n Support for SQL was added in 1994\n\n Released as Postgres95 in 1995\n\n Re-released as PostgreSQL 6.0 in 1996\n\n Establishment of the PostgreSQL Global Development Team\n\n Key Features of PostgreSQL\n\n PostgreSQL runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It supports text, images, sounds, and video, and includes programming interfaces for C / C++, Java, Perl, Python, Ruby, Tcl and Open Database Connectivity (ODBC).\n\n PostgreSQL supports a large part of the SQL standard and offers many modern features including the following −\n\n Complex SQL queries\n SQL Sub-selects\n Foreign keys\n Trigger\n Views\n Transactions\n Multiversion concurrency control (MVCC)\n Streaming Replication (as of 9.0)\n Hot Standby (as of 9.0)\n\n You can check official documentation of PostgreSQL to understand the above-mentioned features. PostgreSQL can be extended by the user in many ways. For example by adding new −\n\n Data types\n Functions\n Operators\n Aggregate functions\n Index methods\n\n Procedural Languages Support\n\n PostgreSQL supports four standard procedural languages, which allows the users to write their own code in any of the languages and it can be executed by PostgreSQL database server. These procedural languages are - PL/pgSQL, PL/Tcl, PL/Perl and PL/Python. Besides, other non-standard procedural languages like PL/PHP, PL/V8, PL/Ruby, PL/Java, etc., are also supported.');\n INSERT INTO grafit_article (id, title, text) VALUES (27, 'Setup PostgreSQL on Windows with Docker', 'Over the weekend I finally got the chance to start reading A Curious Moon by Rob Conery which is a book on learning PostgreSQL by following the fictional Dee Yan as she is thrown into database administrator role at an aerospace startup.\n\n I have a lot of experience using Microsoft’s SQL Server, but up until now, I haven’t touched PostgreSQL. For personal projects SQL Server’s cost and be prohibitive and the release of Rob’s book added up to a good time to give PostgreSQL a try.\n Install Directly or not?\n\n On the download section of the official Postgres site, there is an option to download an installer. This is the route I was going to at first, but in Rob’s book, he suggests using a VM for Postgres installation on Windows. This kicked off a lot of searching on my part and didn’t find a good definitive answer on why that is or isn’t the way to do.\n\n In the end, I decided to try and run the Postgres process using Docker instead installing directly on Windows or dealing with a full VM.\n Installing Docker\n\n Head to this link and click the Get Docker link to download the installer. After the install is complete you will have to log out and back in. When I logged back in I got a message about Hyper-V not being enabled.\n\n After logging back in I then got the following message about hardware-assisted virtualization not being enabled.\n\n After tweaking my BIOS settings and logging back in I was greeted by the Docker welcome screen.\n\n Open a command prompt and run the following command.\n\n docker run hello-world\n\n You should output that starts with the following if your installation is working.\n\n Hello from Docker!\n This message shows that your installation appears to be working correctly.\n\n What about Postgres?\n\n Getting up and going with a container running Postgres was pretty simple and could be done with the following command which will create a container and expose the port used by Postgres so it can be accessed from the host.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d postgres\n\n The problem with this approach is if you ever need to rebuild the container for some reason, like a new version of Postgres is released, your data will be lost. Thankfully I found this blog post which shows how to use a secondary container for the data leaving the Postgres container able to be destroyed and recreated as needed. The following is the command I used to create my data container.\n\n docker create -v /var/lib/postgresql/data --name PostgresData alpine\n\n The above creates a container named PostgresData based on the Alpine image. It is important that the -v parameter matches the path that Postgres expects.\n\n Now that we have a container that will keep our data safe let’s create the actual Postgres container with the following command.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d --volumes-from PostgresData postgres\n\n The only difference from the first example run command is the addition of –volumes-from PostgresData which tells the container to use the PostgresData container.\n\n If you run the docker ps -a command it will show you all your containers.\n\n As you can see in my example I have two containers only one of which is actually running. Make sure you don’t remove the data container just because it will never show as running.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (28, 'DIY: A PostgreSQL database server setup anyone can handle', 'When it comes to databases, I''m a fan of MySQL. The open source database can handle just about any load you want to throw at it, and it has lots of powerful tools that can be used to manage it.\n\n The other popular open source database is PostgreSQL, which is cross-platform and is used by numerous applications. Although PostgreSQL is often seen as being as powerful as MySQL, it doesn''t have nearly the number of available tools to make setup and management as easy as its competition. So I''ve written this handy PostgreSQL primer on how to get your database server up and running and ready to use. (Although PostgreSQL is cross-platform, I demonstrate the installation and setup on a Ubuntu 11.04 machine because it''s my platform of choice. The translation to other platforms should be simple.)\n Step 1: Install PostgreSQL\n\n Here are the installation steps on Ubuntu (this installation will also work on any Debian-based distribution):\n\n Open a terminal window.\n Issue the command sudo apt-get install postgresql.\n Type the sudo password necessary to give you admin rights and hit Enter.\n Allow apt to pick up any necessary dependencies.\n\n Once the installation is complete, it''s time to set this baby up.\n Step 2: Change the default user password\n\n Caution: If you don''t follow this step, you will not be able to add databases and administer PostgreSQL, and the database will not be secure.\n\n Here''s how to change the password for the default user. The user in question is postgres, and the password is changed like so:\n\n Open a terminal window.\n Issue the command sudo passwd postgres.\n Type (and confirm) that password to be used for this user.\n\n The postgres user will be the only user on your system that can open the PostgreSQL prompt without defining a database, which means postgres is the only user who can administer PostgreSQL. To test this, change to the postgres user with the command su - postgres and then enter the command psql. You should now be at the Postgres prompt, which looks like:\n\n postgres=#\n\n All other users have to gain access to the prompt like so:\n\n psql DB_NAME\n\n where DB_NAME is the name of an existing database.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (31, 'The Marketing Behind MongoDB', ' 100% of my friends who have used Mongo/similar NoSQL have given up and had a nasty rewrite back to pgSQL.\n\n This seems to be the journey:\n\n 1. Lack of migrations is awesome! We can iterate so quickly for MVP\n\n 2. Get users\n\n 3. Add features, still enjoying the speed of iteration\n\n 4. Get more users\n\n 5. Start building reporting features for enterprise/customer support/product metrics (ie: when the real potential success starts)\n\n 6. Realise you desperately need joins, transactions and other SQL features\n\n 7. Pause product dev for 1-3+ months to migrate back to SQL, or do some weird parallel development process to move it piecemeal back.\n\n I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n My thought is definitely yes.\n\n \n \n brandur on Aug 29, 2017 [-]\n\n > I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n I''ve used Postgres and Mongo pretty extensively, and for any reasonably seasoned developer, the startup overhead of an SQL system is a myth. There may upfront cost to learning how an RDMS and SQL work in the first place, but once you''re familiar with them, they''ll be faster than Mongo on any new project.\n\n The schemaless concept of a document database seems to be the major selling factor in velocity of movement, but once you''ve got a good handle on a migration framework in the vein of ActiveRecord or other popular software, that''s negated completely. It also really doesn''t take long before schemaless starts to cause big problems for you in terms of data consistency -- it''s not just the big players that get bitten by this.\n\n The simplified query language is another one. SQL is a little bit obtuse, but it''s not that bad once you have a handle on it, and a lot of people are familiar with it. Once you add in an ORM layer, the lazy-style access of a framework like Sequel or SQLAlchemy makes the developer experience quite a bit better than any Mongo APIs that I''ve seen. Also, after you get beyond trivial usage, SQL''s flexibility so wildly outstrips Mongo''s query documents that it''s not even worth talking about.\n\n Postgres on the other hand ships with a great management CLI, a very powerful REPL (psql), and features like data types/constraints/transactions that guarantee you correctness with zero effort on your part. I can only speak for myself, but I''d take Postgres to the hackathon any day of the week.\n\n \n \n martinald on Aug 29, 2017 [-]\n\n I totally agree with you, and started writing something about how understanding a good ORM takes nearly all the headache away.\n\n I think the thing people do find slow is a lot of ''documents within documents'' in SQL. It turns out this is usually a bad development pattern long term but it is super fast being able to just add docs inside docs with no configuration. It feels very slow writing foreign keys, navigation props and schemas for this in SQL vs JSON, where you can just dump your object in and you''re done.\n\n Basically; I think with noSQL you get some very short term gain for a lot of long term pain, and you''re right, ORMs and other tooling solves this mostly.\n\n I myself fell for this trap, and while it was a nightmare it actually matured me more as a professional more than anything I''ve ever done recently. Regardless of crazy hype, I don''t think I''ll ever fall for a solution so easily without evaluating it properly.\n\n I think I assumed the \"crowd\" had done the tech due diligence on this stuff and it definitely wasn''t the case. ');\n INSERT INTO grafit_article (id, title, text) VALUES (32, 'Countless NoSQL databases competed to be the database of choice', 'n 2013, 10gen — the company behind MongoDB — moved into a large 30,000 square foot office in Midtown Manhattan.\n\n The transfer into the former New York Times building capped off a tremendous period of growth: the database boasted 4 million downloads, the MongoDB User Groups already attracted 15,000 members, and ~10,000 people had attended a global event in 2012. Their offices were truly global from London to Sydney to Dublin and Barcelona — and a requisite west coast headquarters in Palo Alto.\n\n Despite the traction, many startups using MongoDB faced their own challenges. One part of MongoDB’s success among startups was because some didn''t critically assess 10gen’s marketing message.\n\n As engineers, we often discuss technical attacks (e.g., DDoS, Sybil attacks, security vulnerabilities), but need to spend time debating how to protect ourselves from marketing “attacks”.1 Today, developer marketing is subtle — third party blog posts, content marketing disguised as engineering lessons, biased talks, and sponsored hackathons — not clearly marked content from vendors. As such, startup engineering decisions can hinge on sources that are not impartial.\n\n A large amount of \"engineering\" content — even when written by engineers — is actually marketing, rather than thoughtful content whose aim is to help you make the best decision.\n\n Previously, we looked at the hype around NoSQL and common engineering mistakes to see how MongoDB became so successful. Now, let''s take a look into 10gen''s marketing strategy — as told by their employees.2\n\n 10gen’s marketing strategy is an increasingly common playbook and understanding it is useful for future developer tool decisions.');\n INSERT INTO grafit_article (id, title, text) VALUES (30, 'Comment Arrango', ' ArangoDB always makes for exciting benchmark posts.\n\n I could see myself there in a bowler hat with a fistful of racing chits screaming “go, Postgres, go.”\n\n I’d love to see a competition were the developers of each database got to use the same hardware and data then tune the hell out of their configs, queries, and indices.\n\n Red Bull could sponsor it. I’d buy a T-shirt.\n\n \n \n kbenson 8 months ago [-]\n\n That doesn''t sound that hard to start. Something like RealWorld[1] and the Web Framework Benchmarks[2] combined but for DB workloads. Have one dataset that includes data amenable to OLAP and OLTP, but have separate tests each consisting of OLAP queries, OLTP queries, and combined queries. Choose a low-end, mid-range and high-end set of AWS or GCE instances/configs to normalize against. Let people submit pull requests with new technologies or configs.\n\n You''d want to get some funding to run the tests (or maybe solicit Google or Amazon to see if you could get the instance time donated once a month or something.\n\n If you started small, with maybe a portion of these features, and then scaled up over time, you might actually get to the point where you had tests that emulated a power failure, or master/slave and dual master scenarios and how they handle certain common network errors (split-brain). That would be an amazing resource.\n\n Edit: It occurs to me I probably should have read more of the article, since this is sort of what they are doing already...\n\n 1: https://github.com/gothinkster/realworld\n\n 2: https://www.techempower.com/benchmarks/\n\n \n \n etxm 8 months ago [-]\n\n Yeah after I posted it I started thinking about what it would take and what that would actually look like... and how you’d cheat :)\n\n It would probably require a few different categories with some sort of output assertion to validate the query performed right and a means of tracking CPU, usage ram usage, and execution time.\n\n It would be cool to see things like disaster recovery and chaos proofing as well. ');\n INSERT INTO grafit_article (id, title, text) VALUES (35, 'Applying machine intelligence to GitHub security alerts ', 'Last year, we released security alerts that track security vulnerabilities in Ruby and JavaScript packages. Since then, we’ve identified more than four million of these vulnerabilities and added support for Python. In our launch post, we mentioned that all vulnerabilities with CVE IDs are included in security alerts, but sometimes there are vulnerabilities that are not disclosed in the National Vulnerability Database. Fortunately, our collection of security alerts can be supplemented with vulnerabilities detected from activity within our developer community.\n Leveraging the community\n\n There are many places a project can publicize security fixes within a new version: the CVE feed, various mailing lists and open source groups, or even within its release notes or changelog. Regardless of how projects share this information, some developers within the GitHub community will see the advisory and immediately bump their required versions of the dependency to a known safe version. If detected, we can use the information in these commits to generate security alerts for vulnerabilities which may not have been published in the CVE feed.\n\n On an average day, the dependency graph can track around 10,000 commits to dependency files for any of our supported languages. We can’t manually process this many commits. Instead, we depend on machine intelligence to sift through them and extract those that might be related to a security release.\n\n For this purpose, we created a machine learning model that scans text associated with public commits (the commit message and linked issues or pull requests) to filter out those related to possible security upgrades. With this smaller batch of commits, the model uses the diff to understand how required version ranges have changed. Then it aggregates across a specific timeframe to get a holistic view of all dependencies that a security release might affect. Finally, the model outputs a list of packages and version ranges it thinks require an alert and currently aren’t covered by any known CVE in our system.\n Always quality focused\n\n No machine learning model is perfect. While machine intelligence can sift through thousands of commits in an instant, this anomaly-detection algorithm will still generate false positives for packages where no security patch was released. Security alert quality is a focus for us, so we review all model output before the community receives an alert.\n Learn more');\n INSERT INTO grafit_article (id, title, text) VALUES (29, 'Performance Benchmark 2018', 'I''ve stopped reading database benchmarks, because they are extremely vague. Instead I spend my time optimizing my current solution/stack. For example Postgresql has hundreds of knobs that you can adjust for almost every scenario you can imagine. Sometimes you have a special query and increase the work_mem just for that session. Other cases you adjust the cost settings for another query/session. You can analyze your indexes and index types. And sometimes you need to rewrite parts of a big query.\n\n Learning all this takes time, you are much better off learning more about your chosen technology stack than switching to another technology stack.\n\n Though in a few rare races, you need a different technology to solve your business problem. In most cases they complement your existing solution, like Elasticsearch/Solr for full-text search or Clickhouse for OLAP workloads.\n\n \n \n maxxxxx 8 months ago [-]\n\n Agreed. Switching to another system is expensive and the benefit is pretty questionable.\n\n \n \n emsy 8 months ago [-]\n\n Unless you hit a very specific use-case/bottleneck, which I only ever witnessed once.\n\n \n \n TremendousJudge 8 months ago [-]\n\n expand, please?\n\n \n \n maxxxxx 8 months ago [-]\n\n I imagine something very specific like having a lot of inserts into a table and that being your main use case. Depending on your data some databases may be better than others and that should be easy to measure.\n\n In most real-world cases the requirements however are not very clear and often conflicting so it''s much harder to get data that shows the performance of one system over the other.\n\n \n \n gopalv 8 months ago [-]\n\n > Depending on your data some databases may be better than others and that should be easy to measure.\n\n And the performance difference could be an accidental feature of the design and completely unintentional.\n\n Postgres for instance has a native data engine, so it can store the exact row-ids for a row into an index, but this means that every update to the row needs all indexes to be updated.\n\n Mysql has many data engines (InnoDB and MyISAM to start with), to the row-id is somewhat opaque, so the index stores the primary key which can be pushed to the data engine scans and then have it lookup a row-id internally. This needs an index to be touched for the columns you modify explicitly or if the primary key is updated (which is a usual no-no due to UNIQUE lookup costs).\n\n When you have a single wide table with a huge number of indexes, where you update a lot of dimensions frequently, the performance difference between these two solutions is architectural.\n\n And if you lookup along an index with few updates, but long running open txns, that is also materially different - one lookup versus two.\n\n Though how it came about isn''t really intentional. ');\n \"\"\"),\n ]\n"},"steps":{"kind":"list like","value":["# Generated by Django 2.1.2 on 2018-10-25 09:36\n\nimport django.contrib.auth.models\nimport django.contrib.auth.validators\nfrom django.db import migrations, models\nimport django.utils.timezone\nimport uuid\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('grafit', '0002_article'),\n ]\n\n operations = [\n migrations.RunSQL(\"\"\"\n INSERT INTO grafit_article (id, title, text) VALUES (2, 'MongoDB', 'MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemata. MongoDB is developed by MongoDB Inc., and is published under a combination of the Server Side Public License and the Apache License.\n 10gen software company began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[6]\n\n On October 20, 2017, MongoDB became a publicly-traded company, listed on NASDAQ as MDB with an IPO price of $24 per share.[7] Ad hoc queries\n\n MongoDB supports field, range query, and regular expression searches.[8] Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size.\n Indexing\n\n Fields in a MongoDB document can be indexed with primary and secondary indices.\n Replication\n\n MongoDB provides high availability with replica sets.[9] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.\n Load balancing[10]\n\n MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.\n\n MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. ');\n INSERT INTO grafit_article (id, title, text) VALUES (3, 'NoSQL', 'A NoSQL (originally referring to \"non SQL\" or \"non relational\")[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the \"NoSQL\" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called \"Not only SQL\" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.[7][8]\n\n Motivations for this approach include: simplicity of design, simpler \"horizontal\" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as \"more flexible\" than relational database tables.[9]\n\n Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)\n\n Instead, most NoSQL databases offer a concept of \"eventual consistency\" in which database changes are propagated to all nodes \"eventually\" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases \"do not allow referential integrity constraints to span databases.\"[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing. ');\n INSERT INTO grafit_article (id, title, text) VALUES (4, 'SQL', 'SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd[15] in the early 1970s.[16] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM''s original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s.[16]\n\n Chamberlin and Boyce''s first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973, they began work on SEQUEL.[15] The acronym SEQUEL was later changed to SQL because \"SEQUEL\" was a trademark of the UK-based Hawker Siddeley aircraft company.[17]\n\n In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce, and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. By 1986, ANSI and ISO standard groups officially adopted the standard \"Database Language SQL\" language definition. New versions of the standard were published in 1989, 1992, 1996, 1999, 2003, 2006, 2008, 2011,[15] and most recently, 2016. After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.[18] ');\n INSERT INTO grafit_article (id, title, text) VALUES (5, 'MySQL', 'Built on MySQL Enterprise Edition and powered by the Oracle Cloud, Oracle MySQL Cloud Service provides a simple, automated, integrated and enterprise ready MySQL cloud service, enabling organizations to increase business agility and reduce costs. \"Relying on the MySQL engine as the low-level storage layer has allowed us to very quickly build a robust system.\"\n \n\n \"We have successfully implemented MySQL Cluster Carrier Grade Edition for our highly mission critical XDMS application which will enable the next generation of converged services.\"\n \n\n \"We found that MySQL was the best database in terms of the price-point and functionality it offers up. The benefits that MySQL brings to our Brightmail product is its relaiability, robustness and very low-cost administration costs.\"');\n INSERT INTO grafit_article (id, title, text) VALUES (6, 'Critical Flaw Reported In phpMyAdmin Lets Attackers Damage Databases', 'A critical security vulnerability has been reported in phpMyAdmin—one of the most popular applications for managing the MySQL database—which could allow remote attackers to perform dangerous database operations just by tricking administrators into clicking a link.\n\n Discovered by an Indian security researcher, Ashutosh Barot, the vulnerability is a cross-site request forgery (CSRF) attack and affects phpMyAdmin versions 4.7.x (prior to 4.7.7).\n\n Cross-site request forgery vulnerability, also known as XSRF, is an attack wherein an attacker tricks an authenticated user into executing an unwanted action.\n\n According to an advisory released by phpMyAdmin, \"by deceiving a user to click on a crafted URL, it is possible to perform harmful database operations such as deleting records, dropping/truncating tables, etc.\"\n\n phpMyAdmin is a free and open source administration tool for MySQL and MariaDB and is widely used to manage the database for websites created with WordPress, Joomla, and many other content management platforms.\n\n Moreover, a lot of hosting providers use phpMyAdmin to offer their customers a convenient way to organize their databases.\n Barot has also released a video, as shown above, demonstrating how a remote attacker can make database admins unknowingly delete (DROP) an entire table from the database just by tricking them into clicking a specially crafted link.\n\n \"A feature of phpMyAdmin was using a GET request and after that POST request for Database operations such as DROP TABLE table_name; GET requests must be protected against CSRF attacks. In this case, POST requests were used which were sent through URL (for bookmarking purpose may be); it was possible for an attacker to trick a database admin into clicking a button and perform a drop table database query of the attacker’s choice.\" Barot explains in a blog post.\n\n However, performing this attack is not simple as it may sound. To prepare a CSRF attack URL, the attacker should be aware of the name of targeted database and table.\n\n \"If a user executes a query on the database by clicking insert, DROP, etc. buttons, the URL will contain database name and table name,\" Barot says. \"This vulnerability can result in the disclosure of sensitive information as the URL is stored at various places such as browser history, SIEM logs, Firewall Logs, ISP Logs, etc.\"\n\n Barot reported the vulnerability to phpMyAdmin developers, who confirmed his finding and released phpMyAdmin 4.7.7 to address this issue. So administrators are highly recommended to update their installations as soon as possible.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (25, 'Death By Database', 'The following is a true story, but with names changed.\n\n When I work with clients to build software, I take the usual steps of understanding their needs, gathering requirements, learning about their customers, and so on. At this point I have a model on paper of roughly what the software is intended to do, so they get surprised when I immediately turn to database design.\n\n \"Who care about database design? What about mockups? What about workflows?\"\n\n Let me tell you about \"Bob''s Luxury Goods.\" I worked for this company many years ago and they had a retail store selling ... you guessed it ... luxury goods. They''d ask all customers for a billing address and if they had a different delivery address. At the database level, they had a \"one-to-many\" relationship between customers and addresses.\n\n That was their first problem. A customer''s partner might come into Bob''s and order something and if the address was entered correctly it would be flagged as \"in use\" and we had to use a different address or deliberately enter a typo. Fortunately, addresses were case-sensitive, so many people had UPPER-CASE ADDRESSES.\n\n We should have had a many-to-many relationship between customers and addresses so we could handle the case where more than one person would share the same address, but we didn''t. Further, I was never allocated the time to fix the database because it was \"cheaper\" to remove the restriction on \"flagged\" addresses and allow a duplicate address to be used.\n\n Naturally, being a luxury goods company, we had many repeat customers and sometimes they would move and if we didn''t find the duplicate address, or the address with the \"typo\", we might update the address for one partner, but not the other. That was a headache, but it didn''t happen frequently enough for management to worry about it.\n\n That''s when the marketing department had a brilliant, inexpensive idea. You see, we periodically did mass mailings of special events to our customers. Since we had the software to do mass mailings, why not import a mailing list of all addresses in high net worth areas and mail everyone about upcoming special events? So the company went ahead and bought a database with all of these addresses, but forgot to mention to me that I was supposed to implement this.\n\n Except that every address record had the customer id embedded in it, so we couldn''t enter an address without a customer.\n\n \"Curtis,\" they said, \"just enter a dummy customer called ''Occupant'' and attach all addresses to that.\"\n\n Except you couldn''t enter a customer without an order.\n\n Except you couldn''t enter an order without at least one item on it.\n\n Except you couldn''t enter an item unless it was listed in inventory.\n\n Except that reserved the \"inventory\" item and made it unavailable.\n\n Except, except, except ...\n\n It came down to trying to create a fake customer, with a fake order, with a fake item, with a fake item category, with a \"paid\" invoice, with exceptions sprinkled throughout the codebase to handle all of these special cases and probably more that I no longer remember.\n\n Then, and only then, could I write the code to provide \"generic\" mass mailings. Management decided it was easier to hire an outside company to handle the mailing list for them.\n\n If they had simply had a proper database design up front, they could have reused their existing system with little trouble.\n\n That''s what bad database design costs you and why I usually start with that before writing my software.\n\n Note: if you''re not familiar with database design, here''s a talk I give where I make it fairly simple to understand. I mostly avoid big words.');\n INSERT INTO grafit_article (id, title, text) VALUES (33, 'GitHub Actions: built by you, run by us', 'Yesterday at GitHub Universe, we announced GitHub Actions, a new way to automate and customize your workflows. Configuring the apps and services that make up your development cycle takes significant time and effort. GitHub Actions applies open source principles to workflow automation, weaving together the tools you use from idea to production into one complete workflow. You can also create, share, and discover any actions your projects require, just as you would create, share, and discover code on GitHub.\n\n Learn more about actions\n\n As we prepared for Universe, we shared GitHub Actions with a group of customers, integrators, and open source maintainers to see what they could do. In just a few short weeks, talented teams and individuals alike have created hundreds of GitHub Actions. During today’s Universe keynote, we heard directly from developers, and we’re excited to share their work with you');\n INSERT INTO grafit_article (id, title, text) VALUES (34, 'Git Submodule Vulnerability Announced ', '\n\n The Git project has disclosed CVE-2018-17456, a vulnerability in Git that can cause arbitrary code to be executed when a user clones a malicious repository. Git v2.19.1 has been released with a fix, along with backports in v2.14.5, v2.15.3, v2.16.5, v2.17.2, and v2.18.1. We encourage all users to update their clients to protect themselves.\n\n Until you’ve updated, you can protect yourself by avoiding submodules from untrusted repositories. This includes commands such as git clone --recurse-submodules and git submodule update.\n Affected products\n GitHub Desktop\n\n GitHub Desktop versions 1.4.1 and older included an embedded version of Git that was affected by this vulnerability. We encourage all GitHub Desktop users to update to the newest version (1.4.2 and 1.4.3-beta0) available today in the Desktop app.\n Atom\n\n Atom included the same embedded Git and was also affected. Releases 1.31.2 and 1.32.0-beta3 include the patch.\n\n Ensure you’re on the latest Atom release by completing any of the following:\n\n Windows: From the toolbar, click Help -> Check for Updates\n MacOS: From the menu bar, click Atom -> Check for Update\n Linux: Update manually by downloading the latest release from atom.io\n\n Git on the command line and other clients\n\n In order to be protected from the vulnerability, you must update your command-line version of Git, and any other application that may include an embedded version of Git, as they are independent of each other.\n Additional notes\n\n Neither GitHub.com nor GitHub Enterprise are directly affected by the vulnerability. However, as with previously discovered vulnerabilities, GitHub.com will detect malicious repositories, and will reject pushes or API requests attempting to create them. Versions of GitHub Enterprise with this detection will ship on October 9.\n Details of the vulnerability\n\n This vulnerability is very similar to CVE-2017-1000117, as both are option-injection attacks related to submodules. In the earlier attack, a malicious repository would ship a .gitmodules file pointing one of its submodules to a remote repository with an SSH host starting with a dash (-). The ssh program—spawned by Git—would then interpret that as an option. This attack works in a similar way, except that the option-injection is against the child git clone itself.\n\n The problem was reported on September 23 by @joernchen, both to Git’s private security list, as well as to GitHub’s Bug Bounty program. Developers at GitHub worked with the Git community to develop a fix.\n\n The basic fix was clear from the report. However, due to to the similarity to CVE-2017-1000117, we also audited all of the .gitmodules values and implemented stricter checks as appropriate. These checks should prevent a similar vulnerability in another code path. We also implemented detection of potentially malicious submodules as part of Git’s object quality checks (which was made much easier by the infrastructure added during the last submodule-related vulnerability).\n\n The coordinated disclosure date of October 5 was selected by Git developers to allow packagers to prepare for the release. This also provided hosting sites (with custom implementations) ample time to detect and block the attack before it became public. Members of the Git community checked the JGit and libgit2 implementations. Those are not affected by the vulnerability because they clone submodules via function calls rather than separate commands.\n\n We were also able to use the time to scan all repositories on GitHub for evidence of the attack being used in the wild. We’re happy to report that no instances were found (and now, with our detection, none can be added).\n\n Please update your copy of Git soon, and happy cloning!\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (21, 'Hackers Targeting Servers Running Database Services for Mining Cryptocurrency', 'Security researchers have discovered multiple attack campaigns conducted by an established Chinese criminal group that operates worldwide, targeting database servers for mining cryptocurrencies, exfiltrating sensitive data and building a DDoS botnet.\n\n The researchers from security firm GuardiCore Labs have analyzed thousands of attacks launched in recent months and identified at least three attack variants—Hex, Hanako, and Taylor—targeting different MS SQL and MySQL servers for both Windows and Linux.\n\n The goals of all the three variants are different—Hex installs cryptocurrency miners and remote access trojans (RATs) on infected machines, Taylor installs a keylogger and a backdoor, and Hanako uses infected devices to build a DDoS botnet.\n\n So far, researchers have recorded hundreds of Hex and Hanako attacks and tens of thousands of Taylor attacks each month and found that most compromised machines are based in China, and some in Thailand, the United States, Japan and others.\n\n To gain unauthorized access to the targeted database servers, the attackers use brute force attacks and then run a series of predefined SQL commands to gain persistent access and evade audit logs.\n\n What''s interesting? To launch the attacks against database servers and serve malicious files, attackers use a network of already compromised systems, making their attack infrastructure modular and preventing takedown of their malicious activities.');\n INSERT INTO grafit_article (id, title, text) VALUES (22, 'RIP Open Source MySQL', ' This is an excellent opportunity for the Postgres community to step up an promote Postgres.\n\n \n \n rbanffy on Aug 18, 2012 [-]\n\n I think this would be a mistake.\n\n This is an excellent opportunity to demonstrate that anyone can fork the MySQL codebase and create other plug-in replacement databases with it, such as MariaDB and Drizzle.\n\n All that is lost is the MySQL name and brand.\n\n PostgreSQL users and developers must seize the opportunity to show businesses that free software cannot be killed, not even by mighty Oracle. They and, most notably, Microsoft, have been trying to kill it for more than a decade now.\n\n Because the anti-free-software FUD machine (fed in part by Oracle itself) is already having a wonderful time with this.\n\n \n \n Udo on Aug 18, 2012 [-]\n\n I wish I could mod this up a hundred times. PostgreSQL people themselves have been playing into the hands of corporate FUDders with their incessant and inappropriate peddling. MySQL is not your enemy, MS SQL Server is. Oracle''s software empire as a whole certainly is your enemy. Show some solidarity with a fellow open source project!\n\n MySQL and PostgreSQL represent two very different implementation philosophies, and being able to choose between them according to taste and merits is a good thing.\n\n Most of us have suspected that the MySQL project itself was going to die as it was acquired by Oracle, in the same way Open Office died when it was acquired by Oracle. This is a company where good software goes to expire, either due to a deliberate intention or gross incompetence I can''t say but I suspect it''s a mixture of both. However sad that may be for the MySQL (or OpenOffice) brand name, the code itself lives on and continues to evolve within a rich open source ecosystem.\n\n Hence, sensational and petulant \"RIP $PRODUCTNAME\" articles are unnecessary. There is no threat to existing projects based on MySQL or any other successful open source project for that matter. Not only will this stuff be free forever, it will also continue to grow and be developed on its own.\n\n The corporate assassination of open source projects will only work if we let it, it''s a purely psychological game. ');\n INSERT INTO grafit_article (id, title, text) VALUES (23, 'Free Text Sources', 'There are a few interesting things to talk about surrounding free and open textbooks. Quality is one. Usability is another. Why to write one (and/or, why not) is certainly critical. But where can you find these disruptive, open texts?\n\n Not all faculty know there are free and open texts they can use; finding free and/or open textbooks (or even knowing to look) can sometimes be a trick. I knew about one or two sources, and did a little bit more digging. Admittedly, many of the sources of free texts linked below have a technical bent. On one hand, this might be because math, computing, and the sciences are familiar with working openly and giving things away. On the other, it might be because I am a member of the computing faculty, and therefore am most familiar with resources in that space.');\n INSERT INTO grafit_article (id, title, text) VALUES (24, 'Apache Software Foundation Public Mail Archives', 'A collection of all publicly available mail archives from the Apache55 Software Foundation (ASF), taken on July 11, 2011. This collection contains all publicly available email archives from the ASF''s 80+ projects (http://mail-archives.apache.org/mod_mbox/), including mailing lists such as Apache HTTPD Server, Apache Tomcat, Apache Lucene and Solr, Apache Hadoop and many more. Generally speaking, most projects have at least three lists: user, dev and commits, but some have more, some have less. The user lists are where users of the software ask questions on usage, while the dev list usually contains discussions on the development of the project (code, releases, etc.) The commit lists usually consists of automated notifications sent by the various ASF version control tools, like Subversion or CVS, and contain information about changes made to the project''s source code.\n\n Both tarballs and per project sets are available in the snapshot. The tarballs are organized according to project name. Thus, a-d.tar.gz contains all ASF projects that begin with the letters a, b, c or d, such as abdera.apache.org. Files within the project are usually gzipped mbox files.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (26, 'PostgreSQL - Overview', 'PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development phase and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness.\n\n This tutorial will give you a quick start with PostgreSQL and make you comfortable with PostgreSQL programming.\n What is PostgreSQL?\n\n PostgreSQL (pronounced as post-gress-Q-L) is an open source relational database management system (DBMS) developed by a worldwide team of volunteers. PostgreSQL is not controlled by any corporation or other private entity and the source code is available free of charge.\n A Brief History of PostgreSQL\n\n PostgreSQL, originally called Postgres, was created at UCB by a computer science professor named Michael Stonebraker. Stonebraker started Postgres in 1986 as a follow-up project to its predecessor, Ingres, now owned by Computer Associates.\n\n 1977-1985 − A project called INGRES was developed.\n\n Proof-of-concept for relational databases\n\n Established the company Ingres in 1980\n\n Bought by Computer Associates in 1994\n\n 1986-1994 − POSTGRES\n\n Development of the concepts in INGRES with a focus on object orientation and the query language - Quel\n\n The code base of INGRES was not used as a basis for POSTGRES\n\n Commercialized as Illustra (bought by Informix, bought by IBM)\n\n 1994-1995 − Postgres95\n\n Support for SQL was added in 1994\n\n Released as Postgres95 in 1995\n\n Re-released as PostgreSQL 6.0 in 1996\n\n Establishment of the PostgreSQL Global Development Team\n\n Key Features of PostgreSQL\n\n PostgreSQL runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It supports text, images, sounds, and video, and includes programming interfaces for C / C++, Java, Perl, Python, Ruby, Tcl and Open Database Connectivity (ODBC).\n\n PostgreSQL supports a large part of the SQL standard and offers many modern features including the following −\n\n Complex SQL queries\n SQL Sub-selects\n Foreign keys\n Trigger\n Views\n Transactions\n Multiversion concurrency control (MVCC)\n Streaming Replication (as of 9.0)\n Hot Standby (as of 9.0)\n\n You can check official documentation of PostgreSQL to understand the above-mentioned features. PostgreSQL can be extended by the user in many ways. For example by adding new −\n\n Data types\n Functions\n Operators\n Aggregate functions\n Index methods\n\n Procedural Languages Support\n\n PostgreSQL supports four standard procedural languages, which allows the users to write their own code in any of the languages and it can be executed by PostgreSQL database server. These procedural languages are - PL/pgSQL, PL/Tcl, PL/Perl and PL/Python. Besides, other non-standard procedural languages like PL/PHP, PL/V8, PL/Ruby, PL/Java, etc., are also supported.');\n INSERT INTO grafit_article (id, title, text) VALUES (27, 'Setup PostgreSQL on Windows with Docker', 'Over the weekend I finally got the chance to start reading A Curious Moon by Rob Conery which is a book on learning PostgreSQL by following the fictional Dee Yan as she is thrown into database administrator role at an aerospace startup.\n\n I have a lot of experience using Microsoft’s SQL Server, but up until now, I haven’t touched PostgreSQL. For personal projects SQL Server’s cost and be prohibitive and the release of Rob’s book added up to a good time to give PostgreSQL a try.\n Install Directly or not?\n\n On the download section of the official Postgres site, there is an option to download an installer. This is the route I was going to at first, but in Rob’s book, he suggests using a VM for Postgres installation on Windows. This kicked off a lot of searching on my part and didn’t find a good definitive answer on why that is or isn’t the way to do.\n\n In the end, I decided to try and run the Postgres process using Docker instead installing directly on Windows or dealing with a full VM.\n Installing Docker\n\n Head to this link and click the Get Docker link to download the installer. After the install is complete you will have to log out and back in. When I logged back in I got a message about Hyper-V not being enabled.\n\n After logging back in I then got the following message about hardware-assisted virtualization not being enabled.\n\n After tweaking my BIOS settings and logging back in I was greeted by the Docker welcome screen.\n\n Open a command prompt and run the following command.\n\n docker run hello-world\n\n You should output that starts with the following if your installation is working.\n\n Hello from Docker!\n This message shows that your installation appears to be working correctly.\n\n What about Postgres?\n\n Getting up and going with a container running Postgres was pretty simple and could be done with the following command which will create a container and expose the port used by Postgres so it can be accessed from the host.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d postgres\n\n The problem with this approach is if you ever need to rebuild the container for some reason, like a new version of Postgres is released, your data will be lost. Thankfully I found this blog post which shows how to use a secondary container for the data leaving the Postgres container able to be destroyed and recreated as needed. The following is the command I used to create my data container.\n\n docker create -v /var/lib/postgresql/data --name PostgresData alpine\n\n The above creates a container named PostgresData based on the Alpine image. It is important that the -v parameter matches the path that Postgres expects.\n\n Now that we have a container that will keep our data safe let’s create the actual Postgres container with the following command.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d --volumes-from PostgresData postgres\n\n The only difference from the first example run command is the addition of –volumes-from PostgresData which tells the container to use the PostgresData container.\n\n If you run the docker ps -a command it will show you all your containers.\n\n As you can see in my example I have two containers only one of which is actually running. Make sure you don’t remove the data container just because it will never show as running.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (28, 'DIY: A PostgreSQL database server setup anyone can handle', 'When it comes to databases, I''m a fan of MySQL. The open source database can handle just about any load you want to throw at it, and it has lots of powerful tools that can be used to manage it.\n\n The other popular open source database is PostgreSQL, which is cross-platform and is used by numerous applications. Although PostgreSQL is often seen as being as powerful as MySQL, it doesn''t have nearly the number of available tools to make setup and management as easy as its competition. So I''ve written this handy PostgreSQL primer on how to get your database server up and running and ready to use. (Although PostgreSQL is cross-platform, I demonstrate the installation and setup on a Ubuntu 11.04 machine because it''s my platform of choice. The translation to other platforms should be simple.)\n Step 1: Install PostgreSQL\n\n Here are the installation steps on Ubuntu (this installation will also work on any Debian-based distribution):\n\n Open a terminal window.\n Issue the command sudo apt-get install postgresql.\n Type the sudo password necessary to give you admin rights and hit Enter.\n Allow apt to pick up any necessary dependencies.\n\n Once the installation is complete, it''s time to set this baby up.\n Step 2: Change the default user password\n\n Caution: If you don''t follow this step, you will not be able to add databases and administer PostgreSQL, and the database will not be secure.\n\n Here''s how to change the password for the default user. The user in question is postgres, and the password is changed like so:\n\n Open a terminal window.\n Issue the command sudo passwd postgres.\n Type (and confirm) that password to be used for this user.\n\n The postgres user will be the only user on your system that can open the PostgreSQL prompt without defining a database, which means postgres is the only user who can administer PostgreSQL. To test this, change to the postgres user with the command su - postgres and then enter the command psql. You should now be at the Postgres prompt, which looks like:\n\n postgres=#\n\n All other users have to gain access to the prompt like so:\n\n psql DB_NAME\n\n where DB_NAME is the name of an existing database.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (31, 'The Marketing Behind MongoDB', ' 100% of my friends who have used Mongo/similar NoSQL have given up and had a nasty rewrite back to pgSQL.\n\n This seems to be the journey:\n\n 1. Lack of migrations is awesome! We can iterate so quickly for MVP\n\n 2. Get users\n\n 3. Add features, still enjoying the speed of iteration\n\n 4. Get more users\n\n 5. Start building reporting features for enterprise/customer support/product metrics (ie: when the real potential success starts)\n\n 6. Realise you desperately need joins, transactions and other SQL features\n\n 7. Pause product dev for 1-3+ months to migrate back to SQL, or do some weird parallel development process to move it piecemeal back.\n\n I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n My thought is definitely yes.\n\n \n \n brandur on Aug 29, 2017 [-]\n\n > I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n I''ve used Postgres and Mongo pretty extensively, and for any reasonably seasoned developer, the startup overhead of an SQL system is a myth. There may upfront cost to learning how an RDMS and SQL work in the first place, but once you''re familiar with them, they''ll be faster than Mongo on any new project.\n\n The schemaless concept of a document database seems to be the major selling factor in velocity of movement, but once you''ve got a good handle on a migration framework in the vein of ActiveRecord or other popular software, that''s negated completely. It also really doesn''t take long before schemaless starts to cause big problems for you in terms of data consistency -- it''s not just the big players that get bitten by this.\n\n The simplified query language is another one. SQL is a little bit obtuse, but it''s not that bad once you have a handle on it, and a lot of people are familiar with it. Once you add in an ORM layer, the lazy-style access of a framework like Sequel or SQLAlchemy makes the developer experience quite a bit better than any Mongo APIs that I''ve seen. Also, after you get beyond trivial usage, SQL''s flexibility so wildly outstrips Mongo''s query documents that it''s not even worth talking about.\n\n Postgres on the other hand ships with a great management CLI, a very powerful REPL (psql), and features like data types/constraints/transactions that guarantee you correctness with zero effort on your part. I can only speak for myself, but I''d take Postgres to the hackathon any day of the week.\n\n \n \n martinald on Aug 29, 2017 [-]\n\n I totally agree with you, and started writing something about how understanding a good ORM takes nearly all the headache away.\n\n I think the thing people do find slow is a lot of ''documents within documents'' in SQL. It turns out this is usually a bad development pattern long term but it is super fast being able to just add docs inside docs with no configuration. It feels very slow writing foreign keys, navigation props and schemas for this in SQL vs JSON, where you can just dump your object in and you''re done.\n\n Basically; I think with noSQL you get some very short term gain for a lot of long term pain, and you''re right, ORMs and other tooling solves this mostly.\n\n I myself fell for this trap, and while it was a nightmare it actually matured me more as a professional more than anything I''ve ever done recently. Regardless of crazy hype, I don''t think I''ll ever fall for a solution so easily without evaluating it properly.\n\n I think I assumed the \"crowd\" had done the tech due diligence on this stuff and it definitely wasn''t the case. ');\n INSERT INTO grafit_article (id, title, text) VALUES (32, 'Countless NoSQL databases competed to be the database of choice', 'n 2013, 10gen — the company behind MongoDB — moved into a large 30,000 square foot office in Midtown Manhattan.\n\n The transfer into the former New York Times building capped off a tremendous period of growth: the database boasted 4 million downloads, the MongoDB User Groups already attracted 15,000 members, and ~10,000 people had attended a global event in 2012. Their offices were truly global from London to Sydney to Dublin and Barcelona — and a requisite west coast headquarters in Palo Alto.\n\n Despite the traction, many startups using MongoDB faced their own challenges. One part of MongoDB’s success among startups was because some didn''t critically assess 10gen’s marketing message.\n\n As engineers, we often discuss technical attacks (e.g., DDoS, Sybil attacks, security vulnerabilities), but need to spend time debating how to protect ourselves from marketing “attacks”.1 Today, developer marketing is subtle — third party blog posts, content marketing disguised as engineering lessons, biased talks, and sponsored hackathons — not clearly marked content from vendors. As such, startup engineering decisions can hinge on sources that are not impartial.\n\n A large amount of \"engineering\" content — even when written by engineers — is actually marketing, rather than thoughtful content whose aim is to help you make the best decision.\n\n Previously, we looked at the hype around NoSQL and common engineering mistakes to see how MongoDB became so successful. Now, let''s take a look into 10gen''s marketing strategy — as told by their employees.2\n\n 10gen’s marketing strategy is an increasingly common playbook and understanding it is useful for future developer tool decisions.');\n INSERT INTO grafit_article (id, title, text) VALUES (30, 'Comment Arrango', ' ArangoDB always makes for exciting benchmark posts.\n\n I could see myself there in a bowler hat with a fistful of racing chits screaming “go, Postgres, go.”\n\n I’d love to see a competition were the developers of each database got to use the same hardware and data then tune the hell out of their configs, queries, and indices.\n\n Red Bull could sponsor it. I’d buy a T-shirt.\n\n \n \n kbenson 8 months ago [-]\n\n That doesn''t sound that hard to start. Something like RealWorld[1] and the Web Framework Benchmarks[2] combined but for DB workloads. Have one dataset that includes data amenable to OLAP and OLTP, but have separate tests each consisting of OLAP queries, OLTP queries, and combined queries. Choose a low-end, mid-range and high-end set of AWS or GCE instances/configs to normalize against. Let people submit pull requests with new technologies or configs.\n\n You''d want to get some funding to run the tests (or maybe solicit Google or Amazon to see if you could get the instance time donated once a month or something.\n\n If you started small, with maybe a portion of these features, and then scaled up over time, you might actually get to the point where you had tests that emulated a power failure, or master/slave and dual master scenarios and how they handle certain common network errors (split-brain). That would be an amazing resource.\n\n Edit: It occurs to me I probably should have read more of the article, since this is sort of what they are doing already...\n\n 1: https://github.com/gothinkster/realworld\n\n 2: https://www.techempower.com/benchmarks/\n\n \n \n etxm 8 months ago [-]\n\n Yeah after I posted it I started thinking about what it would take and what that would actually look like... and how you’d cheat :)\n\n It would probably require a few different categories with some sort of output assertion to validate the query performed right and a means of tracking CPU, usage ram usage, and execution time.\n\n It would be cool to see things like disaster recovery and chaos proofing as well. ');\n INSERT INTO grafit_article (id, title, text) VALUES (35, 'Applying machine intelligence to GitHub security alerts ', 'Last year, we released security alerts that track security vulnerabilities in Ruby and JavaScript packages. Since then, we’ve identified more than four million of these vulnerabilities and added support for Python. In our launch post, we mentioned that all vulnerabilities with CVE IDs are included in security alerts, but sometimes there are vulnerabilities that are not disclosed in the National Vulnerability Database. Fortunately, our collection of security alerts can be supplemented with vulnerabilities detected from activity within our developer community.\n Leveraging the community\n\n There are many places a project can publicize security fixes within a new version: the CVE feed, various mailing lists and open source groups, or even within its release notes or changelog. Regardless of how projects share this information, some developers within the GitHub community will see the advisory and immediately bump their required versions of the dependency to a known safe version. If detected, we can use the information in these commits to generate security alerts for vulnerabilities which may not have been published in the CVE feed.\n\n On an average day, the dependency graph can track around 10,000 commits to dependency files for any of our supported languages. We can’t manually process this many commits. Instead, we depend on machine intelligence to sift through them and extract those that might be related to a security release.\n\n For this purpose, we created a machine learning model that scans text associated with public commits (the commit message and linked issues or pull requests) to filter out those related to possible security upgrades. With this smaller batch of commits, the model uses the diff to understand how required version ranges have changed. Then it aggregates across a specific timeframe to get a holistic view of all dependencies that a security release might affect. Finally, the model outputs a list of packages and version ranges it thinks require an alert and currently aren’t covered by any known CVE in our system.\n Always quality focused\n\n No machine learning model is perfect. While machine intelligence can sift through thousands of commits in an instant, this anomaly-detection algorithm will still generate false positives for packages where no security patch was released. Security alert quality is a focus for us, so we review all model output before the community receives an alert.\n Learn more');\n INSERT INTO grafit_article (id, title, text) VALUES (29, 'Performance Benchmark 2018', 'I''ve stopped reading database benchmarks, because they are extremely vague. Instead I spend my time optimizing my current solution/stack. For example Postgresql has hundreds of knobs that you can adjust for almost every scenario you can imagine. Sometimes you have a special query and increase the work_mem just for that session. Other cases you adjust the cost settings for another query/session. You can analyze your indexes and index types. And sometimes you need to rewrite parts of a big query.\n\n Learning all this takes time, you are much better off learning more about your chosen technology stack than switching to another technology stack.\n\n Though in a few rare races, you need a different technology to solve your business problem. In most cases they complement your existing solution, like Elasticsearch/Solr for full-text search or Clickhouse for OLAP workloads.\n\n \n \n maxxxxx 8 months ago [-]\n\n Agreed. Switching to another system is expensive and the benefit is pretty questionable.\n\n \n \n emsy 8 months ago [-]\n\n Unless you hit a very specific use-case/bottleneck, which I only ever witnessed once.\n\n \n \n TremendousJudge 8 months ago [-]\n\n expand, please?\n\n \n \n maxxxxx 8 months ago [-]\n\n I imagine something very specific like having a lot of inserts into a table and that being your main use case. Depending on your data some databases may be better than others and that should be easy to measure.\n\n In most real-world cases the requirements however are not very clear and often conflicting so it''s much harder to get data that shows the performance of one system over the other.\n\n \n \n gopalv 8 months ago [-]\n\n > Depending on your data some databases may be better than others and that should be easy to measure.\n\n And the performance difference could be an accidental feature of the design and completely unintentional.\n\n Postgres for instance has a native data engine, so it can store the exact row-ids for a row into an index, but this means that every update to the row needs all indexes to be updated.\n\n Mysql has many data engines (InnoDB and MyISAM to start with), to the row-id is somewhat opaque, so the index stores the primary key which can be pushed to the data engine scans and then have it lookup a row-id internally. This needs an index to be touched for the columns you modify explicitly or if the primary key is updated (which is a usual no-no due to UNIQUE lookup costs).\n\n When you have a single wide table with a huge number of indexes, where you update a lot of dimensions frequently, the performance difference between these two solutions is architectural.\n\n And if you lookup along an index with few updates, but long running open txns, that is also materially different - one lookup versus two.\n\n Though how it came about isn''t really intentional. ');\n \"\"\"),\n ]\n","import django.contrib.auth.models\nimport django.contrib.auth.validators\nfrom django.db import migrations, models\nimport django.utils.timezone\nimport uuid\n\n\nclass Migration(migrations.Migration):\n dependencies = [('grafit', '0002_article')]\n operations = [migrations.RunSQL(\n \"\"\"\n INSERT INTO grafit_article (id, title, text) VALUES (2, 'MongoDB', 'MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemata. MongoDB is developed by MongoDB Inc., and is published under a combination of the Server Side Public License and the Apache License.\n 10gen software company began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[6]\n\n On October 20, 2017, MongoDB became a publicly-traded company, listed on NASDAQ as MDB with an IPO price of $24 per share.[7] Ad hoc queries\n\n MongoDB supports field, range query, and regular expression searches.[8] Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size.\n Indexing\n\n Fields in a MongoDB document can be indexed with primary and secondary indices.\n Replication\n\n MongoDB provides high availability with replica sets.[9] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.\n Load balancing[10]\n\n MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.\n\n MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. ');\n INSERT INTO grafit_article (id, title, text) VALUES (3, 'NoSQL', 'A NoSQL (originally referring to \"non SQL\" or \"non relational\")[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the \"NoSQL\" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called \"Not only SQL\" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.[7][8]\n\n Motivations for this approach include: simplicity of design, simpler \"horizontal\" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as \"more flexible\" than relational database tables.[9]\n\n Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)\n\n Instead, most NoSQL databases offer a concept of \"eventual consistency\" in which database changes are propagated to all nodes \"eventually\" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases \"do not allow referential integrity constraints to span databases.\"[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing. ');\n INSERT INTO grafit_article (id, title, text) VALUES (4, 'SQL', 'SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd[15] in the early 1970s.[16] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM''s original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s.[16]\n\n Chamberlin and Boyce''s first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973, they began work on SEQUEL.[15] The acronym SEQUEL was later changed to SQL because \"SEQUEL\" was a trademark of the UK-based Hawker Siddeley aircraft company.[17]\n\n In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce, and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. By 1986, ANSI and ISO standard groups officially adopted the standard \"Database Language SQL\" language definition. New versions of the standard were published in 1989, 1992, 1996, 1999, 2003, 2006, 2008, 2011,[15] and most recently, 2016. After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.[18] ');\n INSERT INTO grafit_article (id, title, text) VALUES (5, 'MySQL', 'Built on MySQL Enterprise Edition and powered by the Oracle Cloud, Oracle MySQL Cloud Service provides a simple, automated, integrated and enterprise ready MySQL cloud service, enabling organizations to increase business agility and reduce costs. \"Relying on the MySQL engine as the low-level storage layer has allowed us to very quickly build a robust system.\"\n \n\n \"We have successfully implemented MySQL Cluster Carrier Grade Edition for our highly mission critical XDMS application which will enable the next generation of converged services.\"\n \n\n \"We found that MySQL was the best database in terms of the price-point and functionality it offers up. The benefits that MySQL brings to our Brightmail product is its relaiability, robustness and very low-cost administration costs.\"');\n INSERT INTO grafit_article (id, title, text) VALUES (6, 'Critical Flaw Reported In phpMyAdmin Lets Attackers Damage Databases', 'A critical security vulnerability has been reported in phpMyAdmin—one of the most popular applications for managing the MySQL database—which could allow remote attackers to perform dangerous database operations just by tricking administrators into clicking a link.\n\n Discovered by an Indian security researcher, Ashutosh Barot, the vulnerability is a cross-site request forgery (CSRF) attack and affects phpMyAdmin versions 4.7.x (prior to 4.7.7).\n\n Cross-site request forgery vulnerability, also known as XSRF, is an attack wherein an attacker tricks an authenticated user into executing an unwanted action.\n\n According to an advisory released by phpMyAdmin, \"by deceiving a user to click on a crafted URL, it is possible to perform harmful database operations such as deleting records, dropping/truncating tables, etc.\"\n\n phpMyAdmin is a free and open source administration tool for MySQL and MariaDB and is widely used to manage the database for websites created with WordPress, Joomla, and many other content management platforms.\n\n Moreover, a lot of hosting providers use phpMyAdmin to offer their customers a convenient way to organize their databases.\n Barot has also released a video, as shown above, demonstrating how a remote attacker can make database admins unknowingly delete (DROP) an entire table from the database just by tricking them into clicking a specially crafted link.\n\n \"A feature of phpMyAdmin was using a GET request and after that POST request for Database operations such as DROP TABLE table_name; GET requests must be protected against CSRF attacks. In this case, POST requests were used which were sent through URL (for bookmarking purpose may be); it was possible for an attacker to trick a database admin into clicking a button and perform a drop table database query of the attacker’s choice.\" Barot explains in a blog post.\n\n However, performing this attack is not simple as it may sound. To prepare a CSRF attack URL, the attacker should be aware of the name of targeted database and table.\n\n \"If a user executes a query on the database by clicking insert, DROP, etc. buttons, the URL will contain database name and table name,\" Barot says. \"This vulnerability can result in the disclosure of sensitive information as the URL is stored at various places such as browser history, SIEM logs, Firewall Logs, ISP Logs, etc.\"\n\n Barot reported the vulnerability to phpMyAdmin developers, who confirmed his finding and released phpMyAdmin 4.7.7 to address this issue. So administrators are highly recommended to update their installations as soon as possible.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (25, 'Death By Database', 'The following is a true story, but with names changed.\n\n When I work with clients to build software, I take the usual steps of understanding their needs, gathering requirements, learning about their customers, and so on. At this point I have a model on paper of roughly what the software is intended to do, so they get surprised when I immediately turn to database design.\n\n \"Who care about database design? What about mockups? What about workflows?\"\n\n Let me tell you about \"Bob''s Luxury Goods.\" I worked for this company many years ago and they had a retail store selling ... you guessed it ... luxury goods. They''d ask all customers for a billing address and if they had a different delivery address. At the database level, they had a \"one-to-many\" relationship between customers and addresses.\n\n That was their first problem. A customer''s partner might come into Bob''s and order something and if the address was entered correctly it would be flagged as \"in use\" and we had to use a different address or deliberately enter a typo. Fortunately, addresses were case-sensitive, so many people had UPPER-CASE ADDRESSES.\n\n We should have had a many-to-many relationship between customers and addresses so we could handle the case where more than one person would share the same address, but we didn''t. Further, I was never allocated the time to fix the database because it was \"cheaper\" to remove the restriction on \"flagged\" addresses and allow a duplicate address to be used.\n\n Naturally, being a luxury goods company, we had many repeat customers and sometimes they would move and if we didn''t find the duplicate address, or the address with the \"typo\", we might update the address for one partner, but not the other. That was a headache, but it didn''t happen frequently enough for management to worry about it.\n\n That''s when the marketing department had a brilliant, inexpensive idea. You see, we periodically did mass mailings of special events to our customers. Since we had the software to do mass mailings, why not import a mailing list of all addresses in high net worth areas and mail everyone about upcoming special events? So the company went ahead and bought a database with all of these addresses, but forgot to mention to me that I was supposed to implement this.\n\n Except that every address record had the customer id embedded in it, so we couldn''t enter an address without a customer.\n\n \"Curtis,\" they said, \"just enter a dummy customer called ''Occupant'' and attach all addresses to that.\"\n\n Except you couldn''t enter a customer without an order.\n\n Except you couldn''t enter an order without at least one item on it.\n\n Except you couldn''t enter an item unless it was listed in inventory.\n\n Except that reserved the \"inventory\" item and made it unavailable.\n\n Except, except, except ...\n\n It came down to trying to create a fake customer, with a fake order, with a fake item, with a fake item category, with a \"paid\" invoice, with exceptions sprinkled throughout the codebase to handle all of these special cases and probably more that I no longer remember.\n\n Then, and only then, could I write the code to provide \"generic\" mass mailings. Management decided it was easier to hire an outside company to handle the mailing list for them.\n\n If they had simply had a proper database design up front, they could have reused their existing system with little trouble.\n\n That''s what bad database design costs you and why I usually start with that before writing my software.\n\n Note: if you''re not familiar with database design, here''s a talk I give where I make it fairly simple to understand. I mostly avoid big words.');\n INSERT INTO grafit_article (id, title, text) VALUES (33, 'GitHub Actions: built by you, run by us', 'Yesterday at GitHub Universe, we announced GitHub Actions, a new way to automate and customize your workflows. Configuring the apps and services that make up your development cycle takes significant time and effort. GitHub Actions applies open source principles to workflow automation, weaving together the tools you use from idea to production into one complete workflow. You can also create, share, and discover any actions your projects require, just as you would create, share, and discover code on GitHub.\n\n Learn more about actions\n\n As we prepared for Universe, we shared GitHub Actions with a group of customers, integrators, and open source maintainers to see what they could do. In just a few short weeks, talented teams and individuals alike have created hundreds of GitHub Actions. During today’s Universe keynote, we heard directly from developers, and we’re excited to share their work with you');\n INSERT INTO grafit_article (id, title, text) VALUES (34, 'Git Submodule Vulnerability Announced ', '\n\n The Git project has disclosed CVE-2018-17456, a vulnerability in Git that can cause arbitrary code to be executed when a user clones a malicious repository. Git v2.19.1 has been released with a fix, along with backports in v2.14.5, v2.15.3, v2.16.5, v2.17.2, and v2.18.1. We encourage all users to update their clients to protect themselves.\n\n Until you’ve updated, you can protect yourself by avoiding submodules from untrusted repositories. This includes commands such as git clone --recurse-submodules and git submodule update.\n Affected products\n GitHub Desktop\n\n GitHub Desktop versions 1.4.1 and older included an embedded version of Git that was affected by this vulnerability. We encourage all GitHub Desktop users to update to the newest version (1.4.2 and 1.4.3-beta0) available today in the Desktop app.\n Atom\n\n Atom included the same embedded Git and was also affected. Releases 1.31.2 and 1.32.0-beta3 include the patch.\n\n Ensure you’re on the latest Atom release by completing any of the following:\n\n Windows: From the toolbar, click Help -> Check for Updates\n MacOS: From the menu bar, click Atom -> Check for Update\n Linux: Update manually by downloading the latest release from atom.io\n\n Git on the command line and other clients\n\n In order to be protected from the vulnerability, you must update your command-line version of Git, and any other application that may include an embedded version of Git, as they are independent of each other.\n Additional notes\n\n Neither GitHub.com nor GitHub Enterprise are directly affected by the vulnerability. However, as with previously discovered vulnerabilities, GitHub.com will detect malicious repositories, and will reject pushes or API requests attempting to create them. Versions of GitHub Enterprise with this detection will ship on October 9.\n Details of the vulnerability\n\n This vulnerability is very similar to CVE-2017-1000117, as both are option-injection attacks related to submodules. In the earlier attack, a malicious repository would ship a .gitmodules file pointing one of its submodules to a remote repository with an SSH host starting with a dash (-). The ssh program—spawned by Git—would then interpret that as an option. This attack works in a similar way, except that the option-injection is against the child git clone itself.\n\n The problem was reported on September 23 by @joernchen, both to Git’s private security list, as well as to GitHub’s Bug Bounty program. Developers at GitHub worked with the Git community to develop a fix.\n\n The basic fix was clear from the report. However, due to to the similarity to CVE-2017-1000117, we also audited all of the .gitmodules values and implemented stricter checks as appropriate. These checks should prevent a similar vulnerability in another code path. We also implemented detection of potentially malicious submodules as part of Git’s object quality checks (which was made much easier by the infrastructure added during the last submodule-related vulnerability).\n\n The coordinated disclosure date of October 5 was selected by Git developers to allow packagers to prepare for the release. This also provided hosting sites (with custom implementations) ample time to detect and block the attack before it became public. Members of the Git community checked the JGit and libgit2 implementations. Those are not affected by the vulnerability because they clone submodules via function calls rather than separate commands.\n\n We were also able to use the time to scan all repositories on GitHub for evidence of the attack being used in the wild. We’re happy to report that no instances were found (and now, with our detection, none can be added).\n\n Please update your copy of Git soon, and happy cloning!\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (21, 'Hackers Targeting Servers Running Database Services for Mining Cryptocurrency', 'Security researchers have discovered multiple attack campaigns conducted by an established Chinese criminal group that operates worldwide, targeting database servers for mining cryptocurrencies, exfiltrating sensitive data and building a DDoS botnet.\n\n The researchers from security firm GuardiCore Labs have analyzed thousands of attacks launched in recent months and identified at least three attack variants—Hex, Hanako, and Taylor—targeting different MS SQL and MySQL servers for both Windows and Linux.\n\n The goals of all the three variants are different—Hex installs cryptocurrency miners and remote access trojans (RATs) on infected machines, Taylor installs a keylogger and a backdoor, and Hanako uses infected devices to build a DDoS botnet.\n\n So far, researchers have recorded hundreds of Hex and Hanako attacks and tens of thousands of Taylor attacks each month and found that most compromised machines are based in China, and some in Thailand, the United States, Japan and others.\n\n To gain unauthorized access to the targeted database servers, the attackers use brute force attacks and then run a series of predefined SQL commands to gain persistent access and evade audit logs.\n\n What''s interesting? To launch the attacks against database servers and serve malicious files, attackers use a network of already compromised systems, making their attack infrastructure modular and preventing takedown of their malicious activities.');\n INSERT INTO grafit_article (id, title, text) VALUES (22, 'RIP Open Source MySQL', ' This is an excellent opportunity for the Postgres community to step up an promote Postgres.\n\n \n \n rbanffy on Aug 18, 2012 [-]\n\n I think this would be a mistake.\n\n This is an excellent opportunity to demonstrate that anyone can fork the MySQL codebase and create other plug-in replacement databases with it, such as MariaDB and Drizzle.\n\n All that is lost is the MySQL name and brand.\n\n PostgreSQL users and developers must seize the opportunity to show businesses that free software cannot be killed, not even by mighty Oracle. They and, most notably, Microsoft, have been trying to kill it for more than a decade now.\n\n Because the anti-free-software FUD machine (fed in part by Oracle itself) is already having a wonderful time with this.\n\n \n \n Udo on Aug 18, 2012 [-]\n\n I wish I could mod this up a hundred times. PostgreSQL people themselves have been playing into the hands of corporate FUDders with their incessant and inappropriate peddling. MySQL is not your enemy, MS SQL Server is. Oracle''s software empire as a whole certainly is your enemy. Show some solidarity with a fellow open source project!\n\n MySQL and PostgreSQL represent two very different implementation philosophies, and being able to choose between them according to taste and merits is a good thing.\n\n Most of us have suspected that the MySQL project itself was going to die as it was acquired by Oracle, in the same way Open Office died when it was acquired by Oracle. This is a company where good software goes to expire, either due to a deliberate intention or gross incompetence I can''t say but I suspect it''s a mixture of both. However sad that may be for the MySQL (or OpenOffice) brand name, the code itself lives on and continues to evolve within a rich open source ecosystem.\n\n Hence, sensational and petulant \"RIP $PRODUCTNAME\" articles are unnecessary. There is no threat to existing projects based on MySQL or any other successful open source project for that matter. Not only will this stuff be free forever, it will also continue to grow and be developed on its own.\n\n The corporate assassination of open source projects will only work if we let it, it''s a purely psychological game. ');\n INSERT INTO grafit_article (id, title, text) VALUES (23, 'Free Text Sources', 'There are a few interesting things to talk about surrounding free and open textbooks. Quality is one. Usability is another. Why to write one (and/or, why not) is certainly critical. But where can you find these disruptive, open texts?\n\n Not all faculty know there are free and open texts they can use; finding free and/or open textbooks (or even knowing to look) can sometimes be a trick. I knew about one or two sources, and did a little bit more digging. Admittedly, many of the sources of free texts linked below have a technical bent. On one hand, this might be because math, computing, and the sciences are familiar with working openly and giving things away. On the other, it might be because I am a member of the computing faculty, and therefore am most familiar with resources in that space.');\n INSERT INTO grafit_article (id, title, text) VALUES (24, 'Apache Software Foundation Public Mail Archives', 'A collection of all publicly available mail archives from the Apache55 Software Foundation (ASF), taken on July 11, 2011. This collection contains all publicly available email archives from the ASF''s 80+ projects (http://mail-archives.apache.org/mod_mbox/), including mailing lists such as Apache HTTPD Server, Apache Tomcat, Apache Lucene and Solr, Apache Hadoop and many more. Generally speaking, most projects have at least three lists: user, dev and commits, but some have more, some have less. The user lists are where users of the software ask questions on usage, while the dev list usually contains discussions on the development of the project (code, releases, etc.) The commit lists usually consists of automated notifications sent by the various ASF version control tools, like Subversion or CVS, and contain information about changes made to the project''s source code.\n\n Both tarballs and per project sets are available in the snapshot. The tarballs are organized according to project name. Thus, a-d.tar.gz contains all ASF projects that begin with the letters a, b, c or d, such as abdera.apache.org. Files within the project are usually gzipped mbox files.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (26, 'PostgreSQL - Overview', 'PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development phase and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness.\n\n This tutorial will give you a quick start with PostgreSQL and make you comfortable with PostgreSQL programming.\n What is PostgreSQL?\n\n PostgreSQL (pronounced as post-gress-Q-L) is an open source relational database management system (DBMS) developed by a worldwide team of volunteers. PostgreSQL is not controlled by any corporation or other private entity and the source code is available free of charge.\n A Brief History of PostgreSQL\n\n PostgreSQL, originally called Postgres, was created at UCB by a computer science professor named Michael Stonebraker. Stonebraker started Postgres in 1986 as a follow-up project to its predecessor, Ingres, now owned by Computer Associates.\n\n 1977-1985 − A project called INGRES was developed.\n\n Proof-of-concept for relational databases\n\n Established the company Ingres in 1980\n\n Bought by Computer Associates in 1994\n\n 1986-1994 − POSTGRES\n\n Development of the concepts in INGRES with a focus on object orientation and the query language - Quel\n\n The code base of INGRES was not used as a basis for POSTGRES\n\n Commercialized as Illustra (bought by Informix, bought by IBM)\n\n 1994-1995 − Postgres95\n\n Support for SQL was added in 1994\n\n Released as Postgres95 in 1995\n\n Re-released as PostgreSQL 6.0 in 1996\n\n Establishment of the PostgreSQL Global Development Team\n\n Key Features of PostgreSQL\n\n PostgreSQL runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It supports text, images, sounds, and video, and includes programming interfaces for C / C++, Java, Perl, Python, Ruby, Tcl and Open Database Connectivity (ODBC).\n\n PostgreSQL supports a large part of the SQL standard and offers many modern features including the following −\n\n Complex SQL queries\n SQL Sub-selects\n Foreign keys\n Trigger\n Views\n Transactions\n Multiversion concurrency control (MVCC)\n Streaming Replication (as of 9.0)\n Hot Standby (as of 9.0)\n\n You can check official documentation of PostgreSQL to understand the above-mentioned features. PostgreSQL can be extended by the user in many ways. For example by adding new −\n\n Data types\n Functions\n Operators\n Aggregate functions\n Index methods\n\n Procedural Languages Support\n\n PostgreSQL supports four standard procedural languages, which allows the users to write their own code in any of the languages and it can be executed by PostgreSQL database server. These procedural languages are - PL/pgSQL, PL/Tcl, PL/Perl and PL/Python. Besides, other non-standard procedural languages like PL/PHP, PL/V8, PL/Ruby, PL/Java, etc., are also supported.');\n INSERT INTO grafit_article (id, title, text) VALUES (27, 'Setup PostgreSQL on Windows with Docker', 'Over the weekend I finally got the chance to start reading A Curious Moon by Rob Conery which is a book on learning PostgreSQL by following the fictional Dee Yan as she is thrown into database administrator role at an aerospace startup.\n\n I have a lot of experience using Microsoft’s SQL Server, but up until now, I haven’t touched PostgreSQL. For personal projects SQL Server’s cost and be prohibitive and the release of Rob’s book added up to a good time to give PostgreSQL a try.\n Install Directly or not?\n\n On the download section of the official Postgres site, there is an option to download an installer. This is the route I was going to at first, but in Rob’s book, he suggests using a VM for Postgres installation on Windows. This kicked off a lot of searching on my part and didn’t find a good definitive answer on why that is or isn’t the way to do.\n\n In the end, I decided to try and run the Postgres process using Docker instead installing directly on Windows or dealing with a full VM.\n Installing Docker\n\n Head to this link and click the Get Docker link to download the installer. After the install is complete you will have to log out and back in. When I logged back in I got a message about Hyper-V not being enabled.\n\n After logging back in I then got the following message about hardware-assisted virtualization not being enabled.\n\n After tweaking my BIOS settings and logging back in I was greeted by the Docker welcome screen.\n\n Open a command prompt and run the following command.\n\n docker run hello-world\n\n You should output that starts with the following if your installation is working.\n\n Hello from Docker!\n This message shows that your installation appears to be working correctly.\n\n What about Postgres?\n\n Getting up and going with a container running Postgres was pretty simple and could be done with the following command which will create a container and expose the port used by Postgres so it can be accessed from the host.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d postgres\n\n The problem with this approach is if you ever need to rebuild the container for some reason, like a new version of Postgres is released, your data will be lost. Thankfully I found this blog post which shows how to use a secondary container for the data leaving the Postgres container able to be destroyed and recreated as needed. The following is the command I used to create my data container.\n\n docker create -v /var/lib/postgresql/data --name PostgresData alpine\n\n The above creates a container named PostgresData based on the Alpine image. It is important that the -v parameter matches the path that Postgres expects.\n\n Now that we have a container that will keep our data safe let’s create the actual Postgres container with the following command.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d --volumes-from PostgresData postgres\n\n The only difference from the first example run command is the addition of –volumes-from PostgresData which tells the container to use the PostgresData container.\n\n If you run the docker ps -a command it will show you all your containers.\n\n As you can see in my example I have two containers only one of which is actually running. Make sure you don’t remove the data container just because it will never show as running.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (28, 'DIY: A PostgreSQL database server setup anyone can handle', 'When it comes to databases, I''m a fan of MySQL. The open source database can handle just about any load you want to throw at it, and it has lots of powerful tools that can be used to manage it.\n\n The other popular open source database is PostgreSQL, which is cross-platform and is used by numerous applications. Although PostgreSQL is often seen as being as powerful as MySQL, it doesn''t have nearly the number of available tools to make setup and management as easy as its competition. So I''ve written this handy PostgreSQL primer on how to get your database server up and running and ready to use. (Although PostgreSQL is cross-platform, I demonstrate the installation and setup on a Ubuntu 11.04 machine because it''s my platform of choice. The translation to other platforms should be simple.)\n Step 1: Install PostgreSQL\n\n Here are the installation steps on Ubuntu (this installation will also work on any Debian-based distribution):\n\n Open a terminal window.\n Issue the command sudo apt-get install postgresql.\n Type the sudo password necessary to give you admin rights and hit Enter.\n Allow apt to pick up any necessary dependencies.\n\n Once the installation is complete, it''s time to set this baby up.\n Step 2: Change the default user password\n\n Caution: If you don''t follow this step, you will not be able to add databases and administer PostgreSQL, and the database will not be secure.\n\n Here''s how to change the password for the default user. The user in question is postgres, and the password is changed like so:\n\n Open a terminal window.\n Issue the command sudo passwd postgres.\n Type (and confirm) that password to be used for this user.\n\n The postgres user will be the only user on your system that can open the PostgreSQL prompt without defining a database, which means postgres is the only user who can administer PostgreSQL. To test this, change to the postgres user with the command su - postgres and then enter the command psql. You should now be at the Postgres prompt, which looks like:\n\n postgres=#\n\n All other users have to gain access to the prompt like so:\n\n psql DB_NAME\n\n where DB_NAME is the name of an existing database.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (31, 'The Marketing Behind MongoDB', ' 100% of my friends who have used Mongo/similar NoSQL have given up and had a nasty rewrite back to pgSQL.\n\n This seems to be the journey:\n\n 1. Lack of migrations is awesome! We can iterate so quickly for MVP\n\n 2. Get users\n\n 3. Add features, still enjoying the speed of iteration\n\n 4. Get more users\n\n 5. Start building reporting features for enterprise/customer support/product metrics (ie: when the real potential success starts)\n\n 6. Realise you desperately need joins, transactions and other SQL features\n\n 7. Pause product dev for 1-3+ months to migrate back to SQL, or do some weird parallel development process to move it piecemeal back.\n\n I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n My thought is definitely yes.\n\n \n \n brandur on Aug 29, 2017 [-]\n\n > I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n I''ve used Postgres and Mongo pretty extensively, and for any reasonably seasoned developer, the startup overhead of an SQL system is a myth. There may upfront cost to learning how an RDMS and SQL work in the first place, but once you''re familiar with them, they''ll be faster than Mongo on any new project.\n\n The schemaless concept of a document database seems to be the major selling factor in velocity of movement, but once you''ve got a good handle on a migration framework in the vein of ActiveRecord or other popular software, that''s negated completely. It also really doesn''t take long before schemaless starts to cause big problems for you in terms of data consistency -- it''s not just the big players that get bitten by this.\n\n The simplified query language is another one. SQL is a little bit obtuse, but it''s not that bad once you have a handle on it, and a lot of people are familiar with it. Once you add in an ORM layer, the lazy-style access of a framework like Sequel or SQLAlchemy makes the developer experience quite a bit better than any Mongo APIs that I''ve seen. Also, after you get beyond trivial usage, SQL''s flexibility so wildly outstrips Mongo''s query documents that it''s not even worth talking about.\n\n Postgres on the other hand ships with a great management CLI, a very powerful REPL (psql), and features like data types/constraints/transactions that guarantee you correctness with zero effort on your part. I can only speak for myself, but I''d take Postgres to the hackathon any day of the week.\n\n \n \n martinald on Aug 29, 2017 [-]\n\n I totally agree with you, and started writing something about how understanding a good ORM takes nearly all the headache away.\n\n I think the thing people do find slow is a lot of ''documents within documents'' in SQL. It turns out this is usually a bad development pattern long term but it is super fast being able to just add docs inside docs with no configuration. It feels very slow writing foreign keys, navigation props and schemas for this in SQL vs JSON, where you can just dump your object in and you''re done.\n\n Basically; I think with noSQL you get some very short term gain for a lot of long term pain, and you''re right, ORMs and other tooling solves this mostly.\n\n I myself fell for this trap, and while it was a nightmare it actually matured me more as a professional more than anything I''ve ever done recently. Regardless of crazy hype, I don''t think I''ll ever fall for a solution so easily without evaluating it properly.\n\n I think I assumed the \"crowd\" had done the tech due diligence on this stuff and it definitely wasn''t the case. ');\n INSERT INTO grafit_article (id, title, text) VALUES (32, 'Countless NoSQL databases competed to be the database of choice', 'n 2013, 10gen — the company behind MongoDB — moved into a large 30,000 square foot office in Midtown Manhattan.\n\n The transfer into the former New York Times building capped off a tremendous period of growth: the database boasted 4 million downloads, the MongoDB User Groups already attracted 15,000 members, and ~10,000 people had attended a global event in 2012. Their offices were truly global from London to Sydney to Dublin and Barcelona — and a requisite west coast headquarters in Palo Alto.\n\n Despite the traction, many startups using MongoDB faced their own challenges. One part of MongoDB’s success among startups was because some didn''t critically assess 10gen’s marketing message.\n\n As engineers, we often discuss technical attacks (e.g., DDoS, Sybil attacks, security vulnerabilities), but need to spend time debating how to protect ourselves from marketing “attacks”.1 Today, developer marketing is subtle — third party blog posts, content marketing disguised as engineering lessons, biased talks, and sponsored hackathons — not clearly marked content from vendors. As such, startup engineering decisions can hinge on sources that are not impartial.\n\n A large amount of \"engineering\" content — even when written by engineers — is actually marketing, rather than thoughtful content whose aim is to help you make the best decision.\n\n Previously, we looked at the hype around NoSQL and common engineering mistakes to see how MongoDB became so successful. Now, let''s take a look into 10gen''s marketing strategy — as told by their employees.2\n\n 10gen’s marketing strategy is an increasingly common playbook and understanding it is useful for future developer tool decisions.');\n INSERT INTO grafit_article (id, title, text) VALUES (30, 'Comment Arrango', ' ArangoDB always makes for exciting benchmark posts.\n\n I could see myself there in a bowler hat with a fistful of racing chits screaming “go, Postgres, go.”\n\n I’d love to see a competition were the developers of each database got to use the same hardware and data then tune the hell out of their configs, queries, and indices.\n\n Red Bull could sponsor it. I’d buy a T-shirt.\n\n \n \n kbenson 8 months ago [-]\n\n That doesn''t sound that hard to start. Something like RealWorld[1] and the Web Framework Benchmarks[2] combined but for DB workloads. Have one dataset that includes data amenable to OLAP and OLTP, but have separate tests each consisting of OLAP queries, OLTP queries, and combined queries. Choose a low-end, mid-range and high-end set of AWS or GCE instances/configs to normalize against. Let people submit pull requests with new technologies or configs.\n\n You''d want to get some funding to run the tests (or maybe solicit Google or Amazon to see if you could get the instance time donated once a month or something.\n\n If you started small, with maybe a portion of these features, and then scaled up over time, you might actually get to the point where you had tests that emulated a power failure, or master/slave and dual master scenarios and how they handle certain common network errors (split-brain). That would be an amazing resource.\n\n Edit: It occurs to me I probably should have read more of the article, since this is sort of what they are doing already...\n\n 1: https://github.com/gothinkster/realworld\n\n 2: https://www.techempower.com/benchmarks/\n\n \n \n etxm 8 months ago [-]\n\n Yeah after I posted it I started thinking about what it would take and what that would actually look like... and how you’d cheat :)\n\n It would probably require a few different categories with some sort of output assertion to validate the query performed right and a means of tracking CPU, usage ram usage, and execution time.\n\n It would be cool to see things like disaster recovery and chaos proofing as well. ');\n INSERT INTO grafit_article (id, title, text) VALUES (35, 'Applying machine intelligence to GitHub security alerts ', 'Last year, we released security alerts that track security vulnerabilities in Ruby and JavaScript packages. Since then, we’ve identified more than four million of these vulnerabilities and added support for Python. In our launch post, we mentioned that all vulnerabilities with CVE IDs are included in security alerts, but sometimes there are vulnerabilities that are not disclosed in the National Vulnerability Database. Fortunately, our collection of security alerts can be supplemented with vulnerabilities detected from activity within our developer community.\n Leveraging the community\n\n There are many places a project can publicize security fixes within a new version: the CVE feed, various mailing lists and open source groups, or even within its release notes or changelog. Regardless of how projects share this information, some developers within the GitHub community will see the advisory and immediately bump their required versions of the dependency to a known safe version. If detected, we can use the information in these commits to generate security alerts for vulnerabilities which may not have been published in the CVE feed.\n\n On an average day, the dependency graph can track around 10,000 commits to dependency files for any of our supported languages. We can’t manually process this many commits. Instead, we depend on machine intelligence to sift through them and extract those that might be related to a security release.\n\n For this purpose, we created a machine learning model that scans text associated with public commits (the commit message and linked issues or pull requests) to filter out those related to possible security upgrades. With this smaller batch of commits, the model uses the diff to understand how required version ranges have changed. Then it aggregates across a specific timeframe to get a holistic view of all dependencies that a security release might affect. Finally, the model outputs a list of packages and version ranges it thinks require an alert and currently aren’t covered by any known CVE in our system.\n Always quality focused\n\n No machine learning model is perfect. While machine intelligence can sift through thousands of commits in an instant, this anomaly-detection algorithm will still generate false positives for packages where no security patch was released. Security alert quality is a focus for us, so we review all model output before the community receives an alert.\n Learn more');\n INSERT INTO grafit_article (id, title, text) VALUES (29, 'Performance Benchmark 2018', 'I''ve stopped reading database benchmarks, because they are extremely vague. Instead I spend my time optimizing my current solution/stack. For example Postgresql has hundreds of knobs that you can adjust for almost every scenario you can imagine. Sometimes you have a special query and increase the work_mem just for that session. Other cases you adjust the cost settings for another query/session. You can analyze your indexes and index types. And sometimes you need to rewrite parts of a big query.\n\n Learning all this takes time, you are much better off learning more about your chosen technology stack than switching to another technology stack.\n\n Though in a few rare races, you need a different technology to solve your business problem. In most cases they complement your existing solution, like Elasticsearch/Solr for full-text search or Clickhouse for OLAP workloads.\n\n \n \n maxxxxx 8 months ago [-]\n\n Agreed. Switching to another system is expensive and the benefit is pretty questionable.\n\n \n \n emsy 8 months ago [-]\n\n Unless you hit a very specific use-case/bottleneck, which I only ever witnessed once.\n\n \n \n TremendousJudge 8 months ago [-]\n\n expand, please?\n\n \n \n maxxxxx 8 months ago [-]\n\n I imagine something very specific like having a lot of inserts into a table and that being your main use case. Depending on your data some databases may be better than others and that should be easy to measure.\n\n In most real-world cases the requirements however are not very clear and often conflicting so it''s much harder to get data that shows the performance of one system over the other.\n\n \n \n gopalv 8 months ago [-]\n\n > Depending on your data some databases may be better than others and that should be easy to measure.\n\n And the performance difference could be an accidental feature of the design and completely unintentional.\n\n Postgres for instance has a native data engine, so it can store the exact row-ids for a row into an index, but this means that every update to the row needs all indexes to be updated.\n\n Mysql has many data engines (InnoDB and MyISAM to start with), to the row-id is somewhat opaque, so the index stores the primary key which can be pushed to the data engine scans and then have it lookup a row-id internally. This needs an index to be touched for the columns you modify explicitly or if the primary key is updated (which is a usual no-no due to UNIQUE lookup costs).\n\n When you have a single wide table with a huge number of indexes, where you update a lot of dimensions frequently, the performance difference between these two solutions is architectural.\n\n And if you lookup along an index with few updates, but long running open txns, that is also materially different - one lookup versus two.\n\n Though how it came about isn''t really intentional. ');\n \"\"\"\n )]\n","<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('grafit', '0002_article')]\n operations = [migrations.RunSQL(\n \"\"\"\n INSERT INTO grafit_article (id, title, text) VALUES (2, 'MongoDB', 'MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemata. MongoDB is developed by MongoDB Inc., and is published under a combination of the Server Side Public License and the Apache License.\n 10gen software company began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[6]\n\n On October 20, 2017, MongoDB became a publicly-traded company, listed on NASDAQ as MDB with an IPO price of $24 per share.[7] Ad hoc queries\n\n MongoDB supports field, range query, and regular expression searches.[8] Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size.\n Indexing\n\n Fields in a MongoDB document can be indexed with primary and secondary indices.\n Replication\n\n MongoDB provides high availability with replica sets.[9] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.\n Load balancing[10]\n\n MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.\n\n MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. ');\n INSERT INTO grafit_article (id, title, text) VALUES (3, 'NoSQL', 'A NoSQL (originally referring to \"non SQL\" or \"non relational\")[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the \"NoSQL\" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called \"Not only SQL\" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.[7][8]\n\n Motivations for this approach include: simplicity of design, simpler \"horizontal\" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as \"more flexible\" than relational database tables.[9]\n\n Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)\n\n Instead, most NoSQL databases offer a concept of \"eventual consistency\" in which database changes are propagated to all nodes \"eventually\" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases \"do not allow referential integrity constraints to span databases.\"[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing. ');\n INSERT INTO grafit_article (id, title, text) VALUES (4, 'SQL', 'SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd[15] in the early 1970s.[16] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM''s original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s.[16]\n\n Chamberlin and Boyce''s first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973, they began work on SEQUEL.[15] The acronym SEQUEL was later changed to SQL because \"SEQUEL\" was a trademark of the UK-based Hawker Siddeley aircraft company.[17]\n\n In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce, and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. By 1986, ANSI and ISO standard groups officially adopted the standard \"Database Language SQL\" language definition. New versions of the standard were published in 1989, 1992, 1996, 1999, 2003, 2006, 2008, 2011,[15] and most recently, 2016. After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.[18] ');\n INSERT INTO grafit_article (id, title, text) VALUES (5, 'MySQL', 'Built on MySQL Enterprise Edition and powered by the Oracle Cloud, Oracle MySQL Cloud Service provides a simple, automated, integrated and enterprise ready MySQL cloud service, enabling organizations to increase business agility and reduce costs. \"Relying on the MySQL engine as the low-level storage layer has allowed us to very quickly build a robust system.\"\n \n\n \"We have successfully implemented MySQL Cluster Carrier Grade Edition for our highly mission critical XDMS application which will enable the next generation of converged services.\"\n \n\n \"We found that MySQL was the best database in terms of the price-point and functionality it offers up. The benefits that MySQL brings to our Brightmail product is its relaiability, robustness and very low-cost administration costs.\"');\n INSERT INTO grafit_article (id, title, text) VALUES (6, 'Critical Flaw Reported In phpMyAdmin Lets Attackers Damage Databases', 'A critical security vulnerability has been reported in phpMyAdmin—one of the most popular applications for managing the MySQL database—which could allow remote attackers to perform dangerous database operations just by tricking administrators into clicking a link.\n\n Discovered by an Indian security researcher, Ashutosh Barot, the vulnerability is a cross-site request forgery (CSRF) attack and affects phpMyAdmin versions 4.7.x (prior to 4.7.7).\n\n Cross-site request forgery vulnerability, also known as XSRF, is an attack wherein an attacker tricks an authenticated user into executing an unwanted action.\n\n According to an advisory released by phpMyAdmin, \"by deceiving a user to click on a crafted URL, it is possible to perform harmful database operations such as deleting records, dropping/truncating tables, etc.\"\n\n phpMyAdmin is a free and open source administration tool for MySQL and MariaDB and is widely used to manage the database for websites created with WordPress, Joomla, and many other content management platforms.\n\n Moreover, a lot of hosting providers use phpMyAdmin to offer their customers a convenient way to organize their databases.\n Barot has also released a video, as shown above, demonstrating how a remote attacker can make database admins unknowingly delete (DROP) an entire table from the database just by tricking them into clicking a specially crafted link.\n\n \"A feature of phpMyAdmin was using a GET request and after that POST request for Database operations such as DROP TABLE table_name; GET requests must be protected against CSRF attacks. In this case, POST requests were used which were sent through URL (for bookmarking purpose may be); it was possible for an attacker to trick a database admin into clicking a button and perform a drop table database query of the attacker’s choice.\" Barot explains in a blog post.\n\n However, performing this attack is not simple as it may sound. To prepare a CSRF attack URL, the attacker should be aware of the name of targeted database and table.\n\n \"If a user executes a query on the database by clicking insert, DROP, etc. buttons, the URL will contain database name and table name,\" Barot says. \"This vulnerability can result in the disclosure of sensitive information as the URL is stored at various places such as browser history, SIEM logs, Firewall Logs, ISP Logs, etc.\"\n\n Barot reported the vulnerability to phpMyAdmin developers, who confirmed his finding and released phpMyAdmin 4.7.7 to address this issue. So administrators are highly recommended to update their installations as soon as possible.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (25, 'Death By Database', 'The following is a true story, but with names changed.\n\n When I work with clients to build software, I take the usual steps of understanding their needs, gathering requirements, learning about their customers, and so on. At this point I have a model on paper of roughly what the software is intended to do, so they get surprised when I immediately turn to database design.\n\n \"Who care about database design? What about mockups? What about workflows?\"\n\n Let me tell you about \"Bob''s Luxury Goods.\" I worked for this company many years ago and they had a retail store selling ... you guessed it ... luxury goods. They''d ask all customers for a billing address and if they had a different delivery address. At the database level, they had a \"one-to-many\" relationship between customers and addresses.\n\n That was their first problem. A customer''s partner might come into Bob''s and order something and if the address was entered correctly it would be flagged as \"in use\" and we had to use a different address or deliberately enter a typo. Fortunately, addresses were case-sensitive, so many people had UPPER-CASE ADDRESSES.\n\n We should have had a many-to-many relationship between customers and addresses so we could handle the case where more than one person would share the same address, but we didn''t. Further, I was never allocated the time to fix the database because it was \"cheaper\" to remove the restriction on \"flagged\" addresses and allow a duplicate address to be used.\n\n Naturally, being a luxury goods company, we had many repeat customers and sometimes they would move and if we didn''t find the duplicate address, or the address with the \"typo\", we might update the address for one partner, but not the other. That was a headache, but it didn''t happen frequently enough for management to worry about it.\n\n That''s when the marketing department had a brilliant, inexpensive idea. You see, we periodically did mass mailings of special events to our customers. Since we had the software to do mass mailings, why not import a mailing list of all addresses in high net worth areas and mail everyone about upcoming special events? So the company went ahead and bought a database with all of these addresses, but forgot to mention to me that I was supposed to implement this.\n\n Except that every address record had the customer id embedded in it, so we couldn''t enter an address without a customer.\n\n \"Curtis,\" they said, \"just enter a dummy customer called ''Occupant'' and attach all addresses to that.\"\n\n Except you couldn''t enter a customer without an order.\n\n Except you couldn''t enter an order without at least one item on it.\n\n Except you couldn''t enter an item unless it was listed in inventory.\n\n Except that reserved the \"inventory\" item and made it unavailable.\n\n Except, except, except ...\n\n It came down to trying to create a fake customer, with a fake order, with a fake item, with a fake item category, with a \"paid\" invoice, with exceptions sprinkled throughout the codebase to handle all of these special cases and probably more that I no longer remember.\n\n Then, and only then, could I write the code to provide \"generic\" mass mailings. Management decided it was easier to hire an outside company to handle the mailing list for them.\n\n If they had simply had a proper database design up front, they could have reused their existing system with little trouble.\n\n That''s what bad database design costs you and why I usually start with that before writing my software.\n\n Note: if you''re not familiar with database design, here''s a talk I give where I make it fairly simple to understand. I mostly avoid big words.');\n INSERT INTO grafit_article (id, title, text) VALUES (33, 'GitHub Actions: built by you, run by us', 'Yesterday at GitHub Universe, we announced GitHub Actions, a new way to automate and customize your workflows. Configuring the apps and services that make up your development cycle takes significant time and effort. GitHub Actions applies open source principles to workflow automation, weaving together the tools you use from idea to production into one complete workflow. You can also create, share, and discover any actions your projects require, just as you would create, share, and discover code on GitHub.\n\n Learn more about actions\n\n As we prepared for Universe, we shared GitHub Actions with a group of customers, integrators, and open source maintainers to see what they could do. In just a few short weeks, talented teams and individuals alike have created hundreds of GitHub Actions. During today’s Universe keynote, we heard directly from developers, and we’re excited to share their work with you');\n INSERT INTO grafit_article (id, title, text) VALUES (34, 'Git Submodule Vulnerability Announced ', '\n\n The Git project has disclosed CVE-2018-17456, a vulnerability in Git that can cause arbitrary code to be executed when a user clones a malicious repository. Git v2.19.1 has been released with a fix, along with backports in v2.14.5, v2.15.3, v2.16.5, v2.17.2, and v2.18.1. We encourage all users to update their clients to protect themselves.\n\n Until you’ve updated, you can protect yourself by avoiding submodules from untrusted repositories. This includes commands such as git clone --recurse-submodules and git submodule update.\n Affected products\n GitHub Desktop\n\n GitHub Desktop versions 1.4.1 and older included an embedded version of Git that was affected by this vulnerability. We encourage all GitHub Desktop users to update to the newest version (1.4.2 and 1.4.3-beta0) available today in the Desktop app.\n Atom\n\n Atom included the same embedded Git and was also affected. Releases 1.31.2 and 1.32.0-beta3 include the patch.\n\n Ensure you’re on the latest Atom release by completing any of the following:\n\n Windows: From the toolbar, click Help -> Check for Updates\n MacOS: From the menu bar, click Atom -> Check for Update\n Linux: Update manually by downloading the latest release from atom.io\n\n Git on the command line and other clients\n\n In order to be protected from the vulnerability, you must update your command-line version of Git, and any other application that may include an embedded version of Git, as they are independent of each other.\n Additional notes\n\n Neither GitHub.com nor GitHub Enterprise are directly affected by the vulnerability. However, as with previously discovered vulnerabilities, GitHub.com will detect malicious repositories, and will reject pushes or API requests attempting to create them. Versions of GitHub Enterprise with this detection will ship on October 9.\n Details of the vulnerability\n\n This vulnerability is very similar to CVE-2017-1000117, as both are option-injection attacks related to submodules. In the earlier attack, a malicious repository would ship a .gitmodules file pointing one of its submodules to a remote repository with an SSH host starting with a dash (-). The ssh program—spawned by Git—would then interpret that as an option. This attack works in a similar way, except that the option-injection is against the child git clone itself.\n\n The problem was reported on September 23 by @joernchen, both to Git’s private security list, as well as to GitHub’s Bug Bounty program. Developers at GitHub worked with the Git community to develop a fix.\n\n The basic fix was clear from the report. However, due to to the similarity to CVE-2017-1000117, we also audited all of the .gitmodules values and implemented stricter checks as appropriate. These checks should prevent a similar vulnerability in another code path. We also implemented detection of potentially malicious submodules as part of Git’s object quality checks (which was made much easier by the infrastructure added during the last submodule-related vulnerability).\n\n The coordinated disclosure date of October 5 was selected by Git developers to allow packagers to prepare for the release. This also provided hosting sites (with custom implementations) ample time to detect and block the attack before it became public. Members of the Git community checked the JGit and libgit2 implementations. Those are not affected by the vulnerability because they clone submodules via function calls rather than separate commands.\n\n We were also able to use the time to scan all repositories on GitHub for evidence of the attack being used in the wild. We’re happy to report that no instances were found (and now, with our detection, none can be added).\n\n Please update your copy of Git soon, and happy cloning!\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (21, 'Hackers Targeting Servers Running Database Services for Mining Cryptocurrency', 'Security researchers have discovered multiple attack campaigns conducted by an established Chinese criminal group that operates worldwide, targeting database servers for mining cryptocurrencies, exfiltrating sensitive data and building a DDoS botnet.\n\n The researchers from security firm GuardiCore Labs have analyzed thousands of attacks launched in recent months and identified at least three attack variants—Hex, Hanako, and Taylor—targeting different MS SQL and MySQL servers for both Windows and Linux.\n\n The goals of all the three variants are different—Hex installs cryptocurrency miners and remote access trojans (RATs) on infected machines, Taylor installs a keylogger and a backdoor, and Hanako uses infected devices to build a DDoS botnet.\n\n So far, researchers have recorded hundreds of Hex and Hanako attacks and tens of thousands of Taylor attacks each month and found that most compromised machines are based in China, and some in Thailand, the United States, Japan and others.\n\n To gain unauthorized access to the targeted database servers, the attackers use brute force attacks and then run a series of predefined SQL commands to gain persistent access and evade audit logs.\n\n What''s interesting? To launch the attacks against database servers and serve malicious files, attackers use a network of already compromised systems, making their attack infrastructure modular and preventing takedown of their malicious activities.');\n INSERT INTO grafit_article (id, title, text) VALUES (22, 'RIP Open Source MySQL', ' This is an excellent opportunity for the Postgres community to step up an promote Postgres.\n\n \n \n rbanffy on Aug 18, 2012 [-]\n\n I think this would be a mistake.\n\n This is an excellent opportunity to demonstrate that anyone can fork the MySQL codebase and create other plug-in replacement databases with it, such as MariaDB and Drizzle.\n\n All that is lost is the MySQL name and brand.\n\n PostgreSQL users and developers must seize the opportunity to show businesses that free software cannot be killed, not even by mighty Oracle. They and, most notably, Microsoft, have been trying to kill it for more than a decade now.\n\n Because the anti-free-software FUD machine (fed in part by Oracle itself) is already having a wonderful time with this.\n\n \n \n Udo on Aug 18, 2012 [-]\n\n I wish I could mod this up a hundred times. PostgreSQL people themselves have been playing into the hands of corporate FUDders with their incessant and inappropriate peddling. MySQL is not your enemy, MS SQL Server is. Oracle''s software empire as a whole certainly is your enemy. Show some solidarity with a fellow open source project!\n\n MySQL and PostgreSQL represent two very different implementation philosophies, and being able to choose between them according to taste and merits is a good thing.\n\n Most of us have suspected that the MySQL project itself was going to die as it was acquired by Oracle, in the same way Open Office died when it was acquired by Oracle. This is a company where good software goes to expire, either due to a deliberate intention or gross incompetence I can''t say but I suspect it''s a mixture of both. However sad that may be for the MySQL (or OpenOffice) brand name, the code itself lives on and continues to evolve within a rich open source ecosystem.\n\n Hence, sensational and petulant \"RIP $PRODUCTNAME\" articles are unnecessary. There is no threat to existing projects based on MySQL or any other successful open source project for that matter. Not only will this stuff be free forever, it will also continue to grow and be developed on its own.\n\n The corporate assassination of open source projects will only work if we let it, it''s a purely psychological game. ');\n INSERT INTO grafit_article (id, title, text) VALUES (23, 'Free Text Sources', 'There are a few interesting things to talk about surrounding free and open textbooks. Quality is one. Usability is another. Why to write one (and/or, why not) is certainly critical. But where can you find these disruptive, open texts?\n\n Not all faculty know there are free and open texts they can use; finding free and/or open textbooks (or even knowing to look) can sometimes be a trick. I knew about one or two sources, and did a little bit more digging. Admittedly, many of the sources of free texts linked below have a technical bent. On one hand, this might be because math, computing, and the sciences are familiar with working openly and giving things away. On the other, it might be because I am a member of the computing faculty, and therefore am most familiar with resources in that space.');\n INSERT INTO grafit_article (id, title, text) VALUES (24, 'Apache Software Foundation Public Mail Archives', 'A collection of all publicly available mail archives from the Apache55 Software Foundation (ASF), taken on July 11, 2011. This collection contains all publicly available email archives from the ASF''s 80+ projects (http://mail-archives.apache.org/mod_mbox/), including mailing lists such as Apache HTTPD Server, Apache Tomcat, Apache Lucene and Solr, Apache Hadoop and many more. Generally speaking, most projects have at least three lists: user, dev and commits, but some have more, some have less. The user lists are where users of the software ask questions on usage, while the dev list usually contains discussions on the development of the project (code, releases, etc.) The commit lists usually consists of automated notifications sent by the various ASF version control tools, like Subversion or CVS, and contain information about changes made to the project''s source code.\n\n Both tarballs and per project sets are available in the snapshot. The tarballs are organized according to project name. Thus, a-d.tar.gz contains all ASF projects that begin with the letters a, b, c or d, such as abdera.apache.org. Files within the project are usually gzipped mbox files.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (26, 'PostgreSQL - Overview', 'PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development phase and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness.\n\n This tutorial will give you a quick start with PostgreSQL and make you comfortable with PostgreSQL programming.\n What is PostgreSQL?\n\n PostgreSQL (pronounced as post-gress-Q-L) is an open source relational database management system (DBMS) developed by a worldwide team of volunteers. PostgreSQL is not controlled by any corporation or other private entity and the source code is available free of charge.\n A Brief History of PostgreSQL\n\n PostgreSQL, originally called Postgres, was created at UCB by a computer science professor named Michael Stonebraker. Stonebraker started Postgres in 1986 as a follow-up project to its predecessor, Ingres, now owned by Computer Associates.\n\n 1977-1985 − A project called INGRES was developed.\n\n Proof-of-concept for relational databases\n\n Established the company Ingres in 1980\n\n Bought by Computer Associates in 1994\n\n 1986-1994 − POSTGRES\n\n Development of the concepts in INGRES with a focus on object orientation and the query language - Quel\n\n The code base of INGRES was not used as a basis for POSTGRES\n\n Commercialized as Illustra (bought by Informix, bought by IBM)\n\n 1994-1995 − Postgres95\n\n Support for SQL was added in 1994\n\n Released as Postgres95 in 1995\n\n Re-released as PostgreSQL 6.0 in 1996\n\n Establishment of the PostgreSQL Global Development Team\n\n Key Features of PostgreSQL\n\n PostgreSQL runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It supports text, images, sounds, and video, and includes programming interfaces for C / C++, Java, Perl, Python, Ruby, Tcl and Open Database Connectivity (ODBC).\n\n PostgreSQL supports a large part of the SQL standard and offers many modern features including the following −\n\n Complex SQL queries\n SQL Sub-selects\n Foreign keys\n Trigger\n Views\n Transactions\n Multiversion concurrency control (MVCC)\n Streaming Replication (as of 9.0)\n Hot Standby (as of 9.0)\n\n You can check official documentation of PostgreSQL to understand the above-mentioned features. PostgreSQL can be extended by the user in many ways. For example by adding new −\n\n Data types\n Functions\n Operators\n Aggregate functions\n Index methods\n\n Procedural Languages Support\n\n PostgreSQL supports four standard procedural languages, which allows the users to write their own code in any of the languages and it can be executed by PostgreSQL database server. These procedural languages are - PL/pgSQL, PL/Tcl, PL/Perl and PL/Python. Besides, other non-standard procedural languages like PL/PHP, PL/V8, PL/Ruby, PL/Java, etc., are also supported.');\n INSERT INTO grafit_article (id, title, text) VALUES (27, 'Setup PostgreSQL on Windows with Docker', 'Over the weekend I finally got the chance to start reading A Curious Moon by Rob Conery which is a book on learning PostgreSQL by following the fictional Dee Yan as she is thrown into database administrator role at an aerospace startup.\n\n I have a lot of experience using Microsoft’s SQL Server, but up until now, I haven’t touched PostgreSQL. For personal projects SQL Server’s cost and be prohibitive and the release of Rob’s book added up to a good time to give PostgreSQL a try.\n Install Directly or not?\n\n On the download section of the official Postgres site, there is an option to download an installer. This is the route I was going to at first, but in Rob’s book, he suggests using a VM for Postgres installation on Windows. This kicked off a lot of searching on my part and didn’t find a good definitive answer on why that is or isn’t the way to do.\n\n In the end, I decided to try and run the Postgres process using Docker instead installing directly on Windows or dealing with a full VM.\n Installing Docker\n\n Head to this link and click the Get Docker link to download the installer. After the install is complete you will have to log out and back in. When I logged back in I got a message about Hyper-V not being enabled.\n\n After logging back in I then got the following message about hardware-assisted virtualization not being enabled.\n\n After tweaking my BIOS settings and logging back in I was greeted by the Docker welcome screen.\n\n Open a command prompt and run the following command.\n\n docker run hello-world\n\n You should output that starts with the following if your installation is working.\n\n Hello from Docker!\n This message shows that your installation appears to be working correctly.\n\n What about Postgres?\n\n Getting up and going with a container running Postgres was pretty simple and could be done with the following command which will create a container and expose the port used by Postgres so it can be accessed from the host.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d postgres\n\n The problem with this approach is if you ever need to rebuild the container for some reason, like a new version of Postgres is released, your data will be lost. Thankfully I found this blog post which shows how to use a secondary container for the data leaving the Postgres container able to be destroyed and recreated as needed. The following is the command I used to create my data container.\n\n docker create -v /var/lib/postgresql/data --name PostgresData alpine\n\n The above creates a container named PostgresData based on the Alpine image. It is important that the -v parameter matches the path that Postgres expects.\n\n Now that we have a container that will keep our data safe let’s create the actual Postgres container with the following command.\n\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d --volumes-from PostgresData postgres\n\n The only difference from the first example run command is the addition of –volumes-from PostgresData which tells the container to use the PostgresData container.\n\n If you run the docker ps -a command it will show you all your containers.\n\n As you can see in my example I have two containers only one of which is actually running. Make sure you don’t remove the data container just because it will never show as running.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (28, 'DIY: A PostgreSQL database server setup anyone can handle', 'When it comes to databases, I''m a fan of MySQL. The open source database can handle just about any load you want to throw at it, and it has lots of powerful tools that can be used to manage it.\n\n The other popular open source database is PostgreSQL, which is cross-platform and is used by numerous applications. Although PostgreSQL is often seen as being as powerful as MySQL, it doesn''t have nearly the number of available tools to make setup and management as easy as its competition. So I''ve written this handy PostgreSQL primer on how to get your database server up and running and ready to use. (Although PostgreSQL is cross-platform, I demonstrate the installation and setup on a Ubuntu 11.04 machine because it''s my platform of choice. The translation to other platforms should be simple.)\n Step 1: Install PostgreSQL\n\n Here are the installation steps on Ubuntu (this installation will also work on any Debian-based distribution):\n\n Open a terminal window.\n Issue the command sudo apt-get install postgresql.\n Type the sudo password necessary to give you admin rights and hit Enter.\n Allow apt to pick up any necessary dependencies.\n\n Once the installation is complete, it''s time to set this baby up.\n Step 2: Change the default user password\n\n Caution: If you don''t follow this step, you will not be able to add databases and administer PostgreSQL, and the database will not be secure.\n\n Here''s how to change the password for the default user. The user in question is postgres, and the password is changed like so:\n\n Open a terminal window.\n Issue the command sudo passwd postgres.\n Type (and confirm) that password to be used for this user.\n\n The postgres user will be the only user on your system that can open the PostgreSQL prompt without defining a database, which means postgres is the only user who can administer PostgreSQL. To test this, change to the postgres user with the command su - postgres and then enter the command psql. You should now be at the Postgres prompt, which looks like:\n\n postgres=#\n\n All other users have to gain access to the prompt like so:\n\n psql DB_NAME\n\n where DB_NAME is the name of an existing database.\n ');\n INSERT INTO grafit_article (id, title, text) VALUES (31, 'The Marketing Behind MongoDB', ' 100% of my friends who have used Mongo/similar NoSQL have given up and had a nasty rewrite back to pgSQL.\n\n This seems to be the journey:\n\n 1. Lack of migrations is awesome! We can iterate so quickly for MVP\n\n 2. Get users\n\n 3. Add features, still enjoying the speed of iteration\n\n 4. Get more users\n\n 5. Start building reporting features for enterprise/customer support/product metrics (ie: when the real potential success starts)\n\n 6. Realise you desperately need joins, transactions and other SQL features\n\n 7. Pause product dev for 1-3+ months to migrate back to SQL, or do some weird parallel development process to move it piecemeal back.\n\n I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n My thought is definitely yes.\n\n \n \n brandur on Aug 29, 2017 [-]\n\n > I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\n\n I''ve used Postgres and Mongo pretty extensively, and for any reasonably seasoned developer, the startup overhead of an SQL system is a myth. There may upfront cost to learning how an RDMS and SQL work in the first place, but once you''re familiar with them, they''ll be faster than Mongo on any new project.\n\n The schemaless concept of a document database seems to be the major selling factor in velocity of movement, but once you''ve got a good handle on a migration framework in the vein of ActiveRecord or other popular software, that''s negated completely. It also really doesn''t take long before schemaless starts to cause big problems for you in terms of data consistency -- it''s not just the big players that get bitten by this.\n\n The simplified query language is another one. SQL is a little bit obtuse, but it''s not that bad once you have a handle on it, and a lot of people are familiar with it. Once you add in an ORM layer, the lazy-style access of a framework like Sequel or SQLAlchemy makes the developer experience quite a bit better than any Mongo APIs that I''ve seen. Also, after you get beyond trivial usage, SQL''s flexibility so wildly outstrips Mongo''s query documents that it''s not even worth talking about.\n\n Postgres on the other hand ships with a great management CLI, a very powerful REPL (psql), and features like data types/constraints/transactions that guarantee you correctness with zero effort on your part. I can only speak for myself, but I''d take Postgres to the hackathon any day of the week.\n\n \n \n martinald on Aug 29, 2017 [-]\n\n I totally agree with you, and started writing something about how understanding a good ORM takes nearly all the headache away.\n\n I think the thing people do find slow is a lot of ''documents within documents'' in SQL. It turns out this is usually a bad development pattern long term but it is super fast being able to just add docs inside docs with no configuration. It feels very slow writing foreign keys, navigation props and schemas for this in SQL vs JSON, where you can just dump your object in and you''re done.\n\n Basically; I think with noSQL you get some very short term gain for a lot of long term pain, and you''re right, ORMs and other tooling solves this mostly.\n\n I myself fell for this trap, and while it was a nightmare it actually matured me more as a professional more than anything I''ve ever done recently. Regardless of crazy hype, I don''t think I''ll ever fall for a solution so easily without evaluating it properly.\n\n I think I assumed the \"crowd\" had done the tech due diligence on this stuff and it definitely wasn''t the case. ');\n INSERT INTO grafit_article (id, title, text) VALUES (32, 'Countless NoSQL databases competed to be the database of choice', 'n 2013, 10gen — the company behind MongoDB — moved into a large 30,000 square foot office in Midtown Manhattan.\n\n The transfer into the former New York Times building capped off a tremendous period of growth: the database boasted 4 million downloads, the MongoDB User Groups already attracted 15,000 members, and ~10,000 people had attended a global event in 2012. Their offices were truly global from London to Sydney to Dublin and Barcelona — and a requisite west coast headquarters in Palo Alto.\n\n Despite the traction, many startups using MongoDB faced their own challenges. One part of MongoDB’s success among startups was because some didn''t critically assess 10gen’s marketing message.\n\n As engineers, we often discuss technical attacks (e.g., DDoS, Sybil attacks, security vulnerabilities), but need to spend time debating how to protect ourselves from marketing “attacks”.1 Today, developer marketing is subtle — third party blog posts, content marketing disguised as engineering lessons, biased talks, and sponsored hackathons — not clearly marked content from vendors. As such, startup engineering decisions can hinge on sources that are not impartial.\n\n A large amount of \"engineering\" content — even when written by engineers — is actually marketing, rather than thoughtful content whose aim is to help you make the best decision.\n\n Previously, we looked at the hype around NoSQL and common engineering mistakes to see how MongoDB became so successful. Now, let''s take a look into 10gen''s marketing strategy — as told by their employees.2\n\n 10gen’s marketing strategy is an increasingly common playbook and understanding it is useful for future developer tool decisions.');\n INSERT INTO grafit_article (id, title, text) VALUES (30, 'Comment Arrango', ' ArangoDB always makes for exciting benchmark posts.\n\n I could see myself there in a bowler hat with a fistful of racing chits screaming “go, Postgres, go.”\n\n I’d love to see a competition were the developers of each database got to use the same hardware and data then tune the hell out of their configs, queries, and indices.\n\n Red Bull could sponsor it. I’d buy a T-shirt.\n\n \n \n kbenson 8 months ago [-]\n\n That doesn''t sound that hard to start. Something like RealWorld[1] and the Web Framework Benchmarks[2] combined but for DB workloads. Have one dataset that includes data amenable to OLAP and OLTP, but have separate tests each consisting of OLAP queries, OLTP queries, and combined queries. Choose a low-end, mid-range and high-end set of AWS or GCE instances/configs to normalize against. Let people submit pull requests with new technologies or configs.\n\n You''d want to get some funding to run the tests (or maybe solicit Google or Amazon to see if you could get the instance time donated once a month or something.\n\n If you started small, with maybe a portion of these features, and then scaled up over time, you might actually get to the point where you had tests that emulated a power failure, or master/slave and dual master scenarios and how they handle certain common network errors (split-brain). That would be an amazing resource.\n\n Edit: It occurs to me I probably should have read more of the article, since this is sort of what they are doing already...\n\n 1: https://github.com/gothinkster/realworld\n\n 2: https://www.techempower.com/benchmarks/\n\n \n \n etxm 8 months ago [-]\n\n Yeah after I posted it I started thinking about what it would take and what that would actually look like... and how you’d cheat :)\n\n It would probably require a few different categories with some sort of output assertion to validate the query performed right and a means of tracking CPU, usage ram usage, and execution time.\n\n It would be cool to see things like disaster recovery and chaos proofing as well. ');\n INSERT INTO grafit_article (id, title, text) VALUES (35, 'Applying machine intelligence to GitHub security alerts ', 'Last year, we released security alerts that track security vulnerabilities in Ruby and JavaScript packages. Since then, we’ve identified more than four million of these vulnerabilities and added support for Python. In our launch post, we mentioned that all vulnerabilities with CVE IDs are included in security alerts, but sometimes there are vulnerabilities that are not disclosed in the National Vulnerability Database. Fortunately, our collection of security alerts can be supplemented with vulnerabilities detected from activity within our developer community.\n Leveraging the community\n\n There are many places a project can publicize security fixes within a new version: the CVE feed, various mailing lists and open source groups, or even within its release notes or changelog. Regardless of how projects share this information, some developers within the GitHub community will see the advisory and immediately bump their required versions of the dependency to a known safe version. If detected, we can use the information in these commits to generate security alerts for vulnerabilities which may not have been published in the CVE feed.\n\n On an average day, the dependency graph can track around 10,000 commits to dependency files for any of our supported languages. We can’t manually process this many commits. Instead, we depend on machine intelligence to sift through them and extract those that might be related to a security release.\n\n For this purpose, we created a machine learning model that scans text associated with public commits (the commit message and linked issues or pull requests) to filter out those related to possible security upgrades. With this smaller batch of commits, the model uses the diff to understand how required version ranges have changed. Then it aggregates across a specific timeframe to get a holistic view of all dependencies that a security release might affect. Finally, the model outputs a list of packages and version ranges it thinks require an alert and currently aren’t covered by any known CVE in our system.\n Always quality focused\n\n No machine learning model is perfect. While machine intelligence can sift through thousands of commits in an instant, this anomaly-detection algorithm will still generate false positives for packages where no security patch was released. Security alert quality is a focus for us, so we review all model output before the community receives an alert.\n Learn more');\n INSERT INTO grafit_article (id, title, text) VALUES (29, 'Performance Benchmark 2018', 'I''ve stopped reading database benchmarks, because they are extremely vague. Instead I spend my time optimizing my current solution/stack. For example Postgresql has hundreds of knobs that you can adjust for almost every scenario you can imagine. Sometimes you have a special query and increase the work_mem just for that session. Other cases you adjust the cost settings for another query/session. You can analyze your indexes and index types. And sometimes you need to rewrite parts of a big query.\n\n Learning all this takes time, you are much better off learning more about your chosen technology stack than switching to another technology stack.\n\n Though in a few rare races, you need a different technology to solve your business problem. In most cases they complement your existing solution, like Elasticsearch/Solr for full-text search or Clickhouse for OLAP workloads.\n\n \n \n maxxxxx 8 months ago [-]\n\n Agreed. Switching to another system is expensive and the benefit is pretty questionable.\n\n \n \n emsy 8 months ago [-]\n\n Unless you hit a very specific use-case/bottleneck, which I only ever witnessed once.\n\n \n \n TremendousJudge 8 months ago [-]\n\n expand, please?\n\n \n \n maxxxxx 8 months ago [-]\n\n I imagine something very specific like having a lot of inserts into a table and that being your main use case. Depending on your data some databases may be better than others and that should be easy to measure.\n\n In most real-world cases the requirements however are not very clear and often conflicting so it''s much harder to get data that shows the performance of one system over the other.\n\n \n \n gopalv 8 months ago [-]\n\n > Depending on your data some databases may be better than others and that should be easy to measure.\n\n And the performance difference could be an accidental feature of the design and completely unintentional.\n\n Postgres for instance has a native data engine, so it can store the exact row-ids for a row into an index, but this means that every update to the row needs all indexes to be updated.\n\n Mysql has many data engines (InnoDB and MyISAM to start with), to the row-id is somewhat opaque, so the index stores the primary key which can be pushed to the data engine scans and then have it lookup a row-id internally. This needs an index to be touched for the columns you modify explicitly or if the primary key is updated (which is a usual no-no due to UNIQUE lookup costs).\n\n When you have a single wide table with a huge number of indexes, where you update a lot of dimensions frequently, the performance difference between these two solutions is architectural.\n\n And if you lookup along an index with few updates, but long running open txns, that is also materially different - one lookup versus two.\n\n Though how it came about isn''t really intentional. ');\n \"\"\"\n )]\n","<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n","<import token>\n<class token>\n"],"string":"[\n \"# Generated by Django 2.1.2 on 2018-10-25 09:36\\n\\nimport django.contrib.auth.models\\nimport django.contrib.auth.validators\\nfrom django.db import migrations, models\\nimport django.utils.timezone\\nimport uuid\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('grafit', '0002_article'),\\n ]\\n\\n operations = [\\n migrations.RunSQL(\\\"\\\"\\\"\\n INSERT INTO grafit_article (id, title, text) VALUES (2, 'MongoDB', 'MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemata. MongoDB is developed by MongoDB Inc., and is published under a combination of the Server Side Public License and the Apache License.\\n 10gen software company began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[6]\\n\\n On October 20, 2017, MongoDB became a publicly-traded company, listed on NASDAQ as MDB with an IPO price of $24 per share.[7] Ad hoc queries\\n\\n MongoDB supports field, range query, and regular expression searches.[8] Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size.\\n Indexing\\n\\n Fields in a MongoDB document can be indexed with primary and secondary indices.\\n Replication\\n\\n MongoDB provides high availability with replica sets.[9] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.\\n Load balancing[10]\\n\\n MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.\\n\\n MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (3, 'NoSQL', 'A NoSQL (originally referring to \\\"non SQL\\\" or \\\"non relational\\\")[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the \\\"NoSQL\\\" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called \\\"Not only SQL\\\" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.[7][8]\\n\\n Motivations for this approach include: simplicity of design, simpler \\\"horizontal\\\" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as \\\"more flexible\\\" than relational database tables.[9]\\n\\n Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)\\n\\n Instead, most NoSQL databases offer a concept of \\\"eventual consistency\\\" in which database changes are propagated to all nodes \\\"eventually\\\" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases \\\"do not allow referential integrity constraints to span databases.\\\"[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (4, 'SQL', 'SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd[15] in the early 1970s.[16] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM''s original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s.[16]\\n\\n Chamberlin and Boyce''s first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973, they began work on SEQUEL.[15] The acronym SEQUEL was later changed to SQL because \\\"SEQUEL\\\" was a trademark of the UK-based Hawker Siddeley aircraft company.[17]\\n\\n In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce, and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. By 1986, ANSI and ISO standard groups officially adopted the standard \\\"Database Language SQL\\\" language definition. New versions of the standard were published in 1989, 1992, 1996, 1999, 2003, 2006, 2008, 2011,[15] and most recently, 2016. After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.[18] ');\\n INSERT INTO grafit_article (id, title, text) VALUES (5, 'MySQL', 'Built on MySQL Enterprise Edition and powered by the Oracle Cloud, Oracle MySQL Cloud Service provides a simple, automated, integrated and enterprise ready MySQL cloud service, enabling organizations to increase business agility and reduce costs. \\\"Relying on the MySQL engine as the low-level storage layer has allowed us to very quickly build a robust system.\\\"\\n \\n\\n \\\"We have successfully implemented MySQL Cluster Carrier Grade Edition for our highly mission critical XDMS application which will enable the next generation of converged services.\\\"\\n \\n\\n \\\"We found that MySQL was the best database in terms of the price-point and functionality it offers up. The benefits that MySQL brings to our Brightmail product is its relaiability, robustness and very low-cost administration costs.\\\"');\\n INSERT INTO grafit_article (id, title, text) VALUES (6, 'Critical Flaw Reported In phpMyAdmin Lets Attackers Damage Databases', 'A critical security vulnerability has been reported in phpMyAdmin—one of the most popular applications for managing the MySQL database—which could allow remote attackers to perform dangerous database operations just by tricking administrators into clicking a link.\\n\\n Discovered by an Indian security researcher, Ashutosh Barot, the vulnerability is a cross-site request forgery (CSRF) attack and affects phpMyAdmin versions 4.7.x (prior to 4.7.7).\\n\\n Cross-site request forgery vulnerability, also known as XSRF, is an attack wherein an attacker tricks an authenticated user into executing an unwanted action.\\n\\n According to an advisory released by phpMyAdmin, \\\"by deceiving a user to click on a crafted URL, it is possible to perform harmful database operations such as deleting records, dropping/truncating tables, etc.\\\"\\n\\n phpMyAdmin is a free and open source administration tool for MySQL and MariaDB and is widely used to manage the database for websites created with WordPress, Joomla, and many other content management platforms.\\n\\n Moreover, a lot of hosting providers use phpMyAdmin to offer their customers a convenient way to organize their databases.\\n Barot has also released a video, as shown above, demonstrating how a remote attacker can make database admins unknowingly delete (DROP) an entire table from the database just by tricking them into clicking a specially crafted link.\\n\\n \\\"A feature of phpMyAdmin was using a GET request and after that POST request for Database operations such as DROP TABLE table_name; GET requests must be protected against CSRF attacks. In this case, POST requests were used which were sent through URL (for bookmarking purpose may be); it was possible for an attacker to trick a database admin into clicking a button and perform a drop table database query of the attacker’s choice.\\\" Barot explains in a blog post.\\n\\n However, performing this attack is not simple as it may sound. To prepare a CSRF attack URL, the attacker should be aware of the name of targeted database and table.\\n\\n \\\"If a user executes a query on the database by clicking insert, DROP, etc. buttons, the URL will contain database name and table name,\\\" Barot says. \\\"This vulnerability can result in the disclosure of sensitive information as the URL is stored at various places such as browser history, SIEM logs, Firewall Logs, ISP Logs, etc.\\\"\\n\\n Barot reported the vulnerability to phpMyAdmin developers, who confirmed his finding and released phpMyAdmin 4.7.7 to address this issue. So administrators are highly recommended to update their installations as soon as possible.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (25, 'Death By Database', 'The following is a true story, but with names changed.\\n\\n When I work with clients to build software, I take the usual steps of understanding their needs, gathering requirements, learning about their customers, and so on. At this point I have a model on paper of roughly what the software is intended to do, so they get surprised when I immediately turn to database design.\\n\\n \\\"Who care about database design? What about mockups? What about workflows?\\\"\\n\\n Let me tell you about \\\"Bob''s Luxury Goods.\\\" I worked for this company many years ago and they had a retail store selling ... you guessed it ... luxury goods. They''d ask all customers for a billing address and if they had a different delivery address. At the database level, they had a \\\"one-to-many\\\" relationship between customers and addresses.\\n\\n That was their first problem. A customer''s partner might come into Bob''s and order something and if the address was entered correctly it would be flagged as \\\"in use\\\" and we had to use a different address or deliberately enter a typo. Fortunately, addresses were case-sensitive, so many people had UPPER-CASE ADDRESSES.\\n\\n We should have had a many-to-many relationship between customers and addresses so we could handle the case where more than one person would share the same address, but we didn''t. Further, I was never allocated the time to fix the database because it was \\\"cheaper\\\" to remove the restriction on \\\"flagged\\\" addresses and allow a duplicate address to be used.\\n\\n Naturally, being a luxury goods company, we had many repeat customers and sometimes they would move and if we didn''t find the duplicate address, or the address with the \\\"typo\\\", we might update the address for one partner, but not the other. That was a headache, but it didn''t happen frequently enough for management to worry about it.\\n\\n That''s when the marketing department had a brilliant, inexpensive idea. You see, we periodically did mass mailings of special events to our customers. Since we had the software to do mass mailings, why not import a mailing list of all addresses in high net worth areas and mail everyone about upcoming special events? So the company went ahead and bought a database with all of these addresses, but forgot to mention to me that I was supposed to implement this.\\n\\n Except that every address record had the customer id embedded in it, so we couldn''t enter an address without a customer.\\n\\n \\\"Curtis,\\\" they said, \\\"just enter a dummy customer called ''Occupant'' and attach all addresses to that.\\\"\\n\\n Except you couldn''t enter a customer without an order.\\n\\n Except you couldn''t enter an order without at least one item on it.\\n\\n Except you couldn''t enter an item unless it was listed in inventory.\\n\\n Except that reserved the \\\"inventory\\\" item and made it unavailable.\\n\\n Except, except, except ...\\n\\n It came down to trying to create a fake customer, with a fake order, with a fake item, with a fake item category, with a \\\"paid\\\" invoice, with exceptions sprinkled throughout the codebase to handle all of these special cases and probably more that I no longer remember.\\n\\n Then, and only then, could I write the code to provide \\\"generic\\\" mass mailings. Management decided it was easier to hire an outside company to handle the mailing list for them.\\n\\n If they had simply had a proper database design up front, they could have reused their existing system with little trouble.\\n\\n That''s what bad database design costs you and why I usually start with that before writing my software.\\n\\n Note: if you''re not familiar with database design, here''s a talk I give where I make it fairly simple to understand. I mostly avoid big words.');\\n INSERT INTO grafit_article (id, title, text) VALUES (33, 'GitHub Actions: built by you, run by us', 'Yesterday at GitHub Universe, we announced GitHub Actions, a new way to automate and customize your workflows. Configuring the apps and services that make up your development cycle takes significant time and effort. GitHub Actions applies open source principles to workflow automation, weaving together the tools you use from idea to production into one complete workflow. You can also create, share, and discover any actions your projects require, just as you would create, share, and discover code on GitHub.\\n\\n Learn more about actions\\n\\n As we prepared for Universe, we shared GitHub Actions with a group of customers, integrators, and open source maintainers to see what they could do. In just a few short weeks, talented teams and individuals alike have created hundreds of GitHub Actions. During today’s Universe keynote, we heard directly from developers, and we’re excited to share their work with you');\\n INSERT INTO grafit_article (id, title, text) VALUES (34, 'Git Submodule Vulnerability Announced ', '\\n\\n The Git project has disclosed CVE-2018-17456, a vulnerability in Git that can cause arbitrary code to be executed when a user clones a malicious repository. Git v2.19.1 has been released with a fix, along with backports in v2.14.5, v2.15.3, v2.16.5, v2.17.2, and v2.18.1. We encourage all users to update their clients to protect themselves.\\n\\n Until you’ve updated, you can protect yourself by avoiding submodules from untrusted repositories. This includes commands such as git clone --recurse-submodules and git submodule update.\\n Affected products\\n GitHub Desktop\\n\\n GitHub Desktop versions 1.4.1 and older included an embedded version of Git that was affected by this vulnerability. We encourage all GitHub Desktop users to update to the newest version (1.4.2 and 1.4.3-beta0) available today in the Desktop app.\\n Atom\\n\\n Atom included the same embedded Git and was also affected. Releases 1.31.2 and 1.32.0-beta3 include the patch.\\n\\n Ensure you’re on the latest Atom release by completing any of the following:\\n\\n Windows: From the toolbar, click Help -> Check for Updates\\n MacOS: From the menu bar, click Atom -> Check for Update\\n Linux: Update manually by downloading the latest release from atom.io\\n\\n Git on the command line and other clients\\n\\n In order to be protected from the vulnerability, you must update your command-line version of Git, and any other application that may include an embedded version of Git, as they are independent of each other.\\n Additional notes\\n\\n Neither GitHub.com nor GitHub Enterprise are directly affected by the vulnerability. However, as with previously discovered vulnerabilities, GitHub.com will detect malicious repositories, and will reject pushes or API requests attempting to create them. Versions of GitHub Enterprise with this detection will ship on October 9.\\n Details of the vulnerability\\n\\n This vulnerability is very similar to CVE-2017-1000117, as both are option-injection attacks related to submodules. In the earlier attack, a malicious repository would ship a .gitmodules file pointing one of its submodules to a remote repository with an SSH host starting with a dash (-). The ssh program—spawned by Git—would then interpret that as an option. This attack works in a similar way, except that the option-injection is against the child git clone itself.\\n\\n The problem was reported on September 23 by @joernchen, both to Git’s private security list, as well as to GitHub’s Bug Bounty program. Developers at GitHub worked with the Git community to develop a fix.\\n\\n The basic fix was clear from the report. However, due to to the similarity to CVE-2017-1000117, we also audited all of the .gitmodules values and implemented stricter checks as appropriate. These checks should prevent a similar vulnerability in another code path. We also implemented detection of potentially malicious submodules as part of Git’s object quality checks (which was made much easier by the infrastructure added during the last submodule-related vulnerability).\\n\\n The coordinated disclosure date of October 5 was selected by Git developers to allow packagers to prepare for the release. This also provided hosting sites (with custom implementations) ample time to detect and block the attack before it became public. Members of the Git community checked the JGit and libgit2 implementations. Those are not affected by the vulnerability because they clone submodules via function calls rather than separate commands.\\n\\n We were also able to use the time to scan all repositories on GitHub for evidence of the attack being used in the wild. We’re happy to report that no instances were found (and now, with our detection, none can be added).\\n\\n Please update your copy of Git soon, and happy cloning!\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (21, 'Hackers Targeting Servers Running Database Services for Mining Cryptocurrency', 'Security researchers have discovered multiple attack campaigns conducted by an established Chinese criminal group that operates worldwide, targeting database servers for mining cryptocurrencies, exfiltrating sensitive data and building a DDoS botnet.\\n\\n The researchers from security firm GuardiCore Labs have analyzed thousands of attacks launched in recent months and identified at least three attack variants—Hex, Hanako, and Taylor—targeting different MS SQL and MySQL servers for both Windows and Linux.\\n\\n The goals of all the three variants are different—Hex installs cryptocurrency miners and remote access trojans (RATs) on infected machines, Taylor installs a keylogger and a backdoor, and Hanako uses infected devices to build a DDoS botnet.\\n\\n So far, researchers have recorded hundreds of Hex and Hanako attacks and tens of thousands of Taylor attacks each month and found that most compromised machines are based in China, and some in Thailand, the United States, Japan and others.\\n\\n To gain unauthorized access to the targeted database servers, the attackers use brute force attacks and then run a series of predefined SQL commands to gain persistent access and evade audit logs.\\n\\n What''s interesting? To launch the attacks against database servers and serve malicious files, attackers use a network of already compromised systems, making their attack infrastructure modular and preventing takedown of their malicious activities.');\\n INSERT INTO grafit_article (id, title, text) VALUES (22, 'RIP Open Source MySQL', ' This is an excellent opportunity for the Postgres community to step up an promote Postgres.\\n\\n \\n \\n rbanffy on Aug 18, 2012 [-]\\n\\n I think this would be a mistake.\\n\\n This is an excellent opportunity to demonstrate that anyone can fork the MySQL codebase and create other plug-in replacement databases with it, such as MariaDB and Drizzle.\\n\\n All that is lost is the MySQL name and brand.\\n\\n PostgreSQL users and developers must seize the opportunity to show businesses that free software cannot be killed, not even by mighty Oracle. They and, most notably, Microsoft, have been trying to kill it for more than a decade now.\\n\\n Because the anti-free-software FUD machine (fed in part by Oracle itself) is already having a wonderful time with this.\\n\\n \\n \\n Udo on Aug 18, 2012 [-]\\n\\n I wish I could mod this up a hundred times. PostgreSQL people themselves have been playing into the hands of corporate FUDders with their incessant and inappropriate peddling. MySQL is not your enemy, MS SQL Server is. Oracle''s software empire as a whole certainly is your enemy. Show some solidarity with a fellow open source project!\\n\\n MySQL and PostgreSQL represent two very different implementation philosophies, and being able to choose between them according to taste and merits is a good thing.\\n\\n Most of us have suspected that the MySQL project itself was going to die as it was acquired by Oracle, in the same way Open Office died when it was acquired by Oracle. This is a company where good software goes to expire, either due to a deliberate intention or gross incompetence I can''t say but I suspect it''s a mixture of both. However sad that may be for the MySQL (or OpenOffice) brand name, the code itself lives on and continues to evolve within a rich open source ecosystem.\\n\\n Hence, sensational and petulant \\\"RIP $PRODUCTNAME\\\" articles are unnecessary. There is no threat to existing projects based on MySQL or any other successful open source project for that matter. Not only will this stuff be free forever, it will also continue to grow and be developed on its own.\\n\\n The corporate assassination of open source projects will only work if we let it, it''s a purely psychological game. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (23, 'Free Text Sources', 'There are a few interesting things to talk about surrounding free and open textbooks. Quality is one. Usability is another. Why to write one (and/or, why not) is certainly critical. But where can you find these disruptive, open texts?\\n\\n Not all faculty know there are free and open texts they can use; finding free and/or open textbooks (or even knowing to look) can sometimes be a trick. I knew about one or two sources, and did a little bit more digging. Admittedly, many of the sources of free texts linked below have a technical bent. On one hand, this might be because math, computing, and the sciences are familiar with working openly and giving things away. On the other, it might be because I am a member of the computing faculty, and therefore am most familiar with resources in that space.');\\n INSERT INTO grafit_article (id, title, text) VALUES (24, 'Apache Software Foundation Public Mail Archives', 'A collection of all publicly available mail archives from the Apache55 Software Foundation (ASF), taken on July 11, 2011. This collection contains all publicly available email archives from the ASF''s 80+ projects (http://mail-archives.apache.org/mod_mbox/), including mailing lists such as Apache HTTPD Server, Apache Tomcat, Apache Lucene and Solr, Apache Hadoop and many more. Generally speaking, most projects have at least three lists: user, dev and commits, but some have more, some have less. The user lists are where users of the software ask questions on usage, while the dev list usually contains discussions on the development of the project (code, releases, etc.) The commit lists usually consists of automated notifications sent by the various ASF version control tools, like Subversion or CVS, and contain information about changes made to the project''s source code.\\n\\n Both tarballs and per project sets are available in the snapshot. The tarballs are organized according to project name. Thus, a-d.tar.gz contains all ASF projects that begin with the letters a, b, c or d, such as abdera.apache.org. Files within the project are usually gzipped mbox files.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (26, 'PostgreSQL - Overview', 'PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development phase and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness.\\n\\n This tutorial will give you a quick start with PostgreSQL and make you comfortable with PostgreSQL programming.\\n What is PostgreSQL?\\n\\n PostgreSQL (pronounced as post-gress-Q-L) is an open source relational database management system (DBMS) developed by a worldwide team of volunteers. PostgreSQL is not controlled by any corporation or other private entity and the source code is available free of charge.\\n A Brief History of PostgreSQL\\n\\n PostgreSQL, originally called Postgres, was created at UCB by a computer science professor named Michael Stonebraker. Stonebraker started Postgres in 1986 as a follow-up project to its predecessor, Ingres, now owned by Computer Associates.\\n\\n 1977-1985 − A project called INGRES was developed.\\n\\n Proof-of-concept for relational databases\\n\\n Established the company Ingres in 1980\\n\\n Bought by Computer Associates in 1994\\n\\n 1986-1994 − POSTGRES\\n\\n Development of the concepts in INGRES with a focus on object orientation and the query language - Quel\\n\\n The code base of INGRES was not used as a basis for POSTGRES\\n\\n Commercialized as Illustra (bought by Informix, bought by IBM)\\n\\n 1994-1995 − Postgres95\\n\\n Support for SQL was added in 1994\\n\\n Released as Postgres95 in 1995\\n\\n Re-released as PostgreSQL 6.0 in 1996\\n\\n Establishment of the PostgreSQL Global Development Team\\n\\n Key Features of PostgreSQL\\n\\n PostgreSQL runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It supports text, images, sounds, and video, and includes programming interfaces for C / C++, Java, Perl, Python, Ruby, Tcl and Open Database Connectivity (ODBC).\\n\\n PostgreSQL supports a large part of the SQL standard and offers many modern features including the following −\\n\\n Complex SQL queries\\n SQL Sub-selects\\n Foreign keys\\n Trigger\\n Views\\n Transactions\\n Multiversion concurrency control (MVCC)\\n Streaming Replication (as of 9.0)\\n Hot Standby (as of 9.0)\\n\\n You can check official documentation of PostgreSQL to understand the above-mentioned features. PostgreSQL can be extended by the user in many ways. For example by adding new −\\n\\n Data types\\n Functions\\n Operators\\n Aggregate functions\\n Index methods\\n\\n Procedural Languages Support\\n\\n PostgreSQL supports four standard procedural languages, which allows the users to write their own code in any of the languages and it can be executed by PostgreSQL database server. These procedural languages are - PL/pgSQL, PL/Tcl, PL/Perl and PL/Python. Besides, other non-standard procedural languages like PL/PHP, PL/V8, PL/Ruby, PL/Java, etc., are also supported.');\\n INSERT INTO grafit_article (id, title, text) VALUES (27, 'Setup PostgreSQL on Windows with Docker', 'Over the weekend I finally got the chance to start reading A Curious Moon by Rob Conery which is a book on learning PostgreSQL by following the fictional Dee Yan as she is thrown into database administrator role at an aerospace startup.\\n\\n I have a lot of experience using Microsoft’s SQL Server, but up until now, I haven’t touched PostgreSQL. For personal projects SQL Server’s cost and be prohibitive and the release of Rob’s book added up to a good time to give PostgreSQL a try.\\n Install Directly or not?\\n\\n On the download section of the official Postgres site, there is an option to download an installer. This is the route I was going to at first, but in Rob’s book, he suggests using a VM for Postgres installation on Windows. This kicked off a lot of searching on my part and didn’t find a good definitive answer on why that is or isn’t the way to do.\\n\\n In the end, I decided to try and run the Postgres process using Docker instead installing directly on Windows or dealing with a full VM.\\n Installing Docker\\n\\n Head to this link and click the Get Docker link to download the installer. After the install is complete you will have to log out and back in. When I logged back in I got a message about Hyper-V not being enabled.\\n\\n After logging back in I then got the following message about hardware-assisted virtualization not being enabled.\\n\\n After tweaking my BIOS settings and logging back in I was greeted by the Docker welcome screen.\\n\\n Open a command prompt and run the following command.\\n\\n docker run hello-world\\n\\n You should output that starts with the following if your installation is working.\\n\\n Hello from Docker!\\n This message shows that your installation appears to be working correctly.\\n\\n What about Postgres?\\n\\n Getting up and going with a container running Postgres was pretty simple and could be done with the following command which will create a container and expose the port used by Postgres so it can be accessed from the host.\\n\\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d postgres\\n\\n The problem with this approach is if you ever need to rebuild the container for some reason, like a new version of Postgres is released, your data will be lost. Thankfully I found this blog post which shows how to use a secondary container for the data leaving the Postgres container able to be destroyed and recreated as needed. The following is the command I used to create my data container.\\n\\n docker create -v /var/lib/postgresql/data --name PostgresData alpine\\n\\n The above creates a container named PostgresData based on the Alpine image. It is important that the -v parameter matches the path that Postgres expects.\\n\\n Now that we have a container that will keep our data safe let’s create the actual Postgres container with the following command.\\n\\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d --volumes-from PostgresData postgres\\n\\n The only difference from the first example run command is the addition of –volumes-from PostgresData which tells the container to use the PostgresData container.\\n\\n If you run the docker ps -a command it will show you all your containers.\\n\\n As you can see in my example I have two containers only one of which is actually running. Make sure you don’t remove the data container just because it will never show as running.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (28, 'DIY: A PostgreSQL database server setup anyone can handle', 'When it comes to databases, I''m a fan of MySQL. The open source database can handle just about any load you want to throw at it, and it has lots of powerful tools that can be used to manage it.\\n\\n The other popular open source database is PostgreSQL, which is cross-platform and is used by numerous applications. Although PostgreSQL is often seen as being as powerful as MySQL, it doesn''t have nearly the number of available tools to make setup and management as easy as its competition. So I''ve written this handy PostgreSQL primer on how to get your database server up and running and ready to use. (Although PostgreSQL is cross-platform, I demonstrate the installation and setup on a Ubuntu 11.04 machine because it''s my platform of choice. The translation to other platforms should be simple.)\\n Step 1: Install PostgreSQL\\n\\n Here are the installation steps on Ubuntu (this installation will also work on any Debian-based distribution):\\n\\n Open a terminal window.\\n Issue the command sudo apt-get install postgresql.\\n Type the sudo password necessary to give you admin rights and hit Enter.\\n Allow apt to pick up any necessary dependencies.\\n\\n Once the installation is complete, it''s time to set this baby up.\\n Step 2: Change the default user password\\n\\n Caution: If you don''t follow this step, you will not be able to add databases and administer PostgreSQL, and the database will not be secure.\\n\\n Here''s how to change the password for the default user. The user in question is postgres, and the password is changed like so:\\n\\n Open a terminal window.\\n Issue the command sudo passwd postgres.\\n Type (and confirm) that password to be used for this user.\\n\\n The postgres user will be the only user on your system that can open the PostgreSQL prompt without defining a database, which means postgres is the only user who can administer PostgreSQL. To test this, change to the postgres user with the command su - postgres and then enter the command psql. You should now be at the Postgres prompt, which looks like:\\n\\n postgres=#\\n\\n All other users have to gain access to the prompt like so:\\n\\n psql DB_NAME\\n\\n where DB_NAME is the name of an existing database.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (31, 'The Marketing Behind MongoDB', ' 100% of my friends who have used Mongo/similar NoSQL have given up and had a nasty rewrite back to pgSQL.\\n\\n This seems to be the journey:\\n\\n 1. Lack of migrations is awesome! We can iterate so quickly for MVP\\n\\n 2. Get users\\n\\n 3. Add features, still enjoying the speed of iteration\\n\\n 4. Get more users\\n\\n 5. Start building reporting features for enterprise/customer support/product metrics (ie: when the real potential success starts)\\n\\n 6. Realise you desperately need joins, transactions and other SQL features\\n\\n 7. Pause product dev for 1-3+ months to migrate back to SQL, or do some weird parallel development process to move it piecemeal back.\\n\\n I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\\n\\n My thought is definitely yes.\\n\\n \\n \\n brandur on Aug 29, 2017 [-]\\n\\n > I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\\n\\n I''ve used Postgres and Mongo pretty extensively, and for any reasonably seasoned developer, the startup overhead of an SQL system is a myth. There may upfront cost to learning how an RDMS and SQL work in the first place, but once you''re familiar with them, they''ll be faster than Mongo on any new project.\\n\\n The schemaless concept of a document database seems to be the major selling factor in velocity of movement, but once you''ve got a good handle on a migration framework in the vein of ActiveRecord or other popular software, that''s negated completely. It also really doesn''t take long before schemaless starts to cause big problems for you in terms of data consistency -- it''s not just the big players that get bitten by this.\\n\\n The simplified query language is another one. SQL is a little bit obtuse, but it''s not that bad once you have a handle on it, and a lot of people are familiar with it. Once you add in an ORM layer, the lazy-style access of a framework like Sequel or SQLAlchemy makes the developer experience quite a bit better than any Mongo APIs that I''ve seen. Also, after you get beyond trivial usage, SQL''s flexibility so wildly outstrips Mongo''s query documents that it''s not even worth talking about.\\n\\n Postgres on the other hand ships with a great management CLI, a very powerful REPL (psql), and features like data types/constraints/transactions that guarantee you correctness with zero effort on your part. I can only speak for myself, but I''d take Postgres to the hackathon any day of the week.\\n\\n \\n \\n martinald on Aug 29, 2017 [-]\\n\\n I totally agree with you, and started writing something about how understanding a good ORM takes nearly all the headache away.\\n\\n I think the thing people do find slow is a lot of ''documents within documents'' in SQL. It turns out this is usually a bad development pattern long term but it is super fast being able to just add docs inside docs with no configuration. It feels very slow writing foreign keys, navigation props and schemas for this in SQL vs JSON, where you can just dump your object in and you''re done.\\n\\n Basically; I think with noSQL you get some very short term gain for a lot of long term pain, and you''re right, ORMs and other tooling solves this mostly.\\n\\n I myself fell for this trap, and while it was a nightmare it actually matured me more as a professional more than anything I''ve ever done recently. Regardless of crazy hype, I don''t think I''ll ever fall for a solution so easily without evaluating it properly.\\n\\n I think I assumed the \\\"crowd\\\" had done the tech due diligence on this stuff and it definitely wasn''t the case. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (32, 'Countless NoSQL databases competed to be the database of choice', 'n 2013, 10gen — the company behind MongoDB — moved into a large 30,000 square foot office in Midtown Manhattan.\\n\\n The transfer into the former New York Times building capped off a tremendous period of growth: the database boasted 4 million downloads, the MongoDB User Groups already attracted 15,000 members, and ~10,000 people had attended a global event in 2012. Their offices were truly global from London to Sydney to Dublin and Barcelona — and a requisite west coast headquarters in Palo Alto.\\n\\n Despite the traction, many startups using MongoDB faced their own challenges. One part of MongoDB’s success among startups was because some didn''t critically assess 10gen’s marketing message.\\n\\n As engineers, we often discuss technical attacks (e.g., DDoS, Sybil attacks, security vulnerabilities), but need to spend time debating how to protect ourselves from marketing “attacks”.1 Today, developer marketing is subtle — third party blog posts, content marketing disguised as engineering lessons, biased talks, and sponsored hackathons — not clearly marked content from vendors. As such, startup engineering decisions can hinge on sources that are not impartial.\\n\\n A large amount of \\\"engineering\\\" content — even when written by engineers — is actually marketing, rather than thoughtful content whose aim is to help you make the best decision.\\n\\n Previously, we looked at the hype around NoSQL and common engineering mistakes to see how MongoDB became so successful. Now, let''s take a look into 10gen''s marketing strategy — as told by their employees.2\\n\\n 10gen’s marketing strategy is an increasingly common playbook and understanding it is useful for future developer tool decisions.');\\n INSERT INTO grafit_article (id, title, text) VALUES (30, 'Comment Arrango', ' ArangoDB always makes for exciting benchmark posts.\\n\\n I could see myself there in a bowler hat with a fistful of racing chits screaming “go, Postgres, go.”\\n\\n I’d love to see a competition were the developers of each database got to use the same hardware and data then tune the hell out of their configs, queries, and indices.\\n\\n Red Bull could sponsor it. I’d buy a T-shirt.\\n\\n \\n \\n kbenson 8 months ago [-]\\n\\n That doesn''t sound that hard to start. Something like RealWorld[1] and the Web Framework Benchmarks[2] combined but for DB workloads. Have one dataset that includes data amenable to OLAP and OLTP, but have separate tests each consisting of OLAP queries, OLTP queries, and combined queries. Choose a low-end, mid-range and high-end set of AWS or GCE instances/configs to normalize against. Let people submit pull requests with new technologies or configs.\\n\\n You''d want to get some funding to run the tests (or maybe solicit Google or Amazon to see if you could get the instance time donated once a month or something.\\n\\n If you started small, with maybe a portion of these features, and then scaled up over time, you might actually get to the point where you had tests that emulated a power failure, or master/slave and dual master scenarios and how they handle certain common network errors (split-brain). That would be an amazing resource.\\n\\n Edit: It occurs to me I probably should have read more of the article, since this is sort of what they are doing already...\\n\\n 1: https://github.com/gothinkster/realworld\\n\\n 2: https://www.techempower.com/benchmarks/\\n\\n \\n \\n etxm 8 months ago [-]\\n\\n Yeah after I posted it I started thinking about what it would take and what that would actually look like... and how you’d cheat :)\\n\\n It would probably require a few different categories with some sort of output assertion to validate the query performed right and a means of tracking CPU, usage ram usage, and execution time.\\n\\n It would be cool to see things like disaster recovery and chaos proofing as well. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (35, 'Applying machine intelligence to GitHub security alerts ', 'Last year, we released security alerts that track security vulnerabilities in Ruby and JavaScript packages. Since then, we’ve identified more than four million of these vulnerabilities and added support for Python. In our launch post, we mentioned that all vulnerabilities with CVE IDs are included in security alerts, but sometimes there are vulnerabilities that are not disclosed in the National Vulnerability Database. Fortunately, our collection of security alerts can be supplemented with vulnerabilities detected from activity within our developer community.\\n Leveraging the community\\n\\n There are many places a project can publicize security fixes within a new version: the CVE feed, various mailing lists and open source groups, or even within its release notes or changelog. Regardless of how projects share this information, some developers within the GitHub community will see the advisory and immediately bump their required versions of the dependency to a known safe version. If detected, we can use the information in these commits to generate security alerts for vulnerabilities which may not have been published in the CVE feed.\\n\\n On an average day, the dependency graph can track around 10,000 commits to dependency files for any of our supported languages. We can’t manually process this many commits. Instead, we depend on machine intelligence to sift through them and extract those that might be related to a security release.\\n\\n For this purpose, we created a machine learning model that scans text associated with public commits (the commit message and linked issues or pull requests) to filter out those related to possible security upgrades. With this smaller batch of commits, the model uses the diff to understand how required version ranges have changed. Then it aggregates across a specific timeframe to get a holistic view of all dependencies that a security release might affect. Finally, the model outputs a list of packages and version ranges it thinks require an alert and currently aren’t covered by any known CVE in our system.\\n Always quality focused\\n\\n No machine learning model is perfect. While machine intelligence can sift through thousands of commits in an instant, this anomaly-detection algorithm will still generate false positives for packages where no security patch was released. Security alert quality is a focus for us, so we review all model output before the community receives an alert.\\n Learn more');\\n INSERT INTO grafit_article (id, title, text) VALUES (29, 'Performance Benchmark 2018', 'I''ve stopped reading database benchmarks, because they are extremely vague. Instead I spend my time optimizing my current solution/stack. For example Postgresql has hundreds of knobs that you can adjust for almost every scenario you can imagine. Sometimes you have a special query and increase the work_mem just for that session. Other cases you adjust the cost settings for another query/session. You can analyze your indexes and index types. And sometimes you need to rewrite parts of a big query.\\n\\n Learning all this takes time, you are much better off learning more about your chosen technology stack than switching to another technology stack.\\n\\n Though in a few rare races, you need a different technology to solve your business problem. In most cases they complement your existing solution, like Elasticsearch/Solr for full-text search or Clickhouse for OLAP workloads.\\n\\n \\n \\n maxxxxx 8 months ago [-]\\n\\n Agreed. Switching to another system is expensive and the benefit is pretty questionable.\\n\\n \\n \\n emsy 8 months ago [-]\\n\\n Unless you hit a very specific use-case/bottleneck, which I only ever witnessed once.\\n\\n \\n \\n TremendousJudge 8 months ago [-]\\n\\n expand, please?\\n\\n \\n \\n maxxxxx 8 months ago [-]\\n\\n I imagine something very specific like having a lot of inserts into a table and that being your main use case. Depending on your data some databases may be better than others and that should be easy to measure.\\n\\n In most real-world cases the requirements however are not very clear and often conflicting so it''s much harder to get data that shows the performance of one system over the other.\\n\\n \\n \\n gopalv 8 months ago [-]\\n\\n > Depending on your data some databases may be better than others and that should be easy to measure.\\n\\n And the performance difference could be an accidental feature of the design and completely unintentional.\\n\\n Postgres for instance has a native data engine, so it can store the exact row-ids for a row into an index, but this means that every update to the row needs all indexes to be updated.\\n\\n Mysql has many data engines (InnoDB and MyISAM to start with), to the row-id is somewhat opaque, so the index stores the primary key which can be pushed to the data engine scans and then have it lookup a row-id internally. This needs an index to be touched for the columns you modify explicitly or if the primary key is updated (which is a usual no-no due to UNIQUE lookup costs).\\n\\n When you have a single wide table with a huge number of indexes, where you update a lot of dimensions frequently, the performance difference between these two solutions is architectural.\\n\\n And if you lookup along an index with few updates, but long running open txns, that is also materially different - one lookup versus two.\\n\\n Though how it came about isn''t really intentional. ');\\n \\\"\\\"\\\"),\\n ]\\n\",\n \"import django.contrib.auth.models\\nimport django.contrib.auth.validators\\nfrom django.db import migrations, models\\nimport django.utils.timezone\\nimport uuid\\n\\n\\nclass Migration(migrations.Migration):\\n dependencies = [('grafit', '0002_article')]\\n operations = [migrations.RunSQL(\\n \\\"\\\"\\\"\\n INSERT INTO grafit_article (id, title, text) VALUES (2, 'MongoDB', 'MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemata. MongoDB is developed by MongoDB Inc., and is published under a combination of the Server Side Public License and the Apache License.\\n 10gen software company began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[6]\\n\\n On October 20, 2017, MongoDB became a publicly-traded company, listed on NASDAQ as MDB with an IPO price of $24 per share.[7] Ad hoc queries\\n\\n MongoDB supports field, range query, and regular expression searches.[8] Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size.\\n Indexing\\n\\n Fields in a MongoDB document can be indexed with primary and secondary indices.\\n Replication\\n\\n MongoDB provides high availability with replica sets.[9] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.\\n Load balancing[10]\\n\\n MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.\\n\\n MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (3, 'NoSQL', 'A NoSQL (originally referring to \\\"non SQL\\\" or \\\"non relational\\\")[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the \\\"NoSQL\\\" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called \\\"Not only SQL\\\" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.[7][8]\\n\\n Motivations for this approach include: simplicity of design, simpler \\\"horizontal\\\" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as \\\"more flexible\\\" than relational database tables.[9]\\n\\n Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)\\n\\n Instead, most NoSQL databases offer a concept of \\\"eventual consistency\\\" in which database changes are propagated to all nodes \\\"eventually\\\" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases \\\"do not allow referential integrity constraints to span databases.\\\"[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (4, 'SQL', 'SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd[15] in the early 1970s.[16] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM''s original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s.[16]\\n\\n Chamberlin and Boyce''s first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973, they began work on SEQUEL.[15] The acronym SEQUEL was later changed to SQL because \\\"SEQUEL\\\" was a trademark of the UK-based Hawker Siddeley aircraft company.[17]\\n\\n In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce, and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. By 1986, ANSI and ISO standard groups officially adopted the standard \\\"Database Language SQL\\\" language definition. New versions of the standard were published in 1989, 1992, 1996, 1999, 2003, 2006, 2008, 2011,[15] and most recently, 2016. After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.[18] ');\\n INSERT INTO grafit_article (id, title, text) VALUES (5, 'MySQL', 'Built on MySQL Enterprise Edition and powered by the Oracle Cloud, Oracle MySQL Cloud Service provides a simple, automated, integrated and enterprise ready MySQL cloud service, enabling organizations to increase business agility and reduce costs. \\\"Relying on the MySQL engine as the low-level storage layer has allowed us to very quickly build a robust system.\\\"\\n \\n\\n \\\"We have successfully implemented MySQL Cluster Carrier Grade Edition for our highly mission critical XDMS application which will enable the next generation of converged services.\\\"\\n \\n\\n \\\"We found that MySQL was the best database in terms of the price-point and functionality it offers up. The benefits that MySQL brings to our Brightmail product is its relaiability, robustness and very low-cost administration costs.\\\"');\\n INSERT INTO grafit_article (id, title, text) VALUES (6, 'Critical Flaw Reported In phpMyAdmin Lets Attackers Damage Databases', 'A critical security vulnerability has been reported in phpMyAdmin—one of the most popular applications for managing the MySQL database—which could allow remote attackers to perform dangerous database operations just by tricking administrators into clicking a link.\\n\\n Discovered by an Indian security researcher, Ashutosh Barot, the vulnerability is a cross-site request forgery (CSRF) attack and affects phpMyAdmin versions 4.7.x (prior to 4.7.7).\\n\\n Cross-site request forgery vulnerability, also known as XSRF, is an attack wherein an attacker tricks an authenticated user into executing an unwanted action.\\n\\n According to an advisory released by phpMyAdmin, \\\"by deceiving a user to click on a crafted URL, it is possible to perform harmful database operations such as deleting records, dropping/truncating tables, etc.\\\"\\n\\n phpMyAdmin is a free and open source administration tool for MySQL and MariaDB and is widely used to manage the database for websites created with WordPress, Joomla, and many other content management platforms.\\n\\n Moreover, a lot of hosting providers use phpMyAdmin to offer their customers a convenient way to organize their databases.\\n Barot has also released a video, as shown above, demonstrating how a remote attacker can make database admins unknowingly delete (DROP) an entire table from the database just by tricking them into clicking a specially crafted link.\\n\\n \\\"A feature of phpMyAdmin was using a GET request and after that POST request for Database operations such as DROP TABLE table_name; GET requests must be protected against CSRF attacks. In this case, POST requests were used which were sent through URL (for bookmarking purpose may be); it was possible for an attacker to trick a database admin into clicking a button and perform a drop table database query of the attacker’s choice.\\\" Barot explains in a blog post.\\n\\n However, performing this attack is not simple as it may sound. To prepare a CSRF attack URL, the attacker should be aware of the name of targeted database and table.\\n\\n \\\"If a user executes a query on the database by clicking insert, DROP, etc. buttons, the URL will contain database name and table name,\\\" Barot says. \\\"This vulnerability can result in the disclosure of sensitive information as the URL is stored at various places such as browser history, SIEM logs, Firewall Logs, ISP Logs, etc.\\\"\\n\\n Barot reported the vulnerability to phpMyAdmin developers, who confirmed his finding and released phpMyAdmin 4.7.7 to address this issue. So administrators are highly recommended to update their installations as soon as possible.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (25, 'Death By Database', 'The following is a true story, but with names changed.\\n\\n When I work with clients to build software, I take the usual steps of understanding their needs, gathering requirements, learning about their customers, and so on. At this point I have a model on paper of roughly what the software is intended to do, so they get surprised when I immediately turn to database design.\\n\\n \\\"Who care about database design? What about mockups? What about workflows?\\\"\\n\\n Let me tell you about \\\"Bob''s Luxury Goods.\\\" I worked for this company many years ago and they had a retail store selling ... you guessed it ... luxury goods. They''d ask all customers for a billing address and if they had a different delivery address. At the database level, they had a \\\"one-to-many\\\" relationship between customers and addresses.\\n\\n That was their first problem. A customer''s partner might come into Bob''s and order something and if the address was entered correctly it would be flagged as \\\"in use\\\" and we had to use a different address or deliberately enter a typo. Fortunately, addresses were case-sensitive, so many people had UPPER-CASE ADDRESSES.\\n\\n We should have had a many-to-many relationship between customers and addresses so we could handle the case where more than one person would share the same address, but we didn''t. Further, I was never allocated the time to fix the database because it was \\\"cheaper\\\" to remove the restriction on \\\"flagged\\\" addresses and allow a duplicate address to be used.\\n\\n Naturally, being a luxury goods company, we had many repeat customers and sometimes they would move and if we didn''t find the duplicate address, or the address with the \\\"typo\\\", we might update the address for one partner, but not the other. That was a headache, but it didn''t happen frequently enough for management to worry about it.\\n\\n That''s when the marketing department had a brilliant, inexpensive idea. You see, we periodically did mass mailings of special events to our customers. Since we had the software to do mass mailings, why not import a mailing list of all addresses in high net worth areas and mail everyone about upcoming special events? So the company went ahead and bought a database with all of these addresses, but forgot to mention to me that I was supposed to implement this.\\n\\n Except that every address record had the customer id embedded in it, so we couldn''t enter an address without a customer.\\n\\n \\\"Curtis,\\\" they said, \\\"just enter a dummy customer called ''Occupant'' and attach all addresses to that.\\\"\\n\\n Except you couldn''t enter a customer without an order.\\n\\n Except you couldn''t enter an order without at least one item on it.\\n\\n Except you couldn''t enter an item unless it was listed in inventory.\\n\\n Except that reserved the \\\"inventory\\\" item and made it unavailable.\\n\\n Except, except, except ...\\n\\n It came down to trying to create a fake customer, with a fake order, with a fake item, with a fake item category, with a \\\"paid\\\" invoice, with exceptions sprinkled throughout the codebase to handle all of these special cases and probably more that I no longer remember.\\n\\n Then, and only then, could I write the code to provide \\\"generic\\\" mass mailings. Management decided it was easier to hire an outside company to handle the mailing list for them.\\n\\n If they had simply had a proper database design up front, they could have reused their existing system with little trouble.\\n\\n That''s what bad database design costs you and why I usually start with that before writing my software.\\n\\n Note: if you''re not familiar with database design, here''s a talk I give where I make it fairly simple to understand. I mostly avoid big words.');\\n INSERT INTO grafit_article (id, title, text) VALUES (33, 'GitHub Actions: built by you, run by us', 'Yesterday at GitHub Universe, we announced GitHub Actions, a new way to automate and customize your workflows. Configuring the apps and services that make up your development cycle takes significant time and effort. GitHub Actions applies open source principles to workflow automation, weaving together the tools you use from idea to production into one complete workflow. You can also create, share, and discover any actions your projects require, just as you would create, share, and discover code on GitHub.\\n\\n Learn more about actions\\n\\n As we prepared for Universe, we shared GitHub Actions with a group of customers, integrators, and open source maintainers to see what they could do. In just a few short weeks, talented teams and individuals alike have created hundreds of GitHub Actions. During today’s Universe keynote, we heard directly from developers, and we’re excited to share their work with you');\\n INSERT INTO grafit_article (id, title, text) VALUES (34, 'Git Submodule Vulnerability Announced ', '\\n\\n The Git project has disclosed CVE-2018-17456, a vulnerability in Git that can cause arbitrary code to be executed when a user clones a malicious repository. Git v2.19.1 has been released with a fix, along with backports in v2.14.5, v2.15.3, v2.16.5, v2.17.2, and v2.18.1. We encourage all users to update their clients to protect themselves.\\n\\n Until you’ve updated, you can protect yourself by avoiding submodules from untrusted repositories. This includes commands such as git clone --recurse-submodules and git submodule update.\\n Affected products\\n GitHub Desktop\\n\\n GitHub Desktop versions 1.4.1 and older included an embedded version of Git that was affected by this vulnerability. We encourage all GitHub Desktop users to update to the newest version (1.4.2 and 1.4.3-beta0) available today in the Desktop app.\\n Atom\\n\\n Atom included the same embedded Git and was also affected. Releases 1.31.2 and 1.32.0-beta3 include the patch.\\n\\n Ensure you’re on the latest Atom release by completing any of the following:\\n\\n Windows: From the toolbar, click Help -> Check for Updates\\n MacOS: From the menu bar, click Atom -> Check for Update\\n Linux: Update manually by downloading the latest release from atom.io\\n\\n Git on the command line and other clients\\n\\n In order to be protected from the vulnerability, you must update your command-line version of Git, and any other application that may include an embedded version of Git, as they are independent of each other.\\n Additional notes\\n\\n Neither GitHub.com nor GitHub Enterprise are directly affected by the vulnerability. However, as with previously discovered vulnerabilities, GitHub.com will detect malicious repositories, and will reject pushes or API requests attempting to create them. Versions of GitHub Enterprise with this detection will ship on October 9.\\n Details of the vulnerability\\n\\n This vulnerability is very similar to CVE-2017-1000117, as both are option-injection attacks related to submodules. In the earlier attack, a malicious repository would ship a .gitmodules file pointing one of its submodules to a remote repository with an SSH host starting with a dash (-). The ssh program—spawned by Git—would then interpret that as an option. This attack works in a similar way, except that the option-injection is against the child git clone itself.\\n\\n The problem was reported on September 23 by @joernchen, both to Git’s private security list, as well as to GitHub’s Bug Bounty program. Developers at GitHub worked with the Git community to develop a fix.\\n\\n The basic fix was clear from the report. However, due to to the similarity to CVE-2017-1000117, we also audited all of the .gitmodules values and implemented stricter checks as appropriate. These checks should prevent a similar vulnerability in another code path. We also implemented detection of potentially malicious submodules as part of Git’s object quality checks (which was made much easier by the infrastructure added during the last submodule-related vulnerability).\\n\\n The coordinated disclosure date of October 5 was selected by Git developers to allow packagers to prepare for the release. This also provided hosting sites (with custom implementations) ample time to detect and block the attack before it became public. Members of the Git community checked the JGit and libgit2 implementations. Those are not affected by the vulnerability because they clone submodules via function calls rather than separate commands.\\n\\n We were also able to use the time to scan all repositories on GitHub for evidence of the attack being used in the wild. We’re happy to report that no instances were found (and now, with our detection, none can be added).\\n\\n Please update your copy of Git soon, and happy cloning!\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (21, 'Hackers Targeting Servers Running Database Services for Mining Cryptocurrency', 'Security researchers have discovered multiple attack campaigns conducted by an established Chinese criminal group that operates worldwide, targeting database servers for mining cryptocurrencies, exfiltrating sensitive data and building a DDoS botnet.\\n\\n The researchers from security firm GuardiCore Labs have analyzed thousands of attacks launched in recent months and identified at least three attack variants—Hex, Hanako, and Taylor—targeting different MS SQL and MySQL servers for both Windows and Linux.\\n\\n The goals of all the three variants are different—Hex installs cryptocurrency miners and remote access trojans (RATs) on infected machines, Taylor installs a keylogger and a backdoor, and Hanako uses infected devices to build a DDoS botnet.\\n\\n So far, researchers have recorded hundreds of Hex and Hanako attacks and tens of thousands of Taylor attacks each month and found that most compromised machines are based in China, and some in Thailand, the United States, Japan and others.\\n\\n To gain unauthorized access to the targeted database servers, the attackers use brute force attacks and then run a series of predefined SQL commands to gain persistent access and evade audit logs.\\n\\n What''s interesting? To launch the attacks against database servers and serve malicious files, attackers use a network of already compromised systems, making their attack infrastructure modular and preventing takedown of their malicious activities.');\\n INSERT INTO grafit_article (id, title, text) VALUES (22, 'RIP Open Source MySQL', ' This is an excellent opportunity for the Postgres community to step up an promote Postgres.\\n\\n \\n \\n rbanffy on Aug 18, 2012 [-]\\n\\n I think this would be a mistake.\\n\\n This is an excellent opportunity to demonstrate that anyone can fork the MySQL codebase and create other plug-in replacement databases with it, such as MariaDB and Drizzle.\\n\\n All that is lost is the MySQL name and brand.\\n\\n PostgreSQL users and developers must seize the opportunity to show businesses that free software cannot be killed, not even by mighty Oracle. They and, most notably, Microsoft, have been trying to kill it for more than a decade now.\\n\\n Because the anti-free-software FUD machine (fed in part by Oracle itself) is already having a wonderful time with this.\\n\\n \\n \\n Udo on Aug 18, 2012 [-]\\n\\n I wish I could mod this up a hundred times. PostgreSQL people themselves have been playing into the hands of corporate FUDders with their incessant and inappropriate peddling. MySQL is not your enemy, MS SQL Server is. Oracle''s software empire as a whole certainly is your enemy. Show some solidarity with a fellow open source project!\\n\\n MySQL and PostgreSQL represent two very different implementation philosophies, and being able to choose between them according to taste and merits is a good thing.\\n\\n Most of us have suspected that the MySQL project itself was going to die as it was acquired by Oracle, in the same way Open Office died when it was acquired by Oracle. This is a company where good software goes to expire, either due to a deliberate intention or gross incompetence I can''t say but I suspect it''s a mixture of both. However sad that may be for the MySQL (or OpenOffice) brand name, the code itself lives on and continues to evolve within a rich open source ecosystem.\\n\\n Hence, sensational and petulant \\\"RIP $PRODUCTNAME\\\" articles are unnecessary. There is no threat to existing projects based on MySQL or any other successful open source project for that matter. Not only will this stuff be free forever, it will also continue to grow and be developed on its own.\\n\\n The corporate assassination of open source projects will only work if we let it, it''s a purely psychological game. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (23, 'Free Text Sources', 'There are a few interesting things to talk about surrounding free and open textbooks. Quality is one. Usability is another. Why to write one (and/or, why not) is certainly critical. But where can you find these disruptive, open texts?\\n\\n Not all faculty know there are free and open texts they can use; finding free and/or open textbooks (or even knowing to look) can sometimes be a trick. I knew about one or two sources, and did a little bit more digging. Admittedly, many of the sources of free texts linked below have a technical bent. On one hand, this might be because math, computing, and the sciences are familiar with working openly and giving things away. On the other, it might be because I am a member of the computing faculty, and therefore am most familiar with resources in that space.');\\n INSERT INTO grafit_article (id, title, text) VALUES (24, 'Apache Software Foundation Public Mail Archives', 'A collection of all publicly available mail archives from the Apache55 Software Foundation (ASF), taken on July 11, 2011. This collection contains all publicly available email archives from the ASF''s 80+ projects (http://mail-archives.apache.org/mod_mbox/), including mailing lists such as Apache HTTPD Server, Apache Tomcat, Apache Lucene and Solr, Apache Hadoop and many more. Generally speaking, most projects have at least three lists: user, dev and commits, but some have more, some have less. The user lists are where users of the software ask questions on usage, while the dev list usually contains discussions on the development of the project (code, releases, etc.) The commit lists usually consists of automated notifications sent by the various ASF version control tools, like Subversion or CVS, and contain information about changes made to the project''s source code.\\n\\n Both tarballs and per project sets are available in the snapshot. The tarballs are organized according to project name. Thus, a-d.tar.gz contains all ASF projects that begin with the letters a, b, c or d, such as abdera.apache.org. Files within the project are usually gzipped mbox files.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (26, 'PostgreSQL - Overview', 'PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development phase and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness.\\n\\n This tutorial will give you a quick start with PostgreSQL and make you comfortable with PostgreSQL programming.\\n What is PostgreSQL?\\n\\n PostgreSQL (pronounced as post-gress-Q-L) is an open source relational database management system (DBMS) developed by a worldwide team of volunteers. PostgreSQL is not controlled by any corporation or other private entity and the source code is available free of charge.\\n A Brief History of PostgreSQL\\n\\n PostgreSQL, originally called Postgres, was created at UCB by a computer science professor named Michael Stonebraker. Stonebraker started Postgres in 1986 as a follow-up project to its predecessor, Ingres, now owned by Computer Associates.\\n\\n 1977-1985 − A project called INGRES was developed.\\n\\n Proof-of-concept for relational databases\\n\\n Established the company Ingres in 1980\\n\\n Bought by Computer Associates in 1994\\n\\n 1986-1994 − POSTGRES\\n\\n Development of the concepts in INGRES with a focus on object orientation and the query language - Quel\\n\\n The code base of INGRES was not used as a basis for POSTGRES\\n\\n Commercialized as Illustra (bought by Informix, bought by IBM)\\n\\n 1994-1995 − Postgres95\\n\\n Support for SQL was added in 1994\\n\\n Released as Postgres95 in 1995\\n\\n Re-released as PostgreSQL 6.0 in 1996\\n\\n Establishment of the PostgreSQL Global Development Team\\n\\n Key Features of PostgreSQL\\n\\n PostgreSQL runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It supports text, images, sounds, and video, and includes programming interfaces for C / C++, Java, Perl, Python, Ruby, Tcl and Open Database Connectivity (ODBC).\\n\\n PostgreSQL supports a large part of the SQL standard and offers many modern features including the following −\\n\\n Complex SQL queries\\n SQL Sub-selects\\n Foreign keys\\n Trigger\\n Views\\n Transactions\\n Multiversion concurrency control (MVCC)\\n Streaming Replication (as of 9.0)\\n Hot Standby (as of 9.0)\\n\\n You can check official documentation of PostgreSQL to understand the above-mentioned features. PostgreSQL can be extended by the user in many ways. For example by adding new −\\n\\n Data types\\n Functions\\n Operators\\n Aggregate functions\\n Index methods\\n\\n Procedural Languages Support\\n\\n PostgreSQL supports four standard procedural languages, which allows the users to write their own code in any of the languages and it can be executed by PostgreSQL database server. These procedural languages are - PL/pgSQL, PL/Tcl, PL/Perl and PL/Python. Besides, other non-standard procedural languages like PL/PHP, PL/V8, PL/Ruby, PL/Java, etc., are also supported.');\\n INSERT INTO grafit_article (id, title, text) VALUES (27, 'Setup PostgreSQL on Windows with Docker', 'Over the weekend I finally got the chance to start reading A Curious Moon by Rob Conery which is a book on learning PostgreSQL by following the fictional Dee Yan as she is thrown into database administrator role at an aerospace startup.\\n\\n I have a lot of experience using Microsoft’s SQL Server, but up until now, I haven’t touched PostgreSQL. For personal projects SQL Server’s cost and be prohibitive and the release of Rob’s book added up to a good time to give PostgreSQL a try.\\n Install Directly or not?\\n\\n On the download section of the official Postgres site, there is an option to download an installer. This is the route I was going to at first, but in Rob’s book, he suggests using a VM for Postgres installation on Windows. This kicked off a lot of searching on my part and didn’t find a good definitive answer on why that is or isn’t the way to do.\\n\\n In the end, I decided to try and run the Postgres process using Docker instead installing directly on Windows or dealing with a full VM.\\n Installing Docker\\n\\n Head to this link and click the Get Docker link to download the installer. After the install is complete you will have to log out and back in. When I logged back in I got a message about Hyper-V not being enabled.\\n\\n After logging back in I then got the following message about hardware-assisted virtualization not being enabled.\\n\\n After tweaking my BIOS settings and logging back in I was greeted by the Docker welcome screen.\\n\\n Open a command prompt and run the following command.\\n\\n docker run hello-world\\n\\n You should output that starts with the following if your installation is working.\\n\\n Hello from Docker!\\n This message shows that your installation appears to be working correctly.\\n\\n What about Postgres?\\n\\n Getting up and going with a container running Postgres was pretty simple and could be done with the following command which will create a container and expose the port used by Postgres so it can be accessed from the host.\\n\\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d postgres\\n\\n The problem with this approach is if you ever need to rebuild the container for some reason, like a new version of Postgres is released, your data will be lost. Thankfully I found this blog post which shows how to use a secondary container for the data leaving the Postgres container able to be destroyed and recreated as needed. The following is the command I used to create my data container.\\n\\n docker create -v /var/lib/postgresql/data --name PostgresData alpine\\n\\n The above creates a container named PostgresData based on the Alpine image. It is important that the -v parameter matches the path that Postgres expects.\\n\\n Now that we have a container that will keep our data safe let’s create the actual Postgres container with the following command.\\n\\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d --volumes-from PostgresData postgres\\n\\n The only difference from the first example run command is the addition of –volumes-from PostgresData which tells the container to use the PostgresData container.\\n\\n If you run the docker ps -a command it will show you all your containers.\\n\\n As you can see in my example I have two containers only one of which is actually running. Make sure you don’t remove the data container just because it will never show as running.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (28, 'DIY: A PostgreSQL database server setup anyone can handle', 'When it comes to databases, I''m a fan of MySQL. The open source database can handle just about any load you want to throw at it, and it has lots of powerful tools that can be used to manage it.\\n\\n The other popular open source database is PostgreSQL, which is cross-platform and is used by numerous applications. Although PostgreSQL is often seen as being as powerful as MySQL, it doesn''t have nearly the number of available tools to make setup and management as easy as its competition. So I''ve written this handy PostgreSQL primer on how to get your database server up and running and ready to use. (Although PostgreSQL is cross-platform, I demonstrate the installation and setup on a Ubuntu 11.04 machine because it''s my platform of choice. The translation to other platforms should be simple.)\\n Step 1: Install PostgreSQL\\n\\n Here are the installation steps on Ubuntu (this installation will also work on any Debian-based distribution):\\n\\n Open a terminal window.\\n Issue the command sudo apt-get install postgresql.\\n Type the sudo password necessary to give you admin rights and hit Enter.\\n Allow apt to pick up any necessary dependencies.\\n\\n Once the installation is complete, it''s time to set this baby up.\\n Step 2: Change the default user password\\n\\n Caution: If you don''t follow this step, you will not be able to add databases and administer PostgreSQL, and the database will not be secure.\\n\\n Here''s how to change the password for the default user. The user in question is postgres, and the password is changed like so:\\n\\n Open a terminal window.\\n Issue the command sudo passwd postgres.\\n Type (and confirm) that password to be used for this user.\\n\\n The postgres user will be the only user on your system that can open the PostgreSQL prompt without defining a database, which means postgres is the only user who can administer PostgreSQL. To test this, change to the postgres user with the command su - postgres and then enter the command psql. You should now be at the Postgres prompt, which looks like:\\n\\n postgres=#\\n\\n All other users have to gain access to the prompt like so:\\n\\n psql DB_NAME\\n\\n where DB_NAME is the name of an existing database.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (31, 'The Marketing Behind MongoDB', ' 100% of my friends who have used Mongo/similar NoSQL have given up and had a nasty rewrite back to pgSQL.\\n\\n This seems to be the journey:\\n\\n 1. Lack of migrations is awesome! We can iterate so quickly for MVP\\n\\n 2. Get users\\n\\n 3. Add features, still enjoying the speed of iteration\\n\\n 4. Get more users\\n\\n 5. Start building reporting features for enterprise/customer support/product metrics (ie: when the real potential success starts)\\n\\n 6. Realise you desperately need joins, transactions and other SQL features\\n\\n 7. Pause product dev for 1-3+ months to migrate back to SQL, or do some weird parallel development process to move it piecemeal back.\\n\\n I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\\n\\n My thought is definitely yes.\\n\\n \\n \\n brandur on Aug 29, 2017 [-]\\n\\n > I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\\n\\n I''ve used Postgres and Mongo pretty extensively, and for any reasonably seasoned developer, the startup overhead of an SQL system is a myth. There may upfront cost to learning how an RDMS and SQL work in the first place, but once you''re familiar with them, they''ll be faster than Mongo on any new project.\\n\\n The schemaless concept of a document database seems to be the major selling factor in velocity of movement, but once you''ve got a good handle on a migration framework in the vein of ActiveRecord or other popular software, that''s negated completely. It also really doesn''t take long before schemaless starts to cause big problems for you in terms of data consistency -- it''s not just the big players that get bitten by this.\\n\\n The simplified query language is another one. SQL is a little bit obtuse, but it''s not that bad once you have a handle on it, and a lot of people are familiar with it. Once you add in an ORM layer, the lazy-style access of a framework like Sequel or SQLAlchemy makes the developer experience quite a bit better than any Mongo APIs that I''ve seen. Also, after you get beyond trivial usage, SQL''s flexibility so wildly outstrips Mongo''s query documents that it''s not even worth talking about.\\n\\n Postgres on the other hand ships with a great management CLI, a very powerful REPL (psql), and features like data types/constraints/transactions that guarantee you correctness with zero effort on your part. I can only speak for myself, but I''d take Postgres to the hackathon any day of the week.\\n\\n \\n \\n martinald on Aug 29, 2017 [-]\\n\\n I totally agree with you, and started writing something about how understanding a good ORM takes nearly all the headache away.\\n\\n I think the thing people do find slow is a lot of ''documents within documents'' in SQL. It turns out this is usually a bad development pattern long term but it is super fast being able to just add docs inside docs with no configuration. It feels very slow writing foreign keys, navigation props and schemas for this in SQL vs JSON, where you can just dump your object in and you''re done.\\n\\n Basically; I think with noSQL you get some very short term gain for a lot of long term pain, and you''re right, ORMs and other tooling solves this mostly.\\n\\n I myself fell for this trap, and while it was a nightmare it actually matured me more as a professional more than anything I''ve ever done recently. Regardless of crazy hype, I don''t think I''ll ever fall for a solution so easily without evaluating it properly.\\n\\n I think I assumed the \\\"crowd\\\" had done the tech due diligence on this stuff and it definitely wasn''t the case. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (32, 'Countless NoSQL databases competed to be the database of choice', 'n 2013, 10gen — the company behind MongoDB — moved into a large 30,000 square foot office in Midtown Manhattan.\\n\\n The transfer into the former New York Times building capped off a tremendous period of growth: the database boasted 4 million downloads, the MongoDB User Groups already attracted 15,000 members, and ~10,000 people had attended a global event in 2012. Their offices were truly global from London to Sydney to Dublin and Barcelona — and a requisite west coast headquarters in Palo Alto.\\n\\n Despite the traction, many startups using MongoDB faced their own challenges. One part of MongoDB’s success among startups was because some didn''t critically assess 10gen’s marketing message.\\n\\n As engineers, we often discuss technical attacks (e.g., DDoS, Sybil attacks, security vulnerabilities), but need to spend time debating how to protect ourselves from marketing “attacks”.1 Today, developer marketing is subtle — third party blog posts, content marketing disguised as engineering lessons, biased talks, and sponsored hackathons — not clearly marked content from vendors. As such, startup engineering decisions can hinge on sources that are not impartial.\\n\\n A large amount of \\\"engineering\\\" content — even when written by engineers — is actually marketing, rather than thoughtful content whose aim is to help you make the best decision.\\n\\n Previously, we looked at the hype around NoSQL and common engineering mistakes to see how MongoDB became so successful. Now, let''s take a look into 10gen''s marketing strategy — as told by their employees.2\\n\\n 10gen’s marketing strategy is an increasingly common playbook and understanding it is useful for future developer tool decisions.');\\n INSERT INTO grafit_article (id, title, text) VALUES (30, 'Comment Arrango', ' ArangoDB always makes for exciting benchmark posts.\\n\\n I could see myself there in a bowler hat with a fistful of racing chits screaming “go, Postgres, go.”\\n\\n I’d love to see a competition were the developers of each database got to use the same hardware and data then tune the hell out of their configs, queries, and indices.\\n\\n Red Bull could sponsor it. I’d buy a T-shirt.\\n\\n \\n \\n kbenson 8 months ago [-]\\n\\n That doesn''t sound that hard to start. Something like RealWorld[1] and the Web Framework Benchmarks[2] combined but for DB workloads. Have one dataset that includes data amenable to OLAP and OLTP, but have separate tests each consisting of OLAP queries, OLTP queries, and combined queries. Choose a low-end, mid-range and high-end set of AWS or GCE instances/configs to normalize against. Let people submit pull requests with new technologies or configs.\\n\\n You''d want to get some funding to run the tests (or maybe solicit Google or Amazon to see if you could get the instance time donated once a month or something.\\n\\n If you started small, with maybe a portion of these features, and then scaled up over time, you might actually get to the point where you had tests that emulated a power failure, or master/slave and dual master scenarios and how they handle certain common network errors (split-brain). That would be an amazing resource.\\n\\n Edit: It occurs to me I probably should have read more of the article, since this is sort of what they are doing already...\\n\\n 1: https://github.com/gothinkster/realworld\\n\\n 2: https://www.techempower.com/benchmarks/\\n\\n \\n \\n etxm 8 months ago [-]\\n\\n Yeah after I posted it I started thinking about what it would take and what that would actually look like... and how you’d cheat :)\\n\\n It would probably require a few different categories with some sort of output assertion to validate the query performed right and a means of tracking CPU, usage ram usage, and execution time.\\n\\n It would be cool to see things like disaster recovery and chaos proofing as well. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (35, 'Applying machine intelligence to GitHub security alerts ', 'Last year, we released security alerts that track security vulnerabilities in Ruby and JavaScript packages. Since then, we’ve identified more than four million of these vulnerabilities and added support for Python. In our launch post, we mentioned that all vulnerabilities with CVE IDs are included in security alerts, but sometimes there are vulnerabilities that are not disclosed in the National Vulnerability Database. Fortunately, our collection of security alerts can be supplemented with vulnerabilities detected from activity within our developer community.\\n Leveraging the community\\n\\n There are many places a project can publicize security fixes within a new version: the CVE feed, various mailing lists and open source groups, or even within its release notes or changelog. Regardless of how projects share this information, some developers within the GitHub community will see the advisory and immediately bump their required versions of the dependency to a known safe version. If detected, we can use the information in these commits to generate security alerts for vulnerabilities which may not have been published in the CVE feed.\\n\\n On an average day, the dependency graph can track around 10,000 commits to dependency files for any of our supported languages. We can’t manually process this many commits. Instead, we depend on machine intelligence to sift through them and extract those that might be related to a security release.\\n\\n For this purpose, we created a machine learning model that scans text associated with public commits (the commit message and linked issues or pull requests) to filter out those related to possible security upgrades. With this smaller batch of commits, the model uses the diff to understand how required version ranges have changed. Then it aggregates across a specific timeframe to get a holistic view of all dependencies that a security release might affect. Finally, the model outputs a list of packages and version ranges it thinks require an alert and currently aren’t covered by any known CVE in our system.\\n Always quality focused\\n\\n No machine learning model is perfect. While machine intelligence can sift through thousands of commits in an instant, this anomaly-detection algorithm will still generate false positives for packages where no security patch was released. Security alert quality is a focus for us, so we review all model output before the community receives an alert.\\n Learn more');\\n INSERT INTO grafit_article (id, title, text) VALUES (29, 'Performance Benchmark 2018', 'I''ve stopped reading database benchmarks, because they are extremely vague. Instead I spend my time optimizing my current solution/stack. For example Postgresql has hundreds of knobs that you can adjust for almost every scenario you can imagine. Sometimes you have a special query and increase the work_mem just for that session. Other cases you adjust the cost settings for another query/session. You can analyze your indexes and index types. And sometimes you need to rewrite parts of a big query.\\n\\n Learning all this takes time, you are much better off learning more about your chosen technology stack than switching to another technology stack.\\n\\n Though in a few rare races, you need a different technology to solve your business problem. In most cases they complement your existing solution, like Elasticsearch/Solr for full-text search or Clickhouse for OLAP workloads.\\n\\n \\n \\n maxxxxx 8 months ago [-]\\n\\n Agreed. Switching to another system is expensive and the benefit is pretty questionable.\\n\\n \\n \\n emsy 8 months ago [-]\\n\\n Unless you hit a very specific use-case/bottleneck, which I only ever witnessed once.\\n\\n \\n \\n TremendousJudge 8 months ago [-]\\n\\n expand, please?\\n\\n \\n \\n maxxxxx 8 months ago [-]\\n\\n I imagine something very specific like having a lot of inserts into a table and that being your main use case. Depending on your data some databases may be better than others and that should be easy to measure.\\n\\n In most real-world cases the requirements however are not very clear and often conflicting so it''s much harder to get data that shows the performance of one system over the other.\\n\\n \\n \\n gopalv 8 months ago [-]\\n\\n > Depending on your data some databases may be better than others and that should be easy to measure.\\n\\n And the performance difference could be an accidental feature of the design and completely unintentional.\\n\\n Postgres for instance has a native data engine, so it can store the exact row-ids for a row into an index, but this means that every update to the row needs all indexes to be updated.\\n\\n Mysql has many data engines (InnoDB and MyISAM to start with), to the row-id is somewhat opaque, so the index stores the primary key which can be pushed to the data engine scans and then have it lookup a row-id internally. This needs an index to be touched for the columns you modify explicitly or if the primary key is updated (which is a usual no-no due to UNIQUE lookup costs).\\n\\n When you have a single wide table with a huge number of indexes, where you update a lot of dimensions frequently, the performance difference between these two solutions is architectural.\\n\\n And if you lookup along an index with few updates, but long running open txns, that is also materially different - one lookup versus two.\\n\\n Though how it came about isn''t really intentional. ');\\n \\\"\\\"\\\"\\n )]\\n\",\n \"<import token>\\n\\n\\nclass Migration(migrations.Migration):\\n dependencies = [('grafit', '0002_article')]\\n operations = [migrations.RunSQL(\\n \\\"\\\"\\\"\\n INSERT INTO grafit_article (id, title, text) VALUES (2, 'MongoDB', 'MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemata. MongoDB is developed by MongoDB Inc., and is published under a combination of the Server Side Public License and the Apache License.\\n 10gen software company began developing MongoDB in 2007 as a component of a planned platform as a service product. In 2009, the company shifted to an open source development model, with the company offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.[6]\\n\\n On October 20, 2017, MongoDB became a publicly-traded company, listed on NASDAQ as MDB with an IPO price of $24 per share.[7] Ad hoc queries\\n\\n MongoDB supports field, range query, and regular expression searches.[8] Queries can return specific fields of documents and also include user-defined JavaScript functions. Queries can also be configured to return a random sample of results of a given size.\\n Indexing\\n\\n Fields in a MongoDB document can be indexed with primary and secondary indices.\\n Replication\\n\\n MongoDB provides high availability with replica sets.[9] A replica set consists of two or more copies of the data. Each replica set member may act in the role of primary or secondary replica at any time. All writes and reads are done on the primary replica by default. Secondary replicas maintain a copy of the data of the primary using built-in replication. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.\\n Load balancing[10]\\n\\n MongoDB scales horizontally using sharding. The user chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.). Alternatively, the shard key can be hashed to map to a shard – enabling an even data distribution.\\n\\n MongoDB can run over multiple servers, balancing the load or duplicating data to keep the system up and running in case of hardware failure. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (3, 'NoSQL', 'A NoSQL (originally referring to \\\"non SQL\\\" or \\\"non relational\\\")[1] database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but did not obtain the \\\"NoSQL\\\" moniker until a surge of popularity in the early twenty-first century,[2] triggered by the needs of Web 2.0 companies.[3][4][5] NoSQL databases are increasingly used in big data and real-time web applications.[6] NoSQL systems are also sometimes called \\\"Not only SQL\\\" to emphasize that they may support SQL-like query languages, or sit alongside SQL database in a polyglot persistence architecture.[7][8]\\n\\n Motivations for this approach include: simplicity of design, simpler \\\"horizontal\\\" scaling to clusters of machines (which is a problem for relational databases),[2] and finer control over availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph, or document) are different from those used by default in relational databases, making some operations faster in NoSQL. The particular suitability of a given NoSQL database depends on the problem it must solve. Sometimes the data structures used by NoSQL databases are also viewed as \\\"more flexible\\\" than relational database tables.[9]\\n\\n Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages (instead of SQL, for instance the lack of ability to perform ad-hoc joins across tables), lack of standardized interfaces, and huge previous investments in existing relational databases.[10] Most NoSQL stores lack true ACID transactions, although a few databases, such as MarkLogic, Aerospike, FairCom c-treeACE, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them central to their designs. (See ACID and join support.)\\n\\n Instead, most NoSQL databases offer a concept of \\\"eventual consistency\\\" in which database changes are propagated to all nodes \\\"eventually\\\" (typically within milliseconds) so queries for data might not return updated data immediately or might result in reading data that is not accurate, a problem known as stale reads.[11] Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss.[12] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss.[13] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases. Even current relational databases \\\"do not allow referential integrity constraints to span databases.\\\"[14] There are few systems that maintain both ACID transactions and X/Open XA standards for distributed transaction processing. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (4, 'SQL', 'SQL was initially developed at IBM by Donald D. Chamberlin and Raymond F. Boyce after learning about the relational model from Ted Codd[15] in the early 1970s.[16] This version, initially called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM''s original quasi-relational database management system, System R, which a group at IBM San Jose Research Laboratory had developed during the 1970s.[16]\\n\\n Chamberlin and Boyce''s first attempt of a relational database language was Square, but it was difficult to use due to subscript notation. After moving to the San Jose Research Laboratory in 1973, they began work on SEQUEL.[15] The acronym SEQUEL was later changed to SQL because \\\"SEQUEL\\\" was a trademark of the UK-based Hawker Siddeley aircraft company.[17]\\n\\n In the late 1970s, Relational Software, Inc. (now Oracle Corporation) saw the potential of the concepts described by Codd, Chamberlin, and Boyce, and developed their own SQL-based RDBMS with aspirations of selling it to the U.S. Navy, Central Intelligence Agency, and other U.S. government agencies. In June 1979, Relational Software, Inc. introduced the first commercially available implementation of SQL, Oracle V2 (Version2) for VAX computers. By 1986, ANSI and ISO standard groups officially adopted the standard \\\"Database Language SQL\\\" language definition. New versions of the standard were published in 1989, 1992, 1996, 1999, 2003, 2006, 2008, 2011,[15] and most recently, 2016. After testing SQL at customer test sites to determine the usefulness and practicality of the system, IBM began developing commercial products based on their System R prototype including System/38, SQL/DS, and DB2, which were commercially available in 1979, 1981, and 1983, respectively.[18] ');\\n INSERT INTO grafit_article (id, title, text) VALUES (5, 'MySQL', 'Built on MySQL Enterprise Edition and powered by the Oracle Cloud, Oracle MySQL Cloud Service provides a simple, automated, integrated and enterprise ready MySQL cloud service, enabling organizations to increase business agility and reduce costs. \\\"Relying on the MySQL engine as the low-level storage layer has allowed us to very quickly build a robust system.\\\"\\n \\n\\n \\\"We have successfully implemented MySQL Cluster Carrier Grade Edition for our highly mission critical XDMS application which will enable the next generation of converged services.\\\"\\n \\n\\n \\\"We found that MySQL was the best database in terms of the price-point and functionality it offers up. The benefits that MySQL brings to our Brightmail product is its relaiability, robustness and very low-cost administration costs.\\\"');\\n INSERT INTO grafit_article (id, title, text) VALUES (6, 'Critical Flaw Reported In phpMyAdmin Lets Attackers Damage Databases', 'A critical security vulnerability has been reported in phpMyAdmin—one of the most popular applications for managing the MySQL database—which could allow remote attackers to perform dangerous database operations just by tricking administrators into clicking a link.\\n\\n Discovered by an Indian security researcher, Ashutosh Barot, the vulnerability is a cross-site request forgery (CSRF) attack and affects phpMyAdmin versions 4.7.x (prior to 4.7.7).\\n\\n Cross-site request forgery vulnerability, also known as XSRF, is an attack wherein an attacker tricks an authenticated user into executing an unwanted action.\\n\\n According to an advisory released by phpMyAdmin, \\\"by deceiving a user to click on a crafted URL, it is possible to perform harmful database operations such as deleting records, dropping/truncating tables, etc.\\\"\\n\\n phpMyAdmin is a free and open source administration tool for MySQL and MariaDB and is widely used to manage the database for websites created with WordPress, Joomla, and many other content management platforms.\\n\\n Moreover, a lot of hosting providers use phpMyAdmin to offer their customers a convenient way to organize their databases.\\n Barot has also released a video, as shown above, demonstrating how a remote attacker can make database admins unknowingly delete (DROP) an entire table from the database just by tricking them into clicking a specially crafted link.\\n\\n \\\"A feature of phpMyAdmin was using a GET request and after that POST request for Database operations such as DROP TABLE table_name; GET requests must be protected against CSRF attacks. In this case, POST requests were used which were sent through URL (for bookmarking purpose may be); it was possible for an attacker to trick a database admin into clicking a button and perform a drop table database query of the attacker’s choice.\\\" Barot explains in a blog post.\\n\\n However, performing this attack is not simple as it may sound. To prepare a CSRF attack URL, the attacker should be aware of the name of targeted database and table.\\n\\n \\\"If a user executes a query on the database by clicking insert, DROP, etc. buttons, the URL will contain database name and table name,\\\" Barot says. \\\"This vulnerability can result in the disclosure of sensitive information as the URL is stored at various places such as browser history, SIEM logs, Firewall Logs, ISP Logs, etc.\\\"\\n\\n Barot reported the vulnerability to phpMyAdmin developers, who confirmed his finding and released phpMyAdmin 4.7.7 to address this issue. So administrators are highly recommended to update their installations as soon as possible.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (25, 'Death By Database', 'The following is a true story, but with names changed.\\n\\n When I work with clients to build software, I take the usual steps of understanding their needs, gathering requirements, learning about their customers, and so on. At this point I have a model on paper of roughly what the software is intended to do, so they get surprised when I immediately turn to database design.\\n\\n \\\"Who care about database design? What about mockups? What about workflows?\\\"\\n\\n Let me tell you about \\\"Bob''s Luxury Goods.\\\" I worked for this company many years ago and they had a retail store selling ... you guessed it ... luxury goods. They''d ask all customers for a billing address and if they had a different delivery address. At the database level, they had a \\\"one-to-many\\\" relationship between customers and addresses.\\n\\n That was their first problem. A customer''s partner might come into Bob''s and order something and if the address was entered correctly it would be flagged as \\\"in use\\\" and we had to use a different address or deliberately enter a typo. Fortunately, addresses were case-sensitive, so many people had UPPER-CASE ADDRESSES.\\n\\n We should have had a many-to-many relationship between customers and addresses so we could handle the case where more than one person would share the same address, but we didn''t. Further, I was never allocated the time to fix the database because it was \\\"cheaper\\\" to remove the restriction on \\\"flagged\\\" addresses and allow a duplicate address to be used.\\n\\n Naturally, being a luxury goods company, we had many repeat customers and sometimes they would move and if we didn''t find the duplicate address, or the address with the \\\"typo\\\", we might update the address for one partner, but not the other. That was a headache, but it didn''t happen frequently enough for management to worry about it.\\n\\n That''s when the marketing department had a brilliant, inexpensive idea. You see, we periodically did mass mailings of special events to our customers. Since we had the software to do mass mailings, why not import a mailing list of all addresses in high net worth areas and mail everyone about upcoming special events? So the company went ahead and bought a database with all of these addresses, but forgot to mention to me that I was supposed to implement this.\\n\\n Except that every address record had the customer id embedded in it, so we couldn''t enter an address without a customer.\\n\\n \\\"Curtis,\\\" they said, \\\"just enter a dummy customer called ''Occupant'' and attach all addresses to that.\\\"\\n\\n Except you couldn''t enter a customer without an order.\\n\\n Except you couldn''t enter an order without at least one item on it.\\n\\n Except you couldn''t enter an item unless it was listed in inventory.\\n\\n Except that reserved the \\\"inventory\\\" item and made it unavailable.\\n\\n Except, except, except ...\\n\\n It came down to trying to create a fake customer, with a fake order, with a fake item, with a fake item category, with a \\\"paid\\\" invoice, with exceptions sprinkled throughout the codebase to handle all of these special cases and probably more that I no longer remember.\\n\\n Then, and only then, could I write the code to provide \\\"generic\\\" mass mailings. Management decided it was easier to hire an outside company to handle the mailing list for them.\\n\\n If they had simply had a proper database design up front, they could have reused their existing system with little trouble.\\n\\n That''s what bad database design costs you and why I usually start with that before writing my software.\\n\\n Note: if you''re not familiar with database design, here''s a talk I give where I make it fairly simple to understand. I mostly avoid big words.');\\n INSERT INTO grafit_article (id, title, text) VALUES (33, 'GitHub Actions: built by you, run by us', 'Yesterday at GitHub Universe, we announced GitHub Actions, a new way to automate and customize your workflows. Configuring the apps and services that make up your development cycle takes significant time and effort. GitHub Actions applies open source principles to workflow automation, weaving together the tools you use from idea to production into one complete workflow. You can also create, share, and discover any actions your projects require, just as you would create, share, and discover code on GitHub.\\n\\n Learn more about actions\\n\\n As we prepared for Universe, we shared GitHub Actions with a group of customers, integrators, and open source maintainers to see what they could do. In just a few short weeks, talented teams and individuals alike have created hundreds of GitHub Actions. During today’s Universe keynote, we heard directly from developers, and we’re excited to share their work with you');\\n INSERT INTO grafit_article (id, title, text) VALUES (34, 'Git Submodule Vulnerability Announced ', '\\n\\n The Git project has disclosed CVE-2018-17456, a vulnerability in Git that can cause arbitrary code to be executed when a user clones a malicious repository. Git v2.19.1 has been released with a fix, along with backports in v2.14.5, v2.15.3, v2.16.5, v2.17.2, and v2.18.1. We encourage all users to update their clients to protect themselves.\\n\\n Until you’ve updated, you can protect yourself by avoiding submodules from untrusted repositories. This includes commands such as git clone --recurse-submodules and git submodule update.\\n Affected products\\n GitHub Desktop\\n\\n GitHub Desktop versions 1.4.1 and older included an embedded version of Git that was affected by this vulnerability. We encourage all GitHub Desktop users to update to the newest version (1.4.2 and 1.4.3-beta0) available today in the Desktop app.\\n Atom\\n\\n Atom included the same embedded Git and was also affected. Releases 1.31.2 and 1.32.0-beta3 include the patch.\\n\\n Ensure you’re on the latest Atom release by completing any of the following:\\n\\n Windows: From the toolbar, click Help -> Check for Updates\\n MacOS: From the menu bar, click Atom -> Check for Update\\n Linux: Update manually by downloading the latest release from atom.io\\n\\n Git on the command line and other clients\\n\\n In order to be protected from the vulnerability, you must update your command-line version of Git, and any other application that may include an embedded version of Git, as they are independent of each other.\\n Additional notes\\n\\n Neither GitHub.com nor GitHub Enterprise are directly affected by the vulnerability. However, as with previously discovered vulnerabilities, GitHub.com will detect malicious repositories, and will reject pushes or API requests attempting to create them. Versions of GitHub Enterprise with this detection will ship on October 9.\\n Details of the vulnerability\\n\\n This vulnerability is very similar to CVE-2017-1000117, as both are option-injection attacks related to submodules. In the earlier attack, a malicious repository would ship a .gitmodules file pointing one of its submodules to a remote repository with an SSH host starting with a dash (-). The ssh program—spawned by Git—would then interpret that as an option. This attack works in a similar way, except that the option-injection is against the child git clone itself.\\n\\n The problem was reported on September 23 by @joernchen, both to Git’s private security list, as well as to GitHub’s Bug Bounty program. Developers at GitHub worked with the Git community to develop a fix.\\n\\n The basic fix was clear from the report. However, due to to the similarity to CVE-2017-1000117, we also audited all of the .gitmodules values and implemented stricter checks as appropriate. These checks should prevent a similar vulnerability in another code path. We also implemented detection of potentially malicious submodules as part of Git’s object quality checks (which was made much easier by the infrastructure added during the last submodule-related vulnerability).\\n\\n The coordinated disclosure date of October 5 was selected by Git developers to allow packagers to prepare for the release. This also provided hosting sites (with custom implementations) ample time to detect and block the attack before it became public. Members of the Git community checked the JGit and libgit2 implementations. Those are not affected by the vulnerability because they clone submodules via function calls rather than separate commands.\\n\\n We were also able to use the time to scan all repositories on GitHub for evidence of the attack being used in the wild. We’re happy to report that no instances were found (and now, with our detection, none can be added).\\n\\n Please update your copy of Git soon, and happy cloning!\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (21, 'Hackers Targeting Servers Running Database Services for Mining Cryptocurrency', 'Security researchers have discovered multiple attack campaigns conducted by an established Chinese criminal group that operates worldwide, targeting database servers for mining cryptocurrencies, exfiltrating sensitive data and building a DDoS botnet.\\n\\n The researchers from security firm GuardiCore Labs have analyzed thousands of attacks launched in recent months and identified at least three attack variants—Hex, Hanako, and Taylor—targeting different MS SQL and MySQL servers for both Windows and Linux.\\n\\n The goals of all the three variants are different—Hex installs cryptocurrency miners and remote access trojans (RATs) on infected machines, Taylor installs a keylogger and a backdoor, and Hanako uses infected devices to build a DDoS botnet.\\n\\n So far, researchers have recorded hundreds of Hex and Hanako attacks and tens of thousands of Taylor attacks each month and found that most compromised machines are based in China, and some in Thailand, the United States, Japan and others.\\n\\n To gain unauthorized access to the targeted database servers, the attackers use brute force attacks and then run a series of predefined SQL commands to gain persistent access and evade audit logs.\\n\\n What''s interesting? To launch the attacks against database servers and serve malicious files, attackers use a network of already compromised systems, making their attack infrastructure modular and preventing takedown of their malicious activities.');\\n INSERT INTO grafit_article (id, title, text) VALUES (22, 'RIP Open Source MySQL', ' This is an excellent opportunity for the Postgres community to step up an promote Postgres.\\n\\n \\n \\n rbanffy on Aug 18, 2012 [-]\\n\\n I think this would be a mistake.\\n\\n This is an excellent opportunity to demonstrate that anyone can fork the MySQL codebase and create other plug-in replacement databases with it, such as MariaDB and Drizzle.\\n\\n All that is lost is the MySQL name and brand.\\n\\n PostgreSQL users and developers must seize the opportunity to show businesses that free software cannot be killed, not even by mighty Oracle. They and, most notably, Microsoft, have been trying to kill it for more than a decade now.\\n\\n Because the anti-free-software FUD machine (fed in part by Oracle itself) is already having a wonderful time with this.\\n\\n \\n \\n Udo on Aug 18, 2012 [-]\\n\\n I wish I could mod this up a hundred times. PostgreSQL people themselves have been playing into the hands of corporate FUDders with their incessant and inappropriate peddling. MySQL is not your enemy, MS SQL Server is. Oracle''s software empire as a whole certainly is your enemy. Show some solidarity with a fellow open source project!\\n\\n MySQL and PostgreSQL represent two very different implementation philosophies, and being able to choose between them according to taste and merits is a good thing.\\n\\n Most of us have suspected that the MySQL project itself was going to die as it was acquired by Oracle, in the same way Open Office died when it was acquired by Oracle. This is a company where good software goes to expire, either due to a deliberate intention or gross incompetence I can''t say but I suspect it''s a mixture of both. However sad that may be for the MySQL (or OpenOffice) brand name, the code itself lives on and continues to evolve within a rich open source ecosystem.\\n\\n Hence, sensational and petulant \\\"RIP $PRODUCTNAME\\\" articles are unnecessary. There is no threat to existing projects based on MySQL or any other successful open source project for that matter. Not only will this stuff be free forever, it will also continue to grow and be developed on its own.\\n\\n The corporate assassination of open source projects will only work if we let it, it''s a purely psychological game. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (23, 'Free Text Sources', 'There are a few interesting things to talk about surrounding free and open textbooks. Quality is one. Usability is another. Why to write one (and/or, why not) is certainly critical. But where can you find these disruptive, open texts?\\n\\n Not all faculty know there are free and open texts they can use; finding free and/or open textbooks (or even knowing to look) can sometimes be a trick. I knew about one or two sources, and did a little bit more digging. Admittedly, many of the sources of free texts linked below have a technical bent. On one hand, this might be because math, computing, and the sciences are familiar with working openly and giving things away. On the other, it might be because I am a member of the computing faculty, and therefore am most familiar with resources in that space.');\\n INSERT INTO grafit_article (id, title, text) VALUES (24, 'Apache Software Foundation Public Mail Archives', 'A collection of all publicly available mail archives from the Apache55 Software Foundation (ASF), taken on July 11, 2011. This collection contains all publicly available email archives from the ASF''s 80+ projects (http://mail-archives.apache.org/mod_mbox/), including mailing lists such as Apache HTTPD Server, Apache Tomcat, Apache Lucene and Solr, Apache Hadoop and many more. Generally speaking, most projects have at least three lists: user, dev and commits, but some have more, some have less. The user lists are where users of the software ask questions on usage, while the dev list usually contains discussions on the development of the project (code, releases, etc.) The commit lists usually consists of automated notifications sent by the various ASF version control tools, like Subversion or CVS, and contain information about changes made to the project''s source code.\\n\\n Both tarballs and per project sets are available in the snapshot. The tarballs are organized according to project name. Thus, a-d.tar.gz contains all ASF projects that begin with the letters a, b, c or d, such as abdera.apache.org. Files within the project are usually gzipped mbox files.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (26, 'PostgreSQL - Overview', 'PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development phase and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness.\\n\\n This tutorial will give you a quick start with PostgreSQL and make you comfortable with PostgreSQL programming.\\n What is PostgreSQL?\\n\\n PostgreSQL (pronounced as post-gress-Q-L) is an open source relational database management system (DBMS) developed by a worldwide team of volunteers. PostgreSQL is not controlled by any corporation or other private entity and the source code is available free of charge.\\n A Brief History of PostgreSQL\\n\\n PostgreSQL, originally called Postgres, was created at UCB by a computer science professor named Michael Stonebraker. Stonebraker started Postgres in 1986 as a follow-up project to its predecessor, Ingres, now owned by Computer Associates.\\n\\n 1977-1985 − A project called INGRES was developed.\\n\\n Proof-of-concept for relational databases\\n\\n Established the company Ingres in 1980\\n\\n Bought by Computer Associates in 1994\\n\\n 1986-1994 − POSTGRES\\n\\n Development of the concepts in INGRES with a focus on object orientation and the query language - Quel\\n\\n The code base of INGRES was not used as a basis for POSTGRES\\n\\n Commercialized as Illustra (bought by Informix, bought by IBM)\\n\\n 1994-1995 − Postgres95\\n\\n Support for SQL was added in 1994\\n\\n Released as Postgres95 in 1995\\n\\n Re-released as PostgreSQL 6.0 in 1996\\n\\n Establishment of the PostgreSQL Global Development Team\\n\\n Key Features of PostgreSQL\\n\\n PostgreSQL runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It supports text, images, sounds, and video, and includes programming interfaces for C / C++, Java, Perl, Python, Ruby, Tcl and Open Database Connectivity (ODBC).\\n\\n PostgreSQL supports a large part of the SQL standard and offers many modern features including the following −\\n\\n Complex SQL queries\\n SQL Sub-selects\\n Foreign keys\\n Trigger\\n Views\\n Transactions\\n Multiversion concurrency control (MVCC)\\n Streaming Replication (as of 9.0)\\n Hot Standby (as of 9.0)\\n\\n You can check official documentation of PostgreSQL to understand the above-mentioned features. PostgreSQL can be extended by the user in many ways. For example by adding new −\\n\\n Data types\\n Functions\\n Operators\\n Aggregate functions\\n Index methods\\n\\n Procedural Languages Support\\n\\n PostgreSQL supports four standard procedural languages, which allows the users to write their own code in any of the languages and it can be executed by PostgreSQL database server. These procedural languages are - PL/pgSQL, PL/Tcl, PL/Perl and PL/Python. Besides, other non-standard procedural languages like PL/PHP, PL/V8, PL/Ruby, PL/Java, etc., are also supported.');\\n INSERT INTO grafit_article (id, title, text) VALUES (27, 'Setup PostgreSQL on Windows with Docker', 'Over the weekend I finally got the chance to start reading A Curious Moon by Rob Conery which is a book on learning PostgreSQL by following the fictional Dee Yan as she is thrown into database administrator role at an aerospace startup.\\n\\n I have a lot of experience using Microsoft’s SQL Server, but up until now, I haven’t touched PostgreSQL. For personal projects SQL Server’s cost and be prohibitive and the release of Rob’s book added up to a good time to give PostgreSQL a try.\\n Install Directly or not?\\n\\n On the download section of the official Postgres site, there is an option to download an installer. This is the route I was going to at first, but in Rob’s book, he suggests using a VM for Postgres installation on Windows. This kicked off a lot of searching on my part and didn’t find a good definitive answer on why that is or isn’t the way to do.\\n\\n In the end, I decided to try and run the Postgres process using Docker instead installing directly on Windows or dealing with a full VM.\\n Installing Docker\\n\\n Head to this link and click the Get Docker link to download the installer. After the install is complete you will have to log out and back in. When I logged back in I got a message about Hyper-V not being enabled.\\n\\n After logging back in I then got the following message about hardware-assisted virtualization not being enabled.\\n\\n After tweaking my BIOS settings and logging back in I was greeted by the Docker welcome screen.\\n\\n Open a command prompt and run the following command.\\n\\n docker run hello-world\\n\\n You should output that starts with the following if your installation is working.\\n\\n Hello from Docker!\\n This message shows that your installation appears to be working correctly.\\n\\n What about Postgres?\\n\\n Getting up and going with a container running Postgres was pretty simple and could be done with the following command which will create a container and expose the port used by Postgres so it can be accessed from the host.\\n\\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d postgres\\n\\n The problem with this approach is if you ever need to rebuild the container for some reason, like a new version of Postgres is released, your data will be lost. Thankfully I found this blog post which shows how to use a secondary container for the data leaving the Postgres container able to be destroyed and recreated as needed. The following is the command I used to create my data container.\\n\\n docker create -v /var/lib/postgresql/data --name PostgresData alpine\\n\\n The above creates a container named PostgresData based on the Alpine image. It is important that the -v parameter matches the path that Postgres expects.\\n\\n Now that we have a container that will keep our data safe let’s create the actual Postgres container with the following command.\\n\\n docker run -p 5432:5432 --name yourContainerName -e POSTGRES_PASSWORD=yourPassword -d --volumes-from PostgresData postgres\\n\\n The only difference from the first example run command is the addition of –volumes-from PostgresData which tells the container to use the PostgresData container.\\n\\n If you run the docker ps -a command it will show you all your containers.\\n\\n As you can see in my example I have two containers only one of which is actually running. Make sure you don’t remove the data container just because it will never show as running.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (28, 'DIY: A PostgreSQL database server setup anyone can handle', 'When it comes to databases, I''m a fan of MySQL. The open source database can handle just about any load you want to throw at it, and it has lots of powerful tools that can be used to manage it.\\n\\n The other popular open source database is PostgreSQL, which is cross-platform and is used by numerous applications. Although PostgreSQL is often seen as being as powerful as MySQL, it doesn''t have nearly the number of available tools to make setup and management as easy as its competition. So I''ve written this handy PostgreSQL primer on how to get your database server up and running and ready to use. (Although PostgreSQL is cross-platform, I demonstrate the installation and setup on a Ubuntu 11.04 machine because it''s my platform of choice. The translation to other platforms should be simple.)\\n Step 1: Install PostgreSQL\\n\\n Here are the installation steps on Ubuntu (this installation will also work on any Debian-based distribution):\\n\\n Open a terminal window.\\n Issue the command sudo apt-get install postgresql.\\n Type the sudo password necessary to give you admin rights and hit Enter.\\n Allow apt to pick up any necessary dependencies.\\n\\n Once the installation is complete, it''s time to set this baby up.\\n Step 2: Change the default user password\\n\\n Caution: If you don''t follow this step, you will not be able to add databases and administer PostgreSQL, and the database will not be secure.\\n\\n Here''s how to change the password for the default user. The user in question is postgres, and the password is changed like so:\\n\\n Open a terminal window.\\n Issue the command sudo passwd postgres.\\n Type (and confirm) that password to be used for this user.\\n\\n The postgres user will be the only user on your system that can open the PostgreSQL prompt without defining a database, which means postgres is the only user who can administer PostgreSQL. To test this, change to the postgres user with the command su - postgres and then enter the command psql. You should now be at the Postgres prompt, which looks like:\\n\\n postgres=#\\n\\n All other users have to gain access to the prompt like so:\\n\\n psql DB_NAME\\n\\n where DB_NAME is the name of an existing database.\\n ');\\n INSERT INTO grafit_article (id, title, text) VALUES (31, 'The Marketing Behind MongoDB', ' 100% of my friends who have used Mongo/similar NoSQL have given up and had a nasty rewrite back to pgSQL.\\n\\n This seems to be the journey:\\n\\n 1. Lack of migrations is awesome! We can iterate so quickly for MVP\\n\\n 2. Get users\\n\\n 3. Add features, still enjoying the speed of iteration\\n\\n 4. Get more users\\n\\n 5. Start building reporting features for enterprise/customer support/product metrics (ie: when the real potential success starts)\\n\\n 6. Realise you desperately need joins, transactions and other SQL features\\n\\n 7. Pause product dev for 1-3+ months to migrate back to SQL, or do some weird parallel development process to move it piecemeal back.\\n\\n I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\\n\\n My thought is definitely yes.\\n\\n \\n \\n brandur on Aug 29, 2017 [-]\\n\\n > I think the most interesting question though is would they be able to get MVP and initial customers that set off this if they were moving (slightly) slower due to SQL and slight overhead that comes with?\\n\\n I''ve used Postgres and Mongo pretty extensively, and for any reasonably seasoned developer, the startup overhead of an SQL system is a myth. There may upfront cost to learning how an RDMS and SQL work in the first place, but once you''re familiar with them, they''ll be faster than Mongo on any new project.\\n\\n The schemaless concept of a document database seems to be the major selling factor in velocity of movement, but once you''ve got a good handle on a migration framework in the vein of ActiveRecord or other popular software, that''s negated completely. It also really doesn''t take long before schemaless starts to cause big problems for you in terms of data consistency -- it''s not just the big players that get bitten by this.\\n\\n The simplified query language is another one. SQL is a little bit obtuse, but it''s not that bad once you have a handle on it, and a lot of people are familiar with it. Once you add in an ORM layer, the lazy-style access of a framework like Sequel or SQLAlchemy makes the developer experience quite a bit better than any Mongo APIs that I''ve seen. Also, after you get beyond trivial usage, SQL''s flexibility so wildly outstrips Mongo''s query documents that it''s not even worth talking about.\\n\\n Postgres on the other hand ships with a great management CLI, a very powerful REPL (psql), and features like data types/constraints/transactions that guarantee you correctness with zero effort on your part. I can only speak for myself, but I''d take Postgres to the hackathon any day of the week.\\n\\n \\n \\n martinald on Aug 29, 2017 [-]\\n\\n I totally agree with you, and started writing something about how understanding a good ORM takes nearly all the headache away.\\n\\n I think the thing people do find slow is a lot of ''documents within documents'' in SQL. It turns out this is usually a bad development pattern long term but it is super fast being able to just add docs inside docs with no configuration. It feels very slow writing foreign keys, navigation props and schemas for this in SQL vs JSON, where you can just dump your object in and you''re done.\\n\\n Basically; I think with noSQL you get some very short term gain for a lot of long term pain, and you''re right, ORMs and other tooling solves this mostly.\\n\\n I myself fell for this trap, and while it was a nightmare it actually matured me more as a professional more than anything I''ve ever done recently. Regardless of crazy hype, I don''t think I''ll ever fall for a solution so easily without evaluating it properly.\\n\\n I think I assumed the \\\"crowd\\\" had done the tech due diligence on this stuff and it definitely wasn''t the case. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (32, 'Countless NoSQL databases competed to be the database of choice', 'n 2013, 10gen — the company behind MongoDB — moved into a large 30,000 square foot office in Midtown Manhattan.\\n\\n The transfer into the former New York Times building capped off a tremendous period of growth: the database boasted 4 million downloads, the MongoDB User Groups already attracted 15,000 members, and ~10,000 people had attended a global event in 2012. Their offices were truly global from London to Sydney to Dublin and Barcelona — and a requisite west coast headquarters in Palo Alto.\\n\\n Despite the traction, many startups using MongoDB faced their own challenges. One part of MongoDB’s success among startups was because some didn''t critically assess 10gen’s marketing message.\\n\\n As engineers, we often discuss technical attacks (e.g., DDoS, Sybil attacks, security vulnerabilities), but need to spend time debating how to protect ourselves from marketing “attacks”.1 Today, developer marketing is subtle — third party blog posts, content marketing disguised as engineering lessons, biased talks, and sponsored hackathons — not clearly marked content from vendors. As such, startup engineering decisions can hinge on sources that are not impartial.\\n\\n A large amount of \\\"engineering\\\" content — even when written by engineers — is actually marketing, rather than thoughtful content whose aim is to help you make the best decision.\\n\\n Previously, we looked at the hype around NoSQL and common engineering mistakes to see how MongoDB became so successful. Now, let''s take a look into 10gen''s marketing strategy — as told by their employees.2\\n\\n 10gen’s marketing strategy is an increasingly common playbook and understanding it is useful for future developer tool decisions.');\\n INSERT INTO grafit_article (id, title, text) VALUES (30, 'Comment Arrango', ' ArangoDB always makes for exciting benchmark posts.\\n\\n I could see myself there in a bowler hat with a fistful of racing chits screaming “go, Postgres, go.”\\n\\n I’d love to see a competition were the developers of each database got to use the same hardware and data then tune the hell out of their configs, queries, and indices.\\n\\n Red Bull could sponsor it. I’d buy a T-shirt.\\n\\n \\n \\n kbenson 8 months ago [-]\\n\\n That doesn''t sound that hard to start. Something like RealWorld[1] and the Web Framework Benchmarks[2] combined but for DB workloads. Have one dataset that includes data amenable to OLAP and OLTP, but have separate tests each consisting of OLAP queries, OLTP queries, and combined queries. Choose a low-end, mid-range and high-end set of AWS or GCE instances/configs to normalize against. Let people submit pull requests with new technologies or configs.\\n\\n You''d want to get some funding to run the tests (or maybe solicit Google or Amazon to see if you could get the instance time donated once a month or something.\\n\\n If you started small, with maybe a portion of these features, and then scaled up over time, you might actually get to the point where you had tests that emulated a power failure, or master/slave and dual master scenarios and how they handle certain common network errors (split-brain). That would be an amazing resource.\\n\\n Edit: It occurs to me I probably should have read more of the article, since this is sort of what they are doing already...\\n\\n 1: https://github.com/gothinkster/realworld\\n\\n 2: https://www.techempower.com/benchmarks/\\n\\n \\n \\n etxm 8 months ago [-]\\n\\n Yeah after I posted it I started thinking about what it would take and what that would actually look like... and how you’d cheat :)\\n\\n It would probably require a few different categories with some sort of output assertion to validate the query performed right and a means of tracking CPU, usage ram usage, and execution time.\\n\\n It would be cool to see things like disaster recovery and chaos proofing as well. ');\\n INSERT INTO grafit_article (id, title, text) VALUES (35, 'Applying machine intelligence to GitHub security alerts ', 'Last year, we released security alerts that track security vulnerabilities in Ruby and JavaScript packages. Since then, we’ve identified more than four million of these vulnerabilities and added support for Python. In our launch post, we mentioned that all vulnerabilities with CVE IDs are included in security alerts, but sometimes there are vulnerabilities that are not disclosed in the National Vulnerability Database. Fortunately, our collection of security alerts can be supplemented with vulnerabilities detected from activity within our developer community.\\n Leveraging the community\\n\\n There are many places a project can publicize security fixes within a new version: the CVE feed, various mailing lists and open source groups, or even within its release notes or changelog. Regardless of how projects share this information, some developers within the GitHub community will see the advisory and immediately bump their required versions of the dependency to a known safe version. If detected, we can use the information in these commits to generate security alerts for vulnerabilities which may not have been published in the CVE feed.\\n\\n On an average day, the dependency graph can track around 10,000 commits to dependency files for any of our supported languages. We can’t manually process this many commits. Instead, we depend on machine intelligence to sift through them and extract those that might be related to a security release.\\n\\n For this purpose, we created a machine learning model that scans text associated with public commits (the commit message and linked issues or pull requests) to filter out those related to possible security upgrades. With this smaller batch of commits, the model uses the diff to understand how required version ranges have changed. Then it aggregates across a specific timeframe to get a holistic view of all dependencies that a security release might affect. Finally, the model outputs a list of packages and version ranges it thinks require an alert and currently aren’t covered by any known CVE in our system.\\n Always quality focused\\n\\n No machine learning model is perfect. While machine intelligence can sift through thousands of commits in an instant, this anomaly-detection algorithm will still generate false positives for packages where no security patch was released. Security alert quality is a focus for us, so we review all model output before the community receives an alert.\\n Learn more');\\n INSERT INTO grafit_article (id, title, text) VALUES (29, 'Performance Benchmark 2018', 'I''ve stopped reading database benchmarks, because they are extremely vague. Instead I spend my time optimizing my current solution/stack. For example Postgresql has hundreds of knobs that you can adjust for almost every scenario you can imagine. Sometimes you have a special query and increase the work_mem just for that session. Other cases you adjust the cost settings for another query/session. You can analyze your indexes and index types. And sometimes you need to rewrite parts of a big query.\\n\\n Learning all this takes time, you are much better off learning more about your chosen technology stack than switching to another technology stack.\\n\\n Though in a few rare races, you need a different technology to solve your business problem. In most cases they complement your existing solution, like Elasticsearch/Solr for full-text search or Clickhouse for OLAP workloads.\\n\\n \\n \\n maxxxxx 8 months ago [-]\\n\\n Agreed. Switching to another system is expensive and the benefit is pretty questionable.\\n\\n \\n \\n emsy 8 months ago [-]\\n\\n Unless you hit a very specific use-case/bottleneck, which I only ever witnessed once.\\n\\n \\n \\n TremendousJudge 8 months ago [-]\\n\\n expand, please?\\n\\n \\n \\n maxxxxx 8 months ago [-]\\n\\n I imagine something very specific like having a lot of inserts into a table and that being your main use case. Depending on your data some databases may be better than others and that should be easy to measure.\\n\\n In most real-world cases the requirements however are not very clear and often conflicting so it''s much harder to get data that shows the performance of one system over the other.\\n\\n \\n \\n gopalv 8 months ago [-]\\n\\n > Depending on your data some databases may be better than others and that should be easy to measure.\\n\\n And the performance difference could be an accidental feature of the design and completely unintentional.\\n\\n Postgres for instance has a native data engine, so it can store the exact row-ids for a row into an index, but this means that every update to the row needs all indexes to be updated.\\n\\n Mysql has many data engines (InnoDB and MyISAM to start with), to the row-id is somewhat opaque, so the index stores the primary key which can be pushed to the data engine scans and then have it lookup a row-id internally. This needs an index to be touched for the columns you modify explicitly or if the primary key is updated (which is a usual no-no due to UNIQUE lookup costs).\\n\\n When you have a single wide table with a huge number of indexes, where you update a lot of dimensions frequently, the performance difference between these two solutions is architectural.\\n\\n And if you lookup along an index with few updates, but long running open txns, that is also materially different - one lookup versus two.\\n\\n Though how it came about isn''t really intentional. ');\\n \\\"\\\"\\\"\\n )]\\n\",\n \"<import token>\\n\\n\\nclass Migration(migrations.Migration):\\n <assignment token>\\n <assignment token>\\n\",\n \"<import token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":7598,"cells":{"index":{"kind":"number","value":7598,"string":"7,598"},"blob_id":{"kind":"string","value":"42e16def0fcf234f3d7c2709de36a321d8ddf29e"},"code":{"kind":"string","value":"import collections\nimport re\nfrom collections import Counter\nimport operator\nimport pickle\nimport math\nimport json\n\npath='C:/Users/rahul/Desktop/CSCI 544/HW 2/op_spam_train/'\n#path=sys.argv[1]\n\nbadWordList = ['and','the','was','for']\nRE=r'\\b[^\\W\\d_]+\\b'\n\n# NEGATIVE TWEETS\nc=collections.Counter()\nNT=\"negativeTweets.txt/\"\nwith open(\"negativeTweets.txt\") as f: \n c.update( word.lower() for line in f for word in re.findall(r'\\b[^\\W\\d_]+\\b', line) if len(word)>2 and word not in badWordList)\n\n# POSITIVE TWEETS\nd=collections.Counter()\nPT=\"positiveTweets.txt/\"\nwith open(\"positiveTweets.txt\") as f: \n d.update( word.lower() for line in f for word in re.findall(r'\\b[^\\W\\d_]+\\b', line) if len(word)>2 and word not in badWordList)\n\n# Storing Counts in a dictionary nb\ndicts=[dict(c),dict(d)] \nnb,cnt={},0\nfor d in dicts:\n for k, v in d.items():\n if(k in nb): nb[k][cnt]= nb[k][cnt]+v\n else: \n nb[k]=[1,1]\n nb[k][cnt]= nb[k][cnt]+v\n cnt=cnt+1\n\nfor k,v in nb.items():\n print k,v\n \nprint len(nb);\n\ntotalClassWord=[0,0]\nfor k, v in nb.items():\n totalClassWord=[x + y for x, y in zip(totalClassWord, v)]\n\nprob={} \nfor k, v in nb.items():\n prob[k]=[0,0]\n prob[k][0]= math.log10( float(nb[k][0])/float(totalClassWord[0]))\n prob[k][1]= math.log10( float(nb[k][1])/float(totalClassWord[1]))\n\nfor k,v in prob.items():\n print k,v\n\n#Dumping dictionary as JSON object in file\n#with open('hackTechTweetClassificationModel.txt', 'wb') as handle: pickle.dump(prob, handle)\nkeys=json.dumps(prob, sort_keys=True)\noutput_file=open('hackTechTweetClassificationModel.txt', 'w')\noutput_file.write(keys)\noutput_file.close()\n\noutput_file=open('hackTechTweetPHP.php', 'w')\n#Format of PHP output\n#$result=mysqli_query($con, \"INSERT INTO ttrain VALUES ('aaa','-2.232','222.4234')\" );\nfor k,v in prob.items():\n strop=\"$result=mysqli_query($con, \\\"INSERT INTO ttrain VALUES (\\'\"+str(k)+\"\\',\\'\"+str(v[0])+\"\\',\\'\"+str(v[1])+\"\\')\\\" );\\n\" \n output_file.write(strop)\noutput_file.close()\n"},"steps":{"kind":"list like","value":["import collections\nimport re\nfrom collections import Counter\nimport operator\nimport pickle\nimport math\nimport json\n\npath='C:/Users/rahul/Desktop/CSCI 544/HW 2/op_spam_train/'\n#path=sys.argv[1]\n\nbadWordList = ['and','the','was','for']\nRE=r'\\b[^\\W\\d_]+\\b'\n\n# NEGATIVE TWEETS\nc=collections.Counter()\nNT=\"negativeTweets.txt/\"\nwith open(\"negativeTweets.txt\") as f: \n c.update( word.lower() for line in f for word in re.findall(r'\\b[^\\W\\d_]+\\b', line) if len(word)>2 and word not in badWordList)\n\n# POSITIVE TWEETS\nd=collections.Counter()\nPT=\"positiveTweets.txt/\"\nwith open(\"positiveTweets.txt\") as f: \n d.update( word.lower() for line in f for word in re.findall(r'\\b[^\\W\\d_]+\\b', line) if len(word)>2 and word not in badWordList)\n\n# Storing Counts in a dictionary nb\ndicts=[dict(c),dict(d)] \nnb,cnt={},0\nfor d in dicts:\n for k, v in d.items():\n if(k in nb): nb[k][cnt]= nb[k][cnt]+v\n else: \n nb[k]=[1,1]\n nb[k][cnt]= nb[k][cnt]+v\n cnt=cnt+1\n\nfor k,v in nb.items():\n print k,v\n \nprint len(nb);\n\ntotalClassWord=[0,0]\nfor k, v in nb.items():\n totalClassWord=[x + y for x, y in zip(totalClassWord, v)]\n\nprob={} \nfor k, v in nb.items():\n prob[k]=[0,0]\n prob[k][0]= math.log10( float(nb[k][0])/float(totalClassWord[0]))\n prob[k][1]= math.log10( float(nb[k][1])/float(totalClassWord[1]))\n\nfor k,v in prob.items():\n print k,v\n\n#Dumping dictionary as JSON object in file\n#with open('hackTechTweetClassificationModel.txt', 'wb') as handle: pickle.dump(prob, handle)\nkeys=json.dumps(prob, sort_keys=True)\noutput_file=open('hackTechTweetClassificationModel.txt', 'w')\noutput_file.write(keys)\noutput_file.close()\n\noutput_file=open('hackTechTweetPHP.php', 'w')\n#Format of PHP output\n#$result=mysqli_query($con, \"INSERT INTO ttrain VALUES ('aaa','-2.232','222.4234')\" );\nfor k,v in prob.items():\n strop=\"$result=mysqli_query($con, \\\"INSERT INTO ttrain VALUES (\\'\"+str(k)+\"\\',\\'\"+str(v[0])+\"\\',\\'\"+str(v[1])+\"\\')\\\" );\\n\" \n output_file.write(strop)\noutput_file.close()\n"],"string":"[\n \"import collections\\nimport re\\nfrom collections import Counter\\nimport operator\\nimport pickle\\nimport math\\nimport json\\n\\npath='C:/Users/rahul/Desktop/CSCI 544/HW 2/op_spam_train/'\\n#path=sys.argv[1]\\n\\nbadWordList = ['and','the','was','for']\\nRE=r'\\\\b[^\\\\W\\\\d_]+\\\\b'\\n\\n# NEGATIVE TWEETS\\nc=collections.Counter()\\nNT=\\\"negativeTweets.txt/\\\"\\nwith open(\\\"negativeTweets.txt\\\") as f: \\n c.update( word.lower() for line in f for word in re.findall(r'\\\\b[^\\\\W\\\\d_]+\\\\b', line) if len(word)>2 and word not in badWordList)\\n\\n# POSITIVE TWEETS\\nd=collections.Counter()\\nPT=\\\"positiveTweets.txt/\\\"\\nwith open(\\\"positiveTweets.txt\\\") as f: \\n d.update( word.lower() for line in f for word in re.findall(r'\\\\b[^\\\\W\\\\d_]+\\\\b', line) if len(word)>2 and word not in badWordList)\\n\\n# Storing Counts in a dictionary nb\\ndicts=[dict(c),dict(d)] \\nnb,cnt={},0\\nfor d in dicts:\\n for k, v in d.items():\\n if(k in nb): nb[k][cnt]= nb[k][cnt]+v\\n else: \\n nb[k]=[1,1]\\n nb[k][cnt]= nb[k][cnt]+v\\n cnt=cnt+1\\n\\nfor k,v in nb.items():\\n print k,v\\n \\nprint len(nb);\\n\\ntotalClassWord=[0,0]\\nfor k, v in nb.items():\\n totalClassWord=[x + y for x, y in zip(totalClassWord, v)]\\n\\nprob={} \\nfor k, v in nb.items():\\n prob[k]=[0,0]\\n prob[k][0]= math.log10( float(nb[k][0])/float(totalClassWord[0]))\\n prob[k][1]= math.log10( float(nb[k][1])/float(totalClassWord[1]))\\n\\nfor k,v in prob.items():\\n print k,v\\n\\n#Dumping dictionary as JSON object in file\\n#with open('hackTechTweetClassificationModel.txt', 'wb') as handle: pickle.dump(prob, handle)\\nkeys=json.dumps(prob, sort_keys=True)\\noutput_file=open('hackTechTweetClassificationModel.txt', 'w')\\noutput_file.write(keys)\\noutput_file.close()\\n\\noutput_file=open('hackTechTweetPHP.php', 'w')\\n#Format of PHP output\\n#$result=mysqli_query($con, \\\"INSERT INTO ttrain VALUES ('aaa','-2.232','222.4234')\\\" );\\nfor k,v in prob.items():\\n strop=\\\"$result=mysqli_query($con, \\\\\\\"INSERT INTO ttrain VALUES (\\\\'\\\"+str(k)+\\\"\\\\',\\\\'\\\"+str(v[0])+\\\"\\\\',\\\\'\\\"+str(v[1])+\\\"\\\\')\\\\\\\" );\\\\n\\\" \\n output_file.write(strop)\\noutput_file.close()\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":7599,"cells":{"index":{"kind":"number","value":7599,"string":"7,599"},"blob_id":{"kind":"string","value":"5adb16c654a4e747f803590c42328fa6ba642e95"},"code":{"kind":"string","value":"import os\nfrom subprocess import Popen, PIPE\nfrom Bio import SeqIO\nfrom Bio.Align.Applications import ClustalOmegaCommandline\nfrom Bio import Phylo\nfrom io import StringIO\n# from ete3 import Tree, TreeStyle\nimport pylab\n\n\nclass TreeDrawer:\n\n def __init__(self, sequences=None):\n self.sequences = sequences\n\n def make_alignment(self, method):\n ### Mulltiple Sequence Alignment ###\n path = os.getcwd()\n in_file = \"example.fasta\"\n out_file = \"alignment.aln\"\n\n if os.path.isfile(\"alignment.aln\"):\n os.remove(\"alignment.aln\")\n clustalomega_cline = ClustalOmegaCommandline(infile=in_file, outfile=out_file, verbose=True, iterations=1,\n max_guidetree_iterations=1, max_hmm_iterations=1, dealign=True,\n outfmt=\"clu\")\n\n print(clustalomega_cline)\n stdout, stderr = clustalomega_cline()\n ### Convert to phylip format ###\n SeqIO.convert(\"alignment.aln\", \"clustal\", \"alignment.phy\", \"phylip\")\n ### Phylogentetic analysis ###\n # Choose method proml, dnaml\n # Maximum likelihood analysis #\n # Run Phylip Proml program\n instructions = bytes(\"alignment.phy\\ny\\n\", 'utf-8')\n proml = Popen(\"phylip \" + method, stdin=PIPE, shell=True)\n (out, err) = proml.communicate(instructions)\n # Change output files names\n files = Popen(\"mv outfile \" + method + \".out\", stdin=PIPE, shell=True)\n (out, err) = files.communicate()\n files = Popen(\"mv outtree \" + method + \".tree\", stdin=PIPE, shell=True)\n (out, err) = files.communicate()\n\n def draw_tree(self, filename):\n # instructions = bytes(\"dnaml.tree\\nl\\na\\ny\\n\", 'utf-8')\n # dnaml = Popen(\"phylip drawtree\", stdin=PIPE, shell=True)\n # (out, err) = dnaml.communicate(instructions)\n tree_file = open('dnaml.tree')\n x = tree_file.read()\n # t = Tree()\n # ts = TreeStyle()\n # ts.show_leaf_name = True\n # ts.branch_vertical_margin = 10 # 10 pixels between adjacent branches\n # t.show(tree_style=ts)\n tree = Phylo.read(StringIO(x[:-2]), \"newick\")\n Phylo.draw(tree, do_show=False)\n pylab.savefig('biohackProject/static/images/'+filename+'.png', dpi=300)\n"},"steps":{"kind":"list like","value":["import os\nfrom subprocess import Popen, PIPE\nfrom Bio import SeqIO\nfrom Bio.Align.Applications import ClustalOmegaCommandline\nfrom Bio import Phylo\nfrom io import StringIO\n# from ete3 import Tree, TreeStyle\nimport pylab\n\n\nclass TreeDrawer:\n\n def __init__(self, sequences=None):\n self.sequences = sequences\n\n def make_alignment(self, method):\n ### Mulltiple Sequence Alignment ###\n path = os.getcwd()\n in_file = \"example.fasta\"\n out_file = \"alignment.aln\"\n\n if os.path.isfile(\"alignment.aln\"):\n os.remove(\"alignment.aln\")\n clustalomega_cline = ClustalOmegaCommandline(infile=in_file, outfile=out_file, verbose=True, iterations=1,\n max_guidetree_iterations=1, max_hmm_iterations=1, dealign=True,\n outfmt=\"clu\")\n\n print(clustalomega_cline)\n stdout, stderr = clustalomega_cline()\n ### Convert to phylip format ###\n SeqIO.convert(\"alignment.aln\", \"clustal\", \"alignment.phy\", \"phylip\")\n ### Phylogentetic analysis ###\n # Choose method proml, dnaml\n # Maximum likelihood analysis #\n # Run Phylip Proml program\n instructions = bytes(\"alignment.phy\\ny\\n\", 'utf-8')\n proml = Popen(\"phylip \" + method, stdin=PIPE, shell=True)\n (out, err) = proml.communicate(instructions)\n # Change output files names\n files = Popen(\"mv outfile \" + method + \".out\", stdin=PIPE, shell=True)\n (out, err) = files.communicate()\n files = Popen(\"mv outtree \" + method + \".tree\", stdin=PIPE, shell=True)\n (out, err) = files.communicate()\n\n def draw_tree(self, filename):\n # instructions = bytes(\"dnaml.tree\\nl\\na\\ny\\n\", 'utf-8')\n # dnaml = Popen(\"phylip drawtree\", stdin=PIPE, shell=True)\n # (out, err) = dnaml.communicate(instructions)\n tree_file = open('dnaml.tree')\n x = tree_file.read()\n # t = Tree()\n # ts = TreeStyle()\n # ts.show_leaf_name = True\n # ts.branch_vertical_margin = 10 # 10 pixels between adjacent branches\n # t.show(tree_style=ts)\n tree = Phylo.read(StringIO(x[:-2]), \"newick\")\n Phylo.draw(tree, do_show=False)\n pylab.savefig('biohackProject/static/images/'+filename+'.png', dpi=300)\n","import os\nfrom subprocess import Popen, PIPE\nfrom Bio import SeqIO\nfrom Bio.Align.Applications import ClustalOmegaCommandline\nfrom Bio import Phylo\nfrom io import StringIO\nimport pylab\n\n\nclass TreeDrawer:\n\n def __init__(self, sequences=None):\n self.sequences = sequences\n\n def make_alignment(self, method):\n path = os.getcwd()\n in_file = 'example.fasta'\n out_file = 'alignment.aln'\n if os.path.isfile('alignment.aln'):\n os.remove('alignment.aln')\n clustalomega_cline = ClustalOmegaCommandline(infile=in_file,\n outfile=out_file, verbose=True, iterations=1,\n max_guidetree_iterations=1, max_hmm_iterations=1, dealign=True,\n outfmt='clu')\n print(clustalomega_cline)\n stdout, stderr = clustalomega_cline()\n SeqIO.convert('alignment.aln', 'clustal', 'alignment.phy', 'phylip')\n instructions = bytes('alignment.phy\\ny\\n', 'utf-8')\n proml = Popen('phylip ' + method, stdin=PIPE, shell=True)\n out, err = proml.communicate(instructions)\n files = Popen('mv outfile ' + method + '.out', stdin=PIPE, shell=True)\n out, err = files.communicate()\n files = Popen('mv outtree ' + method + '.tree', stdin=PIPE, shell=True)\n out, err = files.communicate()\n\n def draw_tree(self, filename):\n tree_file = open('dnaml.tree')\n x = tree_file.read()\n tree = Phylo.read(StringIO(x[:-2]), 'newick')\n Phylo.draw(tree, do_show=False)\n pylab.savefig('biohackProject/static/images/' + filename + '.png',\n dpi=300)\n","<import token>\n\n\nclass TreeDrawer:\n\n def __init__(self, sequences=None):\n self.sequences = sequences\n\n def make_alignment(self, method):\n path = os.getcwd()\n in_file = 'example.fasta'\n out_file = 'alignment.aln'\n if os.path.isfile('alignment.aln'):\n os.remove('alignment.aln')\n clustalomega_cline = ClustalOmegaCommandline(infile=in_file,\n outfile=out_file, verbose=True, iterations=1,\n max_guidetree_iterations=1, max_hmm_iterations=1, dealign=True,\n outfmt='clu')\n print(clustalomega_cline)\n stdout, stderr = clustalomega_cline()\n SeqIO.convert('alignment.aln', 'clustal', 'alignment.phy', 'phylip')\n instructions = bytes('alignment.phy\\ny\\n', 'utf-8')\n proml = Popen('phylip ' + method, stdin=PIPE, shell=True)\n out, err = proml.communicate(instructions)\n files = Popen('mv outfile ' + method + '.out', stdin=PIPE, shell=True)\n out, err = files.communicate()\n files = Popen('mv outtree ' + method + '.tree', stdin=PIPE, shell=True)\n out, err = files.communicate()\n\n def draw_tree(self, filename):\n tree_file = open('dnaml.tree')\n x = tree_file.read()\n tree = Phylo.read(StringIO(x[:-2]), 'newick')\n Phylo.draw(tree, do_show=False)\n pylab.savefig('biohackProject/static/images/' + filename + '.png',\n dpi=300)\n","<import token>\n\n\nclass TreeDrawer:\n\n def __init__(self, sequences=None):\n self.sequences = sequences\n <function token>\n\n def draw_tree(self, filename):\n tree_file = open('dnaml.tree')\n x = tree_file.read()\n tree = Phylo.read(StringIO(x[:-2]), 'newick')\n Phylo.draw(tree, do_show=False)\n pylab.savefig('biohackProject/static/images/' + filename + '.png',\n dpi=300)\n","<import token>\n\n\nclass TreeDrawer:\n\n def __init__(self, sequences=None):\n self.sequences = sequences\n <function token>\n <function token>\n","<import token>\n\n\nclass TreeDrawer:\n <function token>\n <function token>\n <function token>\n","<import token>\n<class token>\n"],"string":"[\n \"import os\\nfrom subprocess import Popen, PIPE\\nfrom Bio import SeqIO\\nfrom Bio.Align.Applications import ClustalOmegaCommandline\\nfrom Bio import Phylo\\nfrom io import StringIO\\n# from ete3 import Tree, TreeStyle\\nimport pylab\\n\\n\\nclass TreeDrawer:\\n\\n def __init__(self, sequences=None):\\n self.sequences = sequences\\n\\n def make_alignment(self, method):\\n ### Mulltiple Sequence Alignment ###\\n path = os.getcwd()\\n in_file = \\\"example.fasta\\\"\\n out_file = \\\"alignment.aln\\\"\\n\\n if os.path.isfile(\\\"alignment.aln\\\"):\\n os.remove(\\\"alignment.aln\\\")\\n clustalomega_cline = ClustalOmegaCommandline(infile=in_file, outfile=out_file, verbose=True, iterations=1,\\n max_guidetree_iterations=1, max_hmm_iterations=1, dealign=True,\\n outfmt=\\\"clu\\\")\\n\\n print(clustalomega_cline)\\n stdout, stderr = clustalomega_cline()\\n ### Convert to phylip format ###\\n SeqIO.convert(\\\"alignment.aln\\\", \\\"clustal\\\", \\\"alignment.phy\\\", \\\"phylip\\\")\\n ### Phylogentetic analysis ###\\n # Choose method proml, dnaml\\n # Maximum likelihood analysis #\\n # Run Phylip Proml program\\n instructions = bytes(\\\"alignment.phy\\\\ny\\\\n\\\", 'utf-8')\\n proml = Popen(\\\"phylip \\\" + method, stdin=PIPE, shell=True)\\n (out, err) = proml.communicate(instructions)\\n # Change output files names\\n files = Popen(\\\"mv outfile \\\" + method + \\\".out\\\", stdin=PIPE, shell=True)\\n (out, err) = files.communicate()\\n files = Popen(\\\"mv outtree \\\" + method + \\\".tree\\\", stdin=PIPE, shell=True)\\n (out, err) = files.communicate()\\n\\n def draw_tree(self, filename):\\n # instructions = bytes(\\\"dnaml.tree\\\\nl\\\\na\\\\ny\\\\n\\\", 'utf-8')\\n # dnaml = Popen(\\\"phylip drawtree\\\", stdin=PIPE, shell=True)\\n # (out, err) = dnaml.communicate(instructions)\\n tree_file = open('dnaml.tree')\\n x = tree_file.read()\\n # t = Tree()\\n # ts = TreeStyle()\\n # ts.show_leaf_name = True\\n # ts.branch_vertical_margin = 10 # 10 pixels between adjacent branches\\n # t.show(tree_style=ts)\\n tree = Phylo.read(StringIO(x[:-2]), \\\"newick\\\")\\n Phylo.draw(tree, do_show=False)\\n pylab.savefig('biohackProject/static/images/'+filename+'.png', dpi=300)\\n\",\n \"import os\\nfrom subprocess import Popen, PIPE\\nfrom Bio import SeqIO\\nfrom Bio.Align.Applications import ClustalOmegaCommandline\\nfrom Bio import Phylo\\nfrom io import StringIO\\nimport pylab\\n\\n\\nclass TreeDrawer:\\n\\n def __init__(self, sequences=None):\\n self.sequences = sequences\\n\\n def make_alignment(self, method):\\n path = os.getcwd()\\n in_file = 'example.fasta'\\n out_file = 'alignment.aln'\\n if os.path.isfile('alignment.aln'):\\n os.remove('alignment.aln')\\n clustalomega_cline = ClustalOmegaCommandline(infile=in_file,\\n outfile=out_file, verbose=True, iterations=1,\\n max_guidetree_iterations=1, max_hmm_iterations=1, dealign=True,\\n outfmt='clu')\\n print(clustalomega_cline)\\n stdout, stderr = clustalomega_cline()\\n SeqIO.convert('alignment.aln', 'clustal', 'alignment.phy', 'phylip')\\n instructions = bytes('alignment.phy\\\\ny\\\\n', 'utf-8')\\n proml = Popen('phylip ' + method, stdin=PIPE, shell=True)\\n out, err = proml.communicate(instructions)\\n files = Popen('mv outfile ' + method + '.out', stdin=PIPE, shell=True)\\n out, err = files.communicate()\\n files = Popen('mv outtree ' + method + '.tree', stdin=PIPE, shell=True)\\n out, err = files.communicate()\\n\\n def draw_tree(self, filename):\\n tree_file = open('dnaml.tree')\\n x = tree_file.read()\\n tree = Phylo.read(StringIO(x[:-2]), 'newick')\\n Phylo.draw(tree, do_show=False)\\n pylab.savefig('biohackProject/static/images/' + filename + '.png',\\n dpi=300)\\n\",\n \"<import token>\\n\\n\\nclass TreeDrawer:\\n\\n def __init__(self, sequences=None):\\n self.sequences = sequences\\n\\n def make_alignment(self, method):\\n path = os.getcwd()\\n in_file = 'example.fasta'\\n out_file = 'alignment.aln'\\n if os.path.isfile('alignment.aln'):\\n os.remove('alignment.aln')\\n clustalomega_cline = ClustalOmegaCommandline(infile=in_file,\\n outfile=out_file, verbose=True, iterations=1,\\n max_guidetree_iterations=1, max_hmm_iterations=1, dealign=True,\\n outfmt='clu')\\n print(clustalomega_cline)\\n stdout, stderr = clustalomega_cline()\\n SeqIO.convert('alignment.aln', 'clustal', 'alignment.phy', 'phylip')\\n instructions = bytes('alignment.phy\\\\ny\\\\n', 'utf-8')\\n proml = Popen('phylip ' + method, stdin=PIPE, shell=True)\\n out, err = proml.communicate(instructions)\\n files = Popen('mv outfile ' + method + '.out', stdin=PIPE, shell=True)\\n out, err = files.communicate()\\n files = Popen('mv outtree ' + method + '.tree', stdin=PIPE, shell=True)\\n out, err = files.communicate()\\n\\n def draw_tree(self, filename):\\n tree_file = open('dnaml.tree')\\n x = tree_file.read()\\n tree = Phylo.read(StringIO(x[:-2]), 'newick')\\n Phylo.draw(tree, do_show=False)\\n pylab.savefig('biohackProject/static/images/' + filename + '.png',\\n dpi=300)\\n\",\n \"<import token>\\n\\n\\nclass TreeDrawer:\\n\\n def __init__(self, sequences=None):\\n self.sequences = sequences\\n <function token>\\n\\n def draw_tree(self, filename):\\n tree_file = open('dnaml.tree')\\n x = tree_file.read()\\n tree = Phylo.read(StringIO(x[:-2]), 'newick')\\n Phylo.draw(tree, do_show=False)\\n pylab.savefig('biohackProject/static/images/' + filename + '.png',\\n dpi=300)\\n\",\n \"<import token>\\n\\n\\nclass TreeDrawer:\\n\\n def __init__(self, sequences=None):\\n self.sequences = sequences\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass TreeDrawer:\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":75,"numItemsPerPage":100,"numTotalItems":100000,"offset":7500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NTkzMjUwNSwic3ViIjoiL2RhdGFzZXRzL0NvaGVuUXUvdGhlLXN0YWNrLXYyLWRlZHVwLVB5dGhvbiIsImV4cCI6MTc3NTkzNjEwNSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.rt0AUAt4EbLTc6QuqINObKvSNmDX6cwZeyOS3eei5WUYsa7wGmBUE-TGDjDHIeD7jnMbjYsHJO_560EG2fb-Aw","displayUrls":true,"splitSizeSummaries":[{"config":"0_100000","split":"train","numRows":100000,"numBytesParquet":1075633227},{"config":"100000_200000","split":"train","numRows":100000,"numBytesParquet":773849529},{"config":"1900000_2000000","split":"train","numRows":100000,"numBytesParquet":905561117},{"config":"2100000_2200000","split":"train","numRows":99999,"numBytesParquet":818411254},{"config":"2700000_2800000","split":"train","numRows":100000,"numBytesParquet":1131112800},{"config":"300000_400000","split":"train","numRows":100000,"numBytesParquet":1073321998},{"config":"500000_600000","split":"train","numRows":100000,"numBytesParquet":925113316}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
index int64 0 100k | blob_id stringlengths 40 40 | code stringlengths 7 7.27M | steps listlengths 1 1.25k | error bool 2
classes |
|---|---|---|---|---|
7,500 | f87c036c1eb5026e088bed62fbc330cfd2ea1952 | # coding=utf8
from __future__ import print_function
from application.controllers import *
from application.models import board
def __return__():
return render_template('board/board.html',
lecturers = board.Lecturer.query.all(), disciplines = board.Discipline.query.all())
def __return_modal__(id):
le... | [
"# coding=utf8\nfrom __future__ import print_function\nfrom application.controllers import *\n\nfrom application.models import board\n\ndef __return__():\n return render_template('board/board.html', \n lecturers = board.Lecturer.query.all(), disciplines = board.Discipline.query.all())\n\ndef __return_moda... | false |
7,501 | 3d55a5b4e332523025f65e5f5859f4633f4ee9a3 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Created with YooLiang Technology (侑良科技).
# Author: Qi-Liang Wen (温啓良）
# Web: http://www.yooliang.com/
# Date: 2015/7/12.
from monkey import BasicModel
from monkey import Fields
class WebInformationModel(BasicModel):
class Meta:
label_name = {
"... | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Created with YooLiang Technology (侑良科技).\n# Author: Qi-Liang Wen (温啓良）\n# Web: http://www.yooliang.com/\n# Date: 2015/7/12.\n\nfrom monkey import BasicModel\nfrom monkey import Fields\n\n\nclass WebInformationModel(BasicModel):\n class Meta:\n label_nam... | false |
7,502 | 11d96a8a400afb0861b92d8900e003826614c99a | # Generated by Django 3.1.3 on 2020-11-19 06:19
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('myems', '0004_auto_20201118_1446'),
]
operations = [
migrations.RenameField(
model_name='dg',
old_name='sn',
... | [
"# Generated by Django 3.1.3 on 2020-11-19 06:19\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('myems', '0004_auto_20201118_1446'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='dg',\n old... | false |
7,503 | c6d6fcc242e1b63104a3f3eb788880635257ff4c | api_id = "2168275"
api_hash = "e011a9cb95b7e7e153aa5840985fc883"
| [
"api_id = \"2168275\"\napi_hash = \"e011a9cb95b7e7e153aa5840985fc883\"\n",
"api_id = '2168275'\napi_hash = 'e011a9cb95b7e7e153aa5840985fc883'\n",
"<assignment token>\n"
] | false |
7,504 | bdd9ebfa9a2f14d57efd527ca88032bfb0160a5e | from tkinter import *
import mathcalc as c
root= Tk()
root.title("CALCULATOR")
ent=Entry(root,width=35)
ent.grid(row=0,column=0,columnspan=3,padx=10,pady=10)
#ent.grid(row=0,column=0)
ch=''
num=ent.get()
def clicked(num):
current=ent.get()
ent.delete(0,END)
ent.insert(0,str(current)+str(num))
def click... | [
"from tkinter import *\r\nimport mathcalc as c \r\nroot= Tk()\r\nroot.title(\"CALCULATOR\")\r\nent=Entry(root,width=35)\r\nent.grid(row=0,column=0,columnspan=3,padx=10,pady=10)\r\n#ent.grid(row=0,column=0)\r\nch=''\r\nnum=ent.get()\r\ndef clicked(num):\r\n\tcurrent=ent.get()\r\n\tent.delete(0,END)\r\n\tent.insert(0... | false |
7,505 | 0edca9893d62eea6513543a1d3dd960e9e95d573 | import math
def normal(data,mean,variance):
# print data-mean
return -1*(((data-mean)**2)/(2*variance)) - (0.5 * math.log(2*3.1415*variance))
a = math.log(0.33333) + normal(67.7854,6.0998,13.5408)
b = math.log(0.33333) + normal(67.7854,119.3287,9.4803)
c = math.log(0.33333) + normal(67.7854,65.7801,12.6203)
d =... | [
"import math\n\ndef normal(data,mean,variance):\n\t# print data-mean\n\treturn -1*(((data-mean)**2)/(2*variance)) - (0.5 * math.log(2*3.1415*variance))\n\na = math.log(0.33333) + normal(67.7854,6.0998,13.5408)\nb = math.log(0.33333) + normal(67.7854,119.3287,9.4803)\nc = math.log(0.33333) + normal(67.7854,65.780... | true |
7,506 | 2d3ab575b18144f714f06167f54cd069af4e5895 | num=int(input("enter no"))
def factorial(no):
fact=1
if no <0:
print("-ve no factorial not exist")
else:
for i in range(1,no+1):
fact=fact*i
return fact
print(factorial(num)) | [
"num=int(input(\"enter no\"))\ndef factorial(no):\n fact=1\n if no <0:\n print(\"-ve no factorial not exist\")\n else:\n for i in range(1,no+1):\n fact=fact*i\n return fact\nprint(factorial(num))",
"num = int(input('enter no'))\n\n\ndef factorial(no):\n fact = 1\n if... | false |
7,507 | f7a493ab8e9845d0e9da33a0ee45d7c3ef66deb5 | from django.http import HttpResponse
from django.shortcuts import render
def home(request):
return render(request, 'home.html')
def people(request):
return render(request, 'people.html')
def docs(request):
return render(request, 'docs.html')
def gallery(request, page=None):
if page:
return render(request, 'g... | [
"from django.http import HttpResponse\nfrom django.shortcuts import render \n\ndef home(request):\n\treturn render(request, 'home.html')\n\ndef people(request):\n\treturn render(request, 'people.html')\n\ndef docs(request):\n\treturn render(request, 'docs.html')\n\ndef gallery(request, page=None):\n\tif page:\n\t\t... | false |
7,508 | 5cc325758d5bd99ebe49c40af4d2e339bbf64044 | import time
import click
from contextlib import contextmanager
from pathlib import Path
from model.data import CellsDataset
from model.model import build_model, train_transform, test_transform
from model.vis import plot_cells
@contextmanager
def timer(name):
t0 = time.time()
yield
print("{color}[{name}] d... | [
"import time\nimport click\nfrom contextlib import contextmanager\nfrom pathlib import Path\nfrom model.data import CellsDataset\nfrom model.model import build_model, train_transform, test_transform\nfrom model.vis import plot_cells\n\n\n@contextmanager\ndef timer(name):\n t0 = time.time()\n yield\n print(... | false |
7,509 | aefb49410e077180a660d17c4c646265a75969a7 | offset = input()
cal = 1030 + int(offset) * 100
if 0 < cal < 2400:
print('Tuesday')
elif cal < 0:
print('Monday')
else:
print('Wednesday')
| [
"offset = input()\ncal = 1030 + int(offset) * 100\nif 0 < cal < 2400:\n print('Tuesday')\nelif cal < 0:\n print('Monday')\nelse:\n print('Wednesday')\n",
"<assignment token>\nif 0 < cal < 2400:\n print('Tuesday')\nelif cal < 0:\n print('Monday')\nelse:\n print('Wednesday')\n",
"<assignment tok... | false |
7,510 | 7112eb52aea9be6f8e682b4dacc6b615365c8cea | import numpy as np
from cost_functions import trajectory_cost_fn
import time
class Controller():
def __init__(self):
pass
# Get the appropriate action(s) for this state(s)
def get_action(self, state):
pass
class RandomController(Controller):
def __init__(self, env):
""" YOUR CODE HERE """
... | [
"import numpy as np\r\nfrom cost_functions import trajectory_cost_fn\r\nimport time\r\n\r\nclass Controller():\r\n\tdef __init__(self):\r\n\t\tpass\r\n\r\n\t# Get the appropriate action(s) for this state(s)\r\n\tdef get_action(self, state):\r\n\t\tpass\r\n\r\n\r\nclass RandomController(Controller):\r\n\tdef __init_... | false |
7,511 | 8ec981bf8746e09d3865bc20dcfbf2fbd797c145 | import xl2dict
myxlobject= XlToDict()
myxlobject.convert_sheet_to_dict(file_path="Soul Breaks.xlsx", sheet="First Sheet",
filter_variables_dict={"User Type" : "Admin", "Environment" : "Dev"}) | [
"import xl2dict\n\nmyxlobject= XlToDict()\nmyxlobject.convert_sheet_to_dict(file_path=\"Soul Breaks.xlsx\", sheet=\"First Sheet\",\n filter_variables_dict={\"User Type\" : \"Admin\", \"Environment\" : \"Dev\"})",
"import xl2dict\nmyxlobject = XlToDict()\nmyxlobject.convert_sheet_to... | false |
7,512 | fb787e688da975d37f9fcc39bf5e02957b186982 | # (c) Copyright 2015-2016 Hewlett Packard Enterprise Development LP
# (c) Copyright 2017 SUSE LLC
import json
from bll.plugins import service
import logging
import pecan
import pymysql.cursors
LOG = logging.getLogger(__name__)
class PreferencesSvc(service.SvcBase):
"""
Simple service to manage user preferenc... | [
"# (c) Copyright 2015-2016 Hewlett Packard Enterprise Development LP\n# (c) Copyright 2017 SUSE LLC\nimport json\nfrom bll.plugins import service\nimport logging\nimport pecan\nimport pymysql.cursors\n\nLOG = logging.getLogger(__name__)\n\n\nclass PreferencesSvc(service.SvcBase):\n \"\"\"\n Simple service to ... | false |
7,513 | 6cdaf89d97be8f5ef37ab35f2916a36b4c75ddbe | def pantip(k, n, arr, path,len):
if len == 0:
if sum(path)==k:
path.reverse()
print(path)
return
path.append(arr[len-1])
pantip(k,n,arr,path,len-1)
path.pop()
#backtrack
pantip(k,n,arr,path,len-1)
inp = input('Enter Input (Money, Product) : ').... | [
"def pantip(k, n, arr, path,len):\r\n if len == 0:\r\n if sum(path)==k:\r\n path.reverse()\r\n print(path)\r\n return \r\n path.append(arr[len-1])\r\n pantip(k,n,arr,path,len-1)\r\n path.pop()\r\n #backtrack\r\n pantip(k,n,arr,path,len-1)\r\ninp = input('Enter I... | false |
7,514 | 9f01483aaa744972fae358577e6f093bd491f357 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
CELERY_TIMEZONE = 'Asia/Shanghai'
# CELERY_RESULT_BACKEND='redis://localhost:6379/1'
# BROKER_URL='redis://localhost:6379/2'
BROKER_BACKEND = 'mongodb' # mongodb作为任务队列（或者说是缓存）
<<<<<<< HEAD
BROKER_URL = 'mongodb://10.6.0.149:27017/' ... | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\n\nCELERY_TIMEZONE = 'Asia/Shanghai'\n# CELERY_RESULT_BACKEND='redis://localhost:6379/1'\n# BROKER_URL='redis://localhost:6379/2'\nBROKER_BACKEND = 'mongodb' # mongodb作为任务队列（或者说是缓存）\n<<<<<<< HEAD\nBROKER_URL = 'mongodb://10.6.0... | true |
7,515 | 07d2da14d0122ad2c8407bb13b8567ca62356bef | def build_shift_dict(self, shift):
'''
Creates a dictionary that can be used to apply a cipher to a letter.
The dictionary maps every uppercase and lowercase letter to a
character shifted down the alphabet by the input shift. The dictionary
should have 52 keys of all the uppercase letters and all th... | [
"def build_shift_dict(self, shift):\n '''\n Creates a dictionary that can be used to apply a cipher to a letter.\n The dictionary maps every uppercase and lowercase letter to a\n character shifted down the alphabet by the input shift. The dictionary\n should have 52 keys of all the uppercase letters ... | false |
7,516 | f1c32fe7a29cddf4f881b46f4feab06390a76a44 | # -*- coding: utf-8 -*-
import hashlib
import time
from datetime import datetime, timedelta
# 像访问对象一样, 访问字典
class ObjectLikeDict(dict):
def __getattr__(self, name):
try:
return self[name]
except:
return ''
# 合并字典
def merge_dict(dict1, dict2):
return (lambda a, b: (lam... | [
"# -*- coding: utf-8 -*-\nimport hashlib\nimport time\nfrom datetime import datetime, timedelta\n\n# 像访问对象一样, 访问字典\nclass ObjectLikeDict(dict):\n def __getattr__(self, name):\n try:\n return self[name]\n except:\n return ''\n\n\n# 合并字典\ndef merge_dict(dict1, dict2):\n retu... | true |
7,517 | 7a1a9d2e773fb783d8522f1ea51e753d5d3782e9 | import config
import math
import pygame
import utils
class Rocket:
def __init__(self):
self.x = config.initialPosition['x']*config.game['scale'] + config.game['width']/2;
self.y = config.game['height'] - config.game['floorHeight'] - config.initialPosition['y']*config.game['scale'];
self.angle = config.initial... | [
"import config\nimport math\nimport pygame\nimport utils\n\nclass Rocket:\n\tdef __init__(self):\n\t\tself.x = config.initialPosition['x']*config.game['scale'] + config.game['width']/2;\n\t\tself.y = config.game['height'] - config.game['floorHeight'] - config.initialPosition['y']*config.game['scale'];\n\n\t\tself.... | false |
7,518 | 6a400419c26c62471dfc6893cc2d1ff6d88e49f4 | import whoosh.index as index
from whoosh.fields import *
from whoosh.qparser import MultifieldParser
from whoosh import scoring
w = scoring.BM25F(B=0.75, content_B=1.0, K1=1.5)
fieldnames = ["bill_text", "bill_title", "year", "sponsor_name", "subject"]
boosts = {"bill_text": 1, "bill_title": 2.5, "year": 0, "sponsor_n... | [
"import whoosh.index as index\nfrom whoosh.fields import *\nfrom whoosh.qparser import MultifieldParser\nfrom whoosh import scoring\n\nw = scoring.BM25F(B=0.75, content_B=1.0, K1=1.5)\nfieldnames = [\"bill_text\", \"bill_title\", \"year\", \"sponsor_name\", \"subject\"]\nboosts = {\"bill_text\": 1, \"bill_title\": ... | true |
7,519 | 9033ba0a19d765a83737d59289735a9ffd02abb1 | distance = float(input("Введите начальную дистанцию: "))
target = int(input("Введите целевую дистанцию: "))
day = 1
print("{:>3}-й день: {:.3}".format(day, distance)) # некрасивенько
while target > distance:
day += 1
distance += distance / 10
print("{:>3}-й день: {:.3}".format(day, distance))
print("Ответ: ... | [
"distance = float(input(\"Введите начальную дистанцию: \"))\ntarget = int(input(\"Введите целевую дистанцию: \"))\n\nday = 1\nprint(\"{:>3}-й день: {:.3}\".format(day, distance)) # некрасивенько\n\n\nwhile target > distance:\n day += 1\n distance += distance / 10\n print(\"{:>3}-й день: {:.3}\".format(day, dis... | false |
7,520 | 3bc6091d822fa197dcce3cd75fa9755dc9f93592 | """Scans all files in this project for FIXME and TODO comments and writes them to todos.txt
has to be invoked while being in myLambda/ and not in e.g. myLambda/src"""
import sys
import os
import re
files = []
searchFiles = []
# get all subdirs and its files
for root, dirs, f in os.walk('./'):
files.append((root, f))
... | [
"\"\"\"Scans all files in this project for FIXME and TODO comments and writes them to todos.txt\nhas to be invoked while being in myLambda/ and not in e.g. myLambda/src\"\"\"\nimport sys\nimport os\nimport re\nfiles = []\nsearchFiles = []\n\n# get all subdirs and its files\nfor root, dirs, f in os.walk('./'):\n\tfi... | true |
7,521 | 5a7b68648898818e0db47f225f3d4b0972cd5b99 | _all__ = ["minning_algo"]
| [
"_all__ = [\"minning_algo\"]\n\n",
"_all__ = ['minning_algo']\n",
"<assignment token>\n"
] | false |
7,522 | 1d72a9882aea1e0f808969828ed2e69ecd79ac71 | from typing import Dict, Any
from urllib import request
from django.shortcuts import render, get_object_or_404
from django.urls import reverse
from .models import Product
from cart.forms import CartAddProductForm
from django.shortcuts import render, redirect
from django.contrib.auth import authenticate, login, logout... | [
"from typing import Dict, Any\nfrom urllib import request\n\nfrom django.shortcuts import render, get_object_or_404\nfrom django.urls import reverse\n\nfrom .models import Product\nfrom cart.forms import CartAddProductForm\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth import authenticate,... | false |
7,523 | 40471bfcf05ef45fbb070bbb5bfd4c425fe59b1c | # Differences between Python 2 and Python 3
print "hello world"
# become
print("hello world") # in Pyton 3
raw_input('What is your name?')
# become
input('What is your name?') # in Python 3
# the language of Python
# Reserved words
and
as
assert
break
class
continue
def
del
elif
else
except
finally
for
from
g... | [
"# Differences between Python 2 and Python 3\n\nprint \"hello world\" \n # become \nprint(\"hello world\") # in Pyton 3\n\nraw_input('What is your name?') \n# become\ninput('What is your name?') # in Python 3\n\n\n# the language of Python \n# Reserved words\nand\nas\nassert\nbreak\nclass\ncontinue\ndef\ndel\nelif\... | true |
7,524 | dfcfa4fa036fe8c058d66fc0b9ea73ddb9d4446e | from aiogram import Dispatcher
from create_bot import bot
from data_base import sqlite_db
# new user in group
async def new_member(message):
new_user = message.new_chat_members[0]
user_id = new_user['id']
if new_user['username']:
user_name = new_user['username']
elif new_user['first_name']:
... | [
"from aiogram import Dispatcher\n\nfrom create_bot import bot\nfrom data_base import sqlite_db\n\n\n# new user in group\nasync def new_member(message):\n new_user = message.new_chat_members[0]\n user_id = new_user['id']\n if new_user['username']:\n user_name = new_user['username']\n elif new_user... | false |
7,525 | c35ecad842477fc8501a763f7eb972f6e7fc13e1 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 11:14:13 2019
@author: dobri
"""
import numpy as np
from astropy.stats import circmean
x = np.multiply(np.pi,[(0,1/4,2/4,3/4,4/4),(1,5/4,6/4,7/4,8/4),(5/4,5/4,5/4,5/4,5/4),(0/5,2/5,4/5,6/5,8/5)])
s = np.shape(x)
phikprime = np.array(x*0, dtype... | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 1 11:14:13 2019\n\n@author: dobri\n\"\"\"\n\nimport numpy as np\nfrom astropy.stats import circmean\n\nx = np.multiply(np.pi,[(0,1/4,2/4,3/4,4/4),(1,5/4,6/4,7/4,8/4),(5/4,5/4,5/4,5/4,5/4),(0/5,2/5,4/5,6/5,8/5)])\ns = np.shape(x)\n\nphikpr... | false |
7,526 | dd71feda1ed5ff7ef9dee1573ad63939a3e09691 | # Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from openvino.tools.mo.ops.pack import PackOp
from openvino.tools.mo.front.extractor import FrontExtractorOp
from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
class StackFrontExtractor(FrontExtractorOp):
... | [
"# Copyright (C) 2018-2023 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nfrom openvino.tools.mo.ops.pack import PackOp\nfrom openvino.tools.mo.front.extractor import FrontExtractorOp\nfrom openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs\n\n\nclass StackFrontExtractor(FrontE... | false |
7,527 | 4a3611ecd70d80575f9f68bf45d67532a17b9c93 | import itertools
from typing import Tuple, List, Dict, Optional, Hashable, Collection
class Hypergraph:
"""
Represents a hypergraph, consisting of nodes, directed edges,
hypernodes (each of which is a set of nodes) and hyperedges (directed edges
from hypernodes to hypernodes). Contains functionality to... | [
"import itertools\nfrom typing import Tuple, List, Dict, Optional, Hashable, Collection\n\nclass Hypergraph:\n \"\"\"\n Represents a hypergraph, consisting of nodes, directed edges,\n hypernodes (each of which is a set of nodes) and hyperedges (directed edges\n from hypernodes to hypernodes). Contains f... | false |
7,528 | ffe10ee8b2ebaad565e9aef5047440a067d4e239 | import server.wsgi as flask
import server.grunner as gunicorn
from utils.cfgreader import EnvReader, BoolVar
def use_flask() -> bool:
env_var = BoolVar('USE_FLASK', False)
return EnvReader().safe_read(env_var)
if __name__ == '__main__':
if use_flask(): # dev mode, run the WSGI app in Flask dev server
... | [
"import server.wsgi as flask\nimport server.grunner as gunicorn\nfrom utils.cfgreader import EnvReader, BoolVar\n\n\ndef use_flask() -> bool:\n env_var = BoolVar('USE_FLASK', False)\n return EnvReader().safe_read(env_var)\n\n\nif __name__ == '__main__':\n if use_flask(): # dev mode, run the WSGI app in Fl... | false |
7,529 | 480787d7bc0e87df7c59c4deb402eea76643680c | from unidecode import unidecode
import pdb
import os, manage
import re
from datetime import *
import codecs
import csv
import smtplib
from django.core.urlresolvers import reverse
from django.shortcuts import redirect
from django.contrib.auth.models import User
from django.db.models import Q
from django.contrib import... | [
"from unidecode import unidecode\nimport pdb\nimport os, manage\nimport re\nfrom datetime import *\nimport codecs\nimport csv\nimport smtplib\n\nfrom django.core.urlresolvers import reverse \nfrom django.shortcuts import redirect\nfrom django.contrib.auth.models import User\nfrom django.db.models import Q\nfrom dja... | false |
7,530 | 2236591b3a30f51442beb20c6c43cc9e6cd921d2 | # import sys
# sys.stdin = open("농작물input.txt")
T = int(input())
for n in range(1, T+1):
N = int(input())
arr = [list(map(int, list(input()))) for _ in range(N)]
# print(arr)
a = N//2
b = N//2
result = 0
for i in range(N):
for j in range(a, b+1):
result += arr[i][j]
... | [
"# import sys\n# sys.stdin = open(\"농작물input.txt\")\n\nT = int(input())\n\nfor n in range(1, T+1):\n N = int(input())\n arr = [list(map(int, list(input()))) for _ in range(N)]\n # print(arr)\n a = N//2\n b = N//2\n result = 0\n for i in range(N):\n for j in range(a, b+1):\n re... | false |
7,531 | 2023e0b749338488e63cbbb475b7a915bccccce0 | from subprocess import check_output
import json
import datetime
date = datetime.datetime.now()
mo = date.month
day = date.day
year = date.year
str = '{0}-{1}-{2}'.format(mo, day, year)
instances = json.loads(check_output("aws lightsail get-instances", shell=True))
inst_names = []
inst_dict = {}
for instance in instan... | [
"from subprocess import check_output\nimport json\nimport datetime\n\ndate = datetime.datetime.now()\nmo = date.month\nday = date.day\nyear = date.year\nstr = '{0}-{1}-{2}'.format(mo, day, year)\ninstances = json.loads(check_output(\"aws lightsail get-instances\", shell=True))\n\ninst_names = []\ninst_dict = {}\nfo... | false |
7,532 | 290811317ddb49a7d2a9f44ab7e0b6d201db12e1 | from recipes.almahelpers import fixsyscaltimes # SACM/JAO - Fixes
__rethrow_casa_exceptions = True
context = h_init()
context.set_state('ProjectSummary', 'proposal_code', '2017.1.01355.L')
context.set_state('ProjectSummary', 'piname', 'unknown')
context.set_state('ProjectSummary', 'proposal_title', 'unknown')
context.s... | [
"from recipes.almahelpers import fixsyscaltimes # SACM/JAO - Fixes\n__rethrow_casa_exceptions = True\ncontext = h_init()\ncontext.set_state('ProjectSummary', 'proposal_code', '2017.1.01355.L')\ncontext.set_state('ProjectSummary', 'piname', 'unknown')\ncontext.set_state('ProjectSummary', 'proposal_title', 'unknown')... | false |
7,533 | 3185b6b1902099caed66ce6f97cd1b9940261fc1 | import torch.nn as nn
from layers import maskAConv, MaskBConvBlock
class PixelCNN(nn.Module):
def __init__(self, n_channel=3, h=128, discrete_channel=256):
"""PixelCNN Model"""
super(PixelCNN, self).__init__()
self.discrete_channel = discrete_channel
self.MaskAConv = maskAConv(n_... | [
"import torch.nn as nn\nfrom layers import maskAConv, MaskBConvBlock\n\n\nclass PixelCNN(nn.Module):\n def __init__(self, n_channel=3, h=128, discrete_channel=256):\n \"\"\"PixelCNN Model\"\"\"\n super(PixelCNN, self).__init__()\n\n self.discrete_channel = discrete_channel\n\n self.Ma... | false |
7,534 | 04097e63de5cd94ca8921be5cb6c2155c1e7bc20 | import pathlib
import sys
import yaml
from google.protobuf.json_format import ParseError
sys.path = [p for p in sys.path if not p.endswith('bazel_tools')]
from tools.config_validation.validate_fragment import validate_fragment
def main():
errors = []
for arg in sys.argv[1:]:
try:
valid... | [
"import pathlib\nimport sys\n\nimport yaml\n\nfrom google.protobuf.json_format import ParseError\n\nsys.path = [p for p in sys.path if not p.endswith('bazel_tools')]\n\nfrom tools.config_validation.validate_fragment import validate_fragment\n\n\ndef main():\n errors = []\n for arg in sys.argv[1:]:\n tr... | false |
7,535 | ad622ff2e1d9286246b2175694a9ae796f8d2557 | ''' This module creates the models/tables in the database
catalog using sqlalchemy '''
from catalog import db
class Items(db.Model):
''' Model to store all the information about an item '''
id = db.Column(db.Integer, primary_key=True)
email = db.Column(db.String)
item = db.Column(db.... | [
"''' This module creates the models/tables in the database \r\n catalog using sqlalchemy '''\r\n\r\nfrom catalog import db\r\n\r\n\r\nclass Items(db.Model):\r\n ''' Model to store all the information about an item '''\r\n\r\n id = db.Column(db.Integer, primary_key=True)\r\n email = db.Column(db.String)\... | false |
7,536 | 608c116cd42132bd63be5056f0aaf5c78933886e | # SPDX-FileCopyrightText: 2021 John Park for Adafruit Industries
# SPDX-License-Identifier: MIT
import time
import random
import board
import audiomp3
import audiopwmio
from adafruit_crickit import crickit
ss = crickit.seesaw # Crickit seesaw setup
button = crickit.SIGNAL1 # momentary switch to trigger animation
ss... | [
"# SPDX-FileCopyrightText: 2021 John Park for Adafruit Industries\n# SPDX-License-Identifier: MIT\nimport time\nimport random\nimport board\nimport audiomp3\nimport audiopwmio\nfrom adafruit_crickit import crickit\n\nss = crickit.seesaw # Crickit seesaw setup\n\nbutton = crickit.SIGNAL1 # momentary switch to trig... | false |
7,537 | 8adf8cfc72d5af955bf7509d3573a9bcc7c0845e | import inspect
import re
import openquake.hazardlib.source as oqsrc
# List of valid attributes for an area source
AREAS_ATTRIBUTES = set(['source_id',
'name',
'tectonic_region_type',
'mfd',
'rupture_mesh_spacing',
'magnitude_scaling_relationship',
... | [
"import inspect\nimport re\nimport openquake.hazardlib.source as oqsrc\n\n# List of valid attributes for an area source\nAREAS_ATTRIBUTES = set(['source_id', \n\t\t\t'name', \n\t\t\t'tectonic_region_type', \n\t\t\t'mfd',\n 'rupture_mesh_spacing',\n 'magnitude_scaling_re... | false |
7,538 | 6f271e6cfb03977d52c50562c3c394b962c9af83 | # vim:sw=4 ts=4 et:
# Copyright (c) 2015 Torchbox Ltd.
# tomasz.knapik@torchbox.com 2017-12-07
#
# Permission is granted to anyone to use this software for any purpose,
# including commercial applications, and to alter it and redistribute it
# freely. This software is provided 'as-is', without any express or implied
# ... | [
"# vim:sw=4 ts=4 et:\n# Copyright (c) 2015 Torchbox Ltd.\n# tomasz.knapik@torchbox.com 2017-12-07\n#\n# Permission is granted to anyone to use this software for any purpose,\n# including commercial applications, and to alter it and redistribute it\n# freely. This software is provided 'as-is', without any express or... | false |
7,539 | 1e02d584cde0cdf251aa36abd27b683219ef87ed | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 10 01:03:35 2020
@author: Jordan
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import date
## from COVID19_Simple import *
from COVID19_Diff import calc_diff_country
### Dash Stuff ###
import dash
import dash_core... | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Apr 10 01:03:35 2020\r\n\r\n@author: Jordan\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom datetime import date\r\n## from COVID19_Simple import *\r\nfrom COVID19_Diff import calc_diff_country\r\n### Dash Stu... | false |
7,540 | d57b91bf41f031e3362dabdef8c67a0da04fe577 | from ROOT import *
gSystem.Load("libAnalysis")
import sys
import argparse
parser = argparse.ArgumentParser(description="Python script to process and merge showers.")
group = parser.add_mutually_exclusive_group()
group.add_argument("-v", "--verbose", help="Turn on verbose output",
action="store_tru... | [
"from ROOT import *\ngSystem.Load(\"libAnalysis\")\nimport sys\n\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"Python script to process and merge showers.\")\ngroup = parser.add_mutually_exclusive_group()\ngroup.add_argument(\"-v\", \"--verbose\", help=\"Turn on verbose output\",\n ... | true |
7,541 | acb879cb72e5b3ac897a271dc680e4ca763d2122 | from django.db import models
class Professor(models.Model):
nome = models.CharField(max_length=100)
apelido = models.CharField(max_length=30)
descricao = models.TextField(max_length=1000)
def __str__(self):
return self.nome
class ImagemProfessor(models.Model):
professor = models.Forei... | [
"from django.db import models\n\nclass Professor(models.Model):\n nome = models.CharField(max_length=100)\n apelido = models.CharField(max_length=30)\n descricao = models.TextField(max_length=1000)\n\n def __str__(self):\n return self.nome\n \nclass ImagemProfessor(models.Model):\n professo... | false |
7,542 | 5fb3905abf958f0a8be41cd6ad07efb2a0cf6c66 | import sys, os
def resource_path(relative_path):
""" Get absolute path to resource, works for dev and for PyInstaller """
try:
# PyInstaller creates a temp folder and stores path in _MEIPASS
base_path = sys._MEIPASS
except Exception:
base_path = os.path.abspath(".")
return os.p... | [
"import sys, os\n\ndef resource_path(relative_path):\n \"\"\" Get absolute path to resource, works for dev and for PyInstaller \"\"\"\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\"... | false |
7,543 | e7c18fa99c801fd959c868954f020d8c55babe0d |
def long_alpha(str1):
list1 = []
list2 = ""
maxi = 0
j = 0
for i in range(len(str1)):
if i == 0:
list2 += str1[i]
elif ord(str1[i - 1]) <= ord(str1[i]):
list2 += str1[i]
else:
list1.append(list2)
list2 = ""
... | [
"\r\ndef long_alpha(str1):\r\n list1 = []\r\n list2 = \"\"\r\n maxi = 0\r\n j = 0\r\n for i in range(len(str1)):\r\n if i == 0:\r\n list2 += str1[i]\r\n elif ord(str1[i - 1]) <= ord(str1[i]):\r\n list2 += str1[i]\r\n else:\r\n list1.append(list2)\... | false |
7,544 | ad9facb9c8e552845df9171549f886f3e9cba193 | # PROBLEM: Code organized in package and want to import a submodule from one o the other pkg
# submodules without hardcoding the package name into the import statement
# SOLUTION: Use pkg-relative import
# Absolete path
from mypackage.A import grok
print(dir(grok))
grok.testA() | [
"# PROBLEM: Code organized in package and want to import a submodule from one o the other pkg\n# submodules without hardcoding the package name into the import statement\n# SOLUTION: Use pkg-relative import\n\n# Absolete path\nfrom mypackage.A import grok\n\nprint(dir(grok))\ngrok.testA()",
"from mypac... | false |
7,545 | 7a6d45ef87d93af9a15bd352b893164d3a36c399 | import sys
import os
import traceback
from src.properties import *
from src.utils import *
from subprocess import call
from src.entity.cursor import Cursor
from curses import *
def main(screen, file_path):
setUpEnv()
text = readFileIfExist(file_path)
while 1:
try:
text = startEditing(s... | [
"import sys\nimport os\nimport traceback\nfrom src.properties import *\nfrom src.utils import *\nfrom subprocess import call\nfrom src.entity.cursor import Cursor\nfrom curses import *\n\n\ndef main(screen, file_path):\n setUpEnv()\n text = readFileIfExist(file_path)\n while 1:\n try:\n t... | false |
7,546 | 1808be09c2730af5829bb0c7c0c7cfe9f80fe84c | list = input().split()
n = int(list[0])
k = int(list[1])
list.clear()
for i in range(0, n):
list.append("")
tmp = input().split()
list[i] = tmp[0] + list[int(tmp[1])-1]
for i in range(0, k):
start = input()
print(len([word for word in list if word.startswith(start)])) | [
"list = input().split()\nn = int(list[0])\nk = int(list[1])\nlist.clear()\nfor i in range(0, n):\n list.append(\"\")\n tmp = input().split()\n list[i] = tmp[0] + list[int(tmp[1])-1]\nfor i in range(0, k):\n start = input()\n print(len([word for word in list if word.startswith(start)]))",
"list = in... | false |
7,547 | c3fae13b488a717419adb8292597746a383b332c | class boxCar:
def __init__(self, *args, **kwargs):
print("print the keyword arguments dictionary {0} by {1}".format(kwargs, "WANGH"))
self.name = kwargs["name"]
self.domains = ["BODY","PWT","INFO","ADAS","INF"]
self.configuration = {}
def addEcu(self, ecu, domain):
i... | [
"class boxCar:\n def __init__(self, *args, **kwargs):\n print(\"print the keyword arguments dictionary {0} by {1}\".format(kwargs, \"WANGH\"))\n self.name = kwargs[\"name\"]\n self.domains = [\"BODY\",\"PWT\",\"INFO\",\"ADAS\",\"INF\"]\n self.configuration = {}\n \n def addEcu(s... | false |
7,548 | 0af45914c8c111a42b0b9684f5f0ee19ef5eeb70 | import math
import random
import time
import numpy as np
class NeuralNetwork:
digits = [
[
1,1,1,1,1,
1,0,0,0,1,
1,0,0,0,1,
1,0,0,0,1,
1,1,1,1,1
],
[
0,0,1,0,0,
0,0,1,0,0,
0,0,1,0,0,
... | [
"import math\nimport random\nimport time\nimport numpy as np\n\nclass NeuralNetwork:\n\n digits = [\n [\n 1,1,1,1,1,\n 1,0,0,0,1,\n 1,0,0,0,1,\n 1,0,0,0,1,\n 1,1,1,1,1\n ],\n [\n 0,0,1,0,0,\n 0,0,1,0,0,\n ... | false |
7,549 | e8a024796b6426e572571e46030678e90c537229 | from django import forms
from django.forms import widgets
# from product.models import PRODUCT_OTHER_CHOICE, PRODUCT_CATEGORY_CHOICES
PRODUCT_OTHER_CHOICE = 'other'
PRODUCT_CATEGORY_CHOICES = (
(PRODUCT_OTHER_CHOICE, 'Разное'),
('food', 'Еда'),
('drink', 'Вода'),
('cloth', 'Одежда'),
('electronics'... | [
"from django import forms\nfrom django.forms import widgets\n# from product.models import PRODUCT_OTHER_CHOICE, PRODUCT_CATEGORY_CHOICES\n\nPRODUCT_OTHER_CHOICE = 'other'\nPRODUCT_CATEGORY_CHOICES = (\n (PRODUCT_OTHER_CHOICE, 'Разное'),\n ('food', 'Еда'),\n ('drink', 'Вода'),\n ('cloth', 'Одежда'),\n ... | false |
7,550 | 826abb18b11afd7a010e2bfc5a29ba068218c23a | from django.contrib.auth.mixins import LoginRequiredMixin
from django.views.generic import View
from django.http import HttpResponse
from django.utils.decorators import method_decorator
from django.contrib.auth.decorators import login_required
from django.core.exceptions import PermissionDenied
class View1(LoginRequir... | [
"from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.views.generic import View\nfrom django.http import HttpResponse\nfrom django.utils.decorators import method_decorator\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.exceptions import PermissionDenied\n\nclass View1... | false |
7,551 | 34ad2e6fc7167766dac1ca962cab40511c89ad68 | import zipfile
zzz = zipfile.ZipFile('channel.zip','r')
filestr = '90052'
comment = []
for i in range(1000):
fname = filestr + ".txt"
for j in zzz.infolist():
if j.filename == fname :
print j.comment
comment.append(j.comment)
break
inzzz = zzz.open(fname).read()
print 'fname = ' + fname
... | [
"import zipfile\n\nzzz = zipfile.ZipFile('channel.zip','r')\nfilestr = '90052'\ncomment = []\nfor i in range(1000):\n fname = filestr + \".txt\"\n for j in zzz.infolist():\n if j.filename == fname :\n print j.comment\n comment.append(j.comment)\n break\n inzzz = zzz.open(fname).read()\n print ... | true |
7,552 | 0f2882971f08450e970e188ed2a06ae1683c682c | import argparse
import logging
import enum
from abc import ABCMeta, abstractmethod
from nmigen import *
from ....gateware.pads import *
from ....gateware.i2c import I2CTarget
from ... import *
class Event(enum.IntEnum):
START = 0x10
STOP = 0x20
RESTART = 0x30
WRITE = 0x40
READ = 0x50
... | [
"import argparse\nimport logging\nimport enum\nfrom abc import ABCMeta, abstractmethod\nfrom nmigen import *\n\nfrom ....gateware.pads import *\nfrom ....gateware.i2c import I2CTarget\nfrom ... import *\n\n\nclass Event(enum.IntEnum):\n START = 0x10\n STOP = 0x20\n RESTART = 0x30\n WRITE = 0x40\n... | false |
7,553 | 4afc2ceed860c20af071e1d9ccaca17973cb9a8e | # Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def swapPairs(self, head: ListNode) -> ListNode:
dummy_head=ListNode(0)
dummy_head.next=head
pre=dummy_head
cur=head
... | [
"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def swapPairs(self, head: ListNode) -> ListNode:\n dummy_head=ListNode(0)\n dummy_head.next=head\n pre=dummy_head\n ... | false |
7,554 | 927470fe0087b17e5fe67a9b8b3cc13a40d8be1a | import BlockDeviceHandler
import json
import LocalMachine
import os
""" This module automaticly format the disk based on diskconf.json """
def module_print(text):
print_text = "[ autoformat disk ] " + str(text)
print(print_text)
def parse_config_file_from_disk(path, confname="diskconf.json"):
json_path =... | [
"import BlockDeviceHandler\nimport json\nimport LocalMachine\nimport os\n\n\"\"\" This module automaticly format the disk based on diskconf.json \"\"\"\n\ndef module_print(text):\n print_text = \"[ autoformat disk ] \" + str(text)\n print(print_text)\n\ndef parse_config_file_from_disk(path, confname=\"diskcon... | false |
7,555 | cab233976653b8135276ff849955f32766833354 | import os
import numpy as np
import warnings
import soundfile as sf
def load_path():
path = os.path.join(os.path.dirname(__file__))
if path == "":
path = "."
return path
def create_folder(directory):
try:
if not os.path.exists(directory):
os.makedirs(directory)
except... | [
"import os\nimport numpy as np\nimport warnings\nimport soundfile as sf\n\n\ndef load_path():\n path = os.path.join(os.path.dirname(__file__))\n if path == \"\":\n path = \".\"\n return path\n\n\ndef create_folder(directory):\n try:\n if not os.path.exists(directory):\n os.maked... | false |
7,556 | 1f114b4716a44f5370495297511c305ecbb680c3 | import os, copy
from a import Moon, updateOneMoon, updateAllMoons
file_path = os.path.dirname(os.path.realpath(__file__))
input_path = file_path + "/b.in.txt"
inpt = open(input_path, 'r')
moons = []
for line in inpt:
new_moon = Moon(line)
moons.append(new_moon)
initial_moon_position = copy.deepcopy(moons)... | [
"import os, copy\nfrom a import Moon, updateOneMoon, updateAllMoons\n\nfile_path = os.path.dirname(os.path.realpath(__file__))\n\ninput_path = file_path + \"/b.in.txt\"\n\ninpt = open(input_path, 'r')\n\nmoons = []\n\nfor line in inpt:\n new_moon = Moon(line)\n moons.append(new_moon)\n\ninitial_moon_position ... | true |
7,557 | 9065842a8e90c833278547310f027bc63c7a9a47 | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import copy
import sys
def mutate(genotype_in, mut_matrix):
genotype_out = np.zeros(8)
for i in range(8):
rand_vec = np.random.choice(8, size=int(genotype_in[i]), p=mut_matrix[i,:])
genotype_out+=np.bincount(rand_vec, minlength=8)
r... | [
"#!/usr/bin/env python\n# coding: utf-8\n\nimport numpy as np\nimport copy\nimport sys\n\n\ndef mutate(genotype_in, mut_matrix):\n genotype_out = np.zeros(8)\n for i in range(8):\n rand_vec = np.random.choice(8, size=int(genotype_in[i]), p=mut_matrix[i,:])\n genotype_out+=np.bincount(rand_vec, m... | false |
7,558 | e048170775c589cf0a9fb3d54c72dab4df3f1bcb | import pickle
import numpy as np
in_dir = "C:\\Users\\ganga\\Github\\Generative-Models\\Project\\Data\\Dynamics\\"
out_dir = f"C:\\Users\\ganga\\Github\\Generative-Models\\Project\\Data\\Dynamics\\"
# Read frames
train_frames = pickle.load( open(in_dir +'\\train_frames.pkl' , 'rb' ))
test_frames = pickle.load( open(... | [
"import pickle\nimport numpy as np\n\nin_dir = \"C:\\\\Users\\\\ganga\\\\Github\\\\Generative-Models\\\\Project\\\\Data\\\\Dynamics\\\\\"\nout_dir = f\"C:\\\\Users\\\\ganga\\\\Github\\\\Generative-Models\\\\Project\\\\Data\\\\Dynamics\\\\\"\n\n\n# Read frames\ntrain_frames = pickle.load( open(in_dir +'\\\\train_fra... | false |
7,559 | d6046217308745b85455aed78734700b9622782c | import os
from logzero import logger as log
from extract import data_generator
from transform import create_dataframe
def bigquery(
datafile,
dataset=os.environ["BQDATASET"],
project=os.environ["GCPPROJECT"],
schema=[
{"name": "conversation", "type": "STRING"},
{"name": "id", "type": ... | [
"import os\nfrom logzero import logger as log\nfrom extract import data_generator\n\nfrom transform import create_dataframe\n\n\ndef bigquery(\n datafile,\n dataset=os.environ[\"BQDATASET\"],\n project=os.environ[\"GCPPROJECT\"],\n schema=[\n {\"name\": \"conversation\", \"type\": \"STRING\"},\n ... | false |
7,560 | a9ce341ffe26ab6c476237030e23e6ae57b8fa33 | from random import randint
#given a list of names, cities and neigborhoods, generate a client table.
#------------------------MODEL------------------------------
#([cliente_id], [nome], [sexo], [telefone], [cpf], [cidade_nome], [cidade_bairro_nome], [cidade_bairro_cep])
class Employee:
def __init__(self, id, name ,s... | [
"from random import randint\n\n#given a list of names, cities and neigborhoods, generate a client table.\n\n#------------------------MODEL------------------------------\n#([cliente_id], [nome], [sexo], [telefone], [cpf], [cidade_nome], [cidade_bairro_nome], [cidade_bairro_cep])\nclass Employee:\n\tdef __init__(self... | true |
7,561 | 0131657a7675904ee2743448f514a9f11e0dc0ad | # -*- coding: utf-8 -*-
"""
Default organizer for bioinfoinformatics project directiories - RNA-Seq based model
"""
import os
import sys
#main path
curr_path = os.getcwd()
print("\nYour current directory is: " + curr_path + "\n\nIt contains the following files and directories:\n\n" + str(os.listdir("."))) # displays... | [
"# -*- coding: utf-8 -*-\n\"\"\"\nDefault organizer for bioinfoinformatics project directiories - RNA-Seq based model\n\"\"\"\nimport os \nimport sys\n\n#main path\n\ncurr_path = os.getcwd()\nprint(\"\\nYour current directory is: \" + curr_path + \"\\n\\nIt contains the following files and directories:\\n\\n\" + st... | false |
7,562 | c5e7fdcbd4a9281597a35a180f2853caac68f811 | # Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
from collections import namedtuple
from functools import partial
import inspect
from itertools import product
import math
import os
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
import pytest
import ... | [
"# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom collections import namedtuple\nfrom functools import partial\nimport inspect\nfrom itertools import product\nimport math\nimport os\n\nimport numpy as np\nfrom numpy.testing import assert_allclose, assert_array_equal\nimpo... | false |
7,563 | d77036ed07231719358658a42dc14d20453bd792 | def pattern4(n):
"""
n: length of the base of the triangle ie. the max number
of starts it will contain.
"""
for row in range(1, n+1):
for col in range(1, row+1):
print("*", end="")
print("")
if __name__ == '__main__':
n = int(input(("Enter height of the triangle: ")))
pattern4(n)
| [
"def pattern4(n):\n\t\"\"\"\n\tn: length of the base of the triangle ie. the max number\n\t\tof starts it will contain.\n\t\"\"\"\n\tfor row in range(1, n+1):\n\t\tfor col in range(1, row+1):\n\t\t\tprint(\"*\", end=\"\")\n\t\tprint(\"\")\nif __name__ == '__main__':\n\tn = int(input((\"Enter height of the triangle:... | false |
7,564 | 511016b9cd54f6824360d609ede233b9cc3e4447 | class Mood(object):
GENERIC = 1
HIGH_TEMP = 2
LOW_TEMP = 3
HIGH_DUST = 4
LOW_DUST = 5
def decision(self, data):
temp = float(data)
if temp <= 10:
return self.LOW_TEMP
if temp > 30:
return self.HIGH_TEMP
if (10 < temp <=30):
... | [
"class Mood(object):\n\n GENERIC = 1\n HIGH_TEMP = 2\n LOW_TEMP = 3\n HIGH_DUST = 4\n LOW_DUST = 5\n\n def decision(self, data):\n temp = float(data)\n\n if temp <= 10:\n return self.LOW_TEMP\n\n if temp > 30:\n return self.HIGH_TEMP\n\n if (10... | false |
7,565 | 9ad92b23b8a02204a86af599e507eb889e5bcec7 | #!/usr/bin/env python
import rospy
from geometry_msgs.msg import PoseStamped
from styx_msgs.msg import Lane, Waypoint
from scipy.spatial import KDTree
import numpy as np
from std_msgs.msg import Int32
import math
'''
This node will publish waypoints from the car's current position to some `x` distance ahead.
As men... | [
"#!/usr/bin/env python\n\nimport rospy\nfrom geometry_msgs.msg import PoseStamped\nfrom styx_msgs.msg import Lane, Waypoint\nfrom scipy.spatial import KDTree\nimport numpy as np\nfrom std_msgs.msg import Int32\n\nimport math\n\n'''\nThis node will publish waypoints from the car's current position to some `x` distan... | false |
7,566 | 1896f4d5b304915d5cbbb30b0a83854c4a8cc60c | from wtforms import Form, StringField
class SearchForm(Form):
criteria = StringField("Texto a buscar")
| [
"from wtforms import Form, StringField\n\nclass SearchForm(Form):\n\tcriteria = StringField(\"Texto a buscar\")\n\t\n",
"from wtforms import Form, StringField\n\n\nclass SearchForm(Form):\n criteria = StringField('Texto a buscar')\n",
"<import token>\n\n\nclass SearchForm(Form):\n criteria = StringField('... | false |
7,567 | 42a717591fb8fe480581d8996e9811d0292d0eb1 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from cv2 import DualTVL1OpticalFlow_create as DualTVL1
from tensorflow.python.platform import flags
import os
import sys
sys.path.insert(0, '..')
from utils import image_funcs
import numpy as np
def make_di... | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom cv2 import DualTVL1OpticalFlow_create as DualTVL1\nfrom tensorflow.python.platform import flags\nimport os\nimport sys\n\nsys.path.insert(0, '..')\nfrom utils import image_funcs\n\nimport numpy as... | false |
7,568 | c7553cadb49c9c7e80a7800b9bff4d5f64796494 | import pytest
from a3 import *
from test_utils import *
from numpy import allclose
def test_problem_7_1_8():
assert(check_linalg())
assert(abs(problem_7_1_8(5000)-84.8)<1)
| [
"import pytest\nfrom a3 import *\nfrom test_utils import *\nfrom numpy import allclose\n\ndef test_problem_7_1_8():\n assert(check_linalg())\n assert(abs(problem_7_1_8(5000)-84.8)<1)\n",
"import pytest\nfrom a3 import *\nfrom test_utils import *\nfrom numpy import allclose\n\n\ndef test_problem_7_1_8():\n ... | false |
7,569 | 7ff19ee35422395f78dca1e17a736df20a40ea98 | import os
import sqlite3 as db
os.system('clear')
persons = []
class Person:
def __init__(self, name, surname, job, salary):
self.name = name
self.surname = surname
self.job = job
self.salary = salary
def create(name):
conn = db.connect(name + '.db')
c = conn.cursor()
c.execute("""CREATE TABLE first(
... | [
"import os\nimport sqlite3 as db\n\nos.system('clear')\npersons = []\n\nclass Person:\n\tdef __init__(self, name, surname, job, salary):\n\t\tself.name = name\n\t\tself.surname = surname\n\t\tself.job = job\n\t\tself.salary = salary\n\ndef create(name):\n\tconn = db.connect(name + '.db')\n\tc = conn.cursor()\n\n\tc... | false |
7,570 | 0dec0f04cfe891eea74ef45484fa7433e3429dcd | import os
import glob
ONE_KB = 1024
def get_files(dirname, size_in_kb):
"""Return files in dirname that are >= size_in_kb"""
return (
filename
for _, _, files in os.walk(dirname)
for filename in files
if int(filename) >= size_in_kb * ONE_KB
)
# Pybites solution
def get_f... | [
"import os\nimport glob\n\nONE_KB = 1024\n\n\ndef get_files(dirname, size_in_kb):\n \"\"\"Return files in dirname that are >= size_in_kb\"\"\"\n return (\n filename\n for _, _, files in os.walk(dirname)\n for filename in files\n if int(filename) >= size_in_kb * ONE_KB\n )\n\n\n#... | false |
7,571 | d567dfe29380a34534308446a9c8940cede84083 | def func_sum_even(n):
e_digit1=n%10
n//=10
e_digit2=n%10
e_digit3=n//10
sum_even=e_digit1*(1-e_digit1%2)+e_digit2*(1-e_digit2%2)+e_digit3*(1-e_digit3%2)
return sum_even
# n=int(input())
# print(func_sum_even(n)) | [
"def func_sum_even(n):\n e_digit1=n%10\n n//=10\n e_digit2=n%10\n e_digit3=n//10\n sum_even=e_digit1*(1-e_digit1%2)+e_digit2*(1-e_digit2%2)+e_digit3*(1-e_digit3%2)\n return sum_even\n# n=int(input())\n# print(func_sum_even(n)) ",
"def func_sum_even(n):\n e_digit1 = n % 10\n n //= 10\n ... | false |
7,572 | e6b3def6ed6f2523d88912832a876caf2742b786 | import argparse
import pickle
import pandas as pd
from pyspark.sql.session import SparkSession
parser = argparse.ArgumentParser()
parser.add_argument('--rs', type=str, nargs='+')
args = parser.parse_args()
ss = SparkSession.builder.getOrCreate()
post_df = None
for f in args.rs:
df = ss.read.json(f).select('id', ... | [
"import argparse\nimport pickle\n\nimport pandas as pd\nfrom pyspark.sql.session import SparkSession\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--rs', type=str, nargs='+')\nargs = parser.parse_args()\n\nss = SparkSession.builder.getOrCreate()\npost_df = None\nfor f in args.rs:\n df = ss.read.jso... | false |
7,573 | 422491852b80c2fc4a2e73c01fd01acaad4cf9c8 | #Testing Operating System Descriptions
#OS : LMDE 4 Debbie
#Version: 4.6.7
#Kernal Version : 4.19.0-8-amd64
#Scripting Langages : Python3
#----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------... | [
"#Testing Operating System Descriptions\n\n#OS : LMDE 4 Debbie\n#Version: 4.6.7\n#Kernal Version : 4.19.0-8-amd64\n\n#Scripting Langages : Python3\n\n#---------------------------------------------------------------------------------------------------------------------------------------------------------------------... | false |
7,574 | e8daf03f987c7512ff245bfbe16c447acd6b5986 | import API.enum as enum
import re
class ObjectValidator():
def __init__(self, validationData={}, *args, **kwargs):
self.data = validationData
self.statusCode = 200
self.validationPipeline = []
self.errors = {}
self.invalidFields = []
def flush(self):
self = Obj... | [
"import API.enum as enum\nimport re\n\nclass ObjectValidator():\n\n def __init__(self, validationData={}, *args, **kwargs):\n self.data = validationData\n self.statusCode = 200\n self.validationPipeline = []\n self.errors = {}\n self.invalidFields = []\n\n def flush(self):\n... | false |
7,575 | 74e3f4cd7b09d9b96feb3f927a509b113481eaed | from django.apps import AppConfig
class CheckoutConfig(AppConfig):
name = "checkout"
# Override the ready method and import the signals module
# so that update_on_save and update_on_delete will be called
# after an OrderLineItem model instance is saved or deleted
def ready(self):
import c... | [
"from django.apps import AppConfig\n\n\nclass CheckoutConfig(AppConfig):\n name = \"checkout\"\n\n # Override the ready method and import the signals module\n # so that update_on_save and update_on_delete will be called\n # after an OrderLineItem model instance is saved or deleted\n def ready(self):\... | false |
7,576 | 0a19efea0c8d7e5e248ca3265ffcb55604dc500c | __author__ = 'Administrator'
import socket,os,time
server = socket.socket()
server.bind(("localhost",9999))
server.listen()
while True:
conn,addr = server.accept()
while True:
data = conn.recv(1024)
if not data:
break
cmd,filename = data.decode().split()
if o... | [
"__author__ = 'Administrator'\n\nimport socket,os,time\n\nserver = socket.socket()\n\nserver.bind((\"localhost\",9999))\n\nserver.listen()\n\nwhile True:\n conn,addr = server.accept()\n\n while True:\n data = conn.recv(1024)\n if not data:\n break\n\n cmd,filename = data.decod... | true |
7,577 | 4c8a873c816678532b029af409be13258757eae1 | # Напишите программу, которая вводит с клавиатуры последовательность чисел и выводит её
# отсортированной в порядке возрастания.
def is_numb_val(val):
try:
x = float(val)
except ValueError:
return False
else:
return True
def main():
num_seq = input("Введите последовательность ... | [
"# Напишите программу, которая вводит с клавиатуры последовательность чисел и выводит её\n# отсортированной в порядке возрастания.\n\n\ndef is_numb_val(val):\n try:\n x = float(val)\n except ValueError:\n return False\n else:\n return True\n\ndef main():\n num_seq = input(\"Введите ... | false |
7,578 | 20d363f5d02cc0b1069aa8951999c0cb22b85613 | # This is a module
class MyMath:
def isEven(num):
if(num%2==0):
return True
return False
def isOdd(num):
if(num%2==0):
return False
return True
def isPrime(num):
for i in range(2,num):
if num%i==0:
return ... | [
"# This is a module\n\nclass MyMath:\n def isEven(num):\n if(num%2==0):\n return True\n return False\n \n def isOdd(num):\n if(num%2==0):\n return False\n return True\n \n def isPrime(num):\n for i in range(2,num):\n if num%i==0:\n ... | false |
7,579 | 31b87a3ceca1f48665ecc9754d5f87bb9b7bbf13 | import psycopg2
from .configuration import ConfigurationException
DB_CONNECT_STRING = "host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'"
class DBItemCompany:
def __init__(self, _id, tweeter, category, categoryUrl, provenScore, ranking, location, url, categoryId):
self.id = _id
se... | [
"import psycopg2\n\nfrom .configuration import ConfigurationException\n\nDB_CONNECT_STRING = \"host='{host}' dbname='{dbname}' user='{user}' password='{passwd}'\"\n\nclass DBItemCompany:\n def __init__(self, _id, tweeter, category, categoryUrl, provenScore, ranking, location, url, categoryId):\n self.id =... | false |
7,580 | b34ad8d7fc8df0ab86c5930ab2b5aa1f86d13ae3 | from django.db import models
class Author(models.Model):
author = models.CharField(
"Author",
max_length=30,
blank=False,
null=False
)
biography = models.TextField(
"About author",
max_length=500,
blank=True,
null=True
)
def __str__... | [
"from django.db import models\n\n\nclass Author(models.Model):\n author = models.CharField(\n \"Author\",\n max_length=30,\n blank=False,\n null=False\n )\n\n biography = models.TextField(\n \"About author\",\n max_length=500,\n blank=True,\n null=Tru... | false |
7,581 | 2cef5311a9ff9497ad6611fe7b47e4f7c5b1b3c7 | #!/usr/bin/python
from random import *
prob = "change"
cases = [
10,
10,
10,
100,
100,
100000,
100000,
100000,
100000,
100000
]
cur = 0
st = [1,2,5,10,20,50,100,200,500,1000,2000,5000,10000]
for (n) in cases :
cout = ""
... | [
"#!/usr/bin/python\n\nfrom random import *\n\nprob = \"change\"\n\ncases = [ \n 10,\n 10,\n 10,\n 100,\n 100,\n 100000,\n 100000,\n 100000,\n 100000,\n 100000\n ]\ncur = 0\n\nst = [1,2,5,10,20,50,100,200,500,1000,2000,5000,10000]\n\nfor ... | true |
7,582 | a28ece0db9bf0d4c3ab26207216b1da45f7aaa0f | """Proper parenthetics extra credit kata."""
from _que_structure import Q
def proper_parenthetics(string):
"""Return if parentheses are matching or not."""
if isinstance(string, str):
paren_q = Q()
for i in range(len(string)):
paren_q.enqueue(string[i])
opening_parens = 0
... | [
"\"\"\"Proper parenthetics extra credit kata.\"\"\"\n\nfrom _que_structure import Q\n\n\ndef proper_parenthetics(string):\n \"\"\"Return if parentheses are matching or not.\"\"\"\n if isinstance(string, str):\n paren_q = Q()\n for i in range(len(string)):\n paren_q.enqueue(string[i])\... | false |
7,583 | 170716ccaaf45db2ee974de260883a8d70513f52 | from django.db import models
class Event(models.Model):
name = models.TextField()
host = models.TextField(null=True)
fields = models.TextField(null=True)
description = models.TextField(null=True)
date = models.TextField()
start_time = models.TextField()
end_time = models.TextField()
ba... | [
"from django.db import models\n\n\nclass Event(models.Model):\n name = models.TextField()\n host = models.TextField(null=True)\n fields = models.TextField(null=True)\n description = models.TextField(null=True)\n date = models.TextField()\n start_time = models.TextField()\n end_time = models.Tex... | false |
7,584 | 0e19d7251db3382c34ad2d38a7984b65325ecfbf | from django.db import models
from django.db.models.base import Model
# Create your models here.
class Categoria(models.Model):
categoria = models.CharField(max_length=40)
def __str__(self):
return self.categoria
class Producto(models.Model):
codigo = models.CharField(max_length=40)
nombre = mo... | [
"from django.db import models\nfrom django.db.models.base import Model\n\n# Create your models here.\nclass Categoria(models.Model):\n categoria = models.CharField(max_length=40)\n def __str__(self):\n return self.categoria\n\nclass Producto(models.Model):\n codigo = models.CharField(max_length=40)\... | false |
7,585 | ba289bcdc0aa7c2ad70dba7fac541900d0b55387 | import os
# Set a single thread per process for numpy with MKL/BLAS
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_DEBUG_CPU_TYPE'] = '5'
import numpy as np
from matplotlib import pyplot as plt
from copy import deepcopy
from kadal.optim_tools.MOBO import MOBO
from kadal.s... | [
"import os\n# Set a single thread per process for numpy with MKL/BLAS\nos.environ['MKL_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_DEBUG_CPU_TYPE'] = '5'\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom copy import deepcopy\n\nfrom kadal.optim_tools.MOBO import MO... | false |
7,586 | fa3ab879541c04e278317b11dd79e6e1b4319536 | FILE = "Luke"
NAME = "Luke Walker"
NATIONALITY = "American"
CLASS = "Manipulator"
WEAPON = ""
BIRTH = ""
BIRTH_LOCATION = ""
LETTER = "W"
RECRUITMENT_ORDER = 10
SUMMARY = ""
ABILITIES = ""
BACKSTORY = ""
HIGHLIGHTS = ""
SUMMONS = ("Tonberry", "Grimnir", "Griever", "Starlet")
| [
"FILE = \"Luke\"\n\nNAME = \"Luke Walker\"\n\nNATIONALITY = \"American\"\n\nCLASS = \"Manipulator\"\n\nWEAPON = \"\"\n\nBIRTH = \"\"\n\nBIRTH_LOCATION = \"\"\n\nLETTER = \"W\"\n\nRECRUITMENT_ORDER = 10\n\nSUMMARY = \"\"\n\nABILITIES = \"\"\n\nBACKSTORY = \"\"\n\nHIGHLIGHTS = \"\"\n\nSUMMONS = (\"Tonberry\", \"Grimn... | false |
7,587 | 1574f034ff9b6ddb785e4c54758b2057009198ed | alias_macro = {
"class": "Application",
"method": "alias_macro",
"doc": """
Returns or modifies the macro of a command alias.
""",
"syntax": """
Rhino.AliasMacro (strAlias [, strMacro])
""",
"params": {
0: {
"name": "alias",
"... | [
"alias_macro = {\r\n\r\n \"class\": \"Application\",\r\n \"method\": \"alias_macro\",\r\n \"doc\": \"\"\"\r\n Returns or modifies the macro of a command alias.\r\n \"\"\",\r\n\r\n \"syntax\": \"\"\"\r\n Rhino.AliasMacro (strAlias [, strMacro])\r\n \"\"\",\r\n\r\n \"params\": {\r\n... | false |
7,588 | 3dc83168264fbb4f9b0ab2980b845dffdc4417bb | import requests
from bs4 import BeautifulSoup
class Book:
def __init__(self, url):
self.url = url
self.title = ""
self.category = ""
self.upc=""
self.price_including_tax=""
self.price_excluding_tax=""
self.number_available=""
self.description=""
... | [
"import requests\nfrom bs4 import BeautifulSoup\n\nclass Book:\n\n def __init__(self, url):\n self.url = url\n self.title = \"\"\n self.category = \"\"\n self.upc=\"\"\n self.price_including_tax=\"\"\n self.price_excluding_tax=\"\"\n self.number_available=\"\"\n ... | false |
7,589 | e51c57f4487a3225936d073142f1f770815c0d47 | #!/usr/bin/env python
# coding: utf-8
import sys,pysrt
import urllib2,urllib,json
import re
from urlparse import urlparse
import os
from mtranslate import translate
from argparse import ArgumentParser
reload(sys)
sys.setdefaultencoding('utf8')
#------------------------------------------------------------------------... | [
"#!/usr/bin/env python\n# coding: utf-8\nimport sys,pysrt\nimport urllib2,urllib,json\nimport re\nfrom urlparse import urlparse\nimport os\nfrom mtranslate import translate\nfrom argparse import ArgumentParser\nreload(sys) \nsys.setdefaultencoding('utf8')\n\n#-------------------------------------------------------... | true |
7,590 | 1a29b3138f6a33fbe2781f044c1bcccd03ecd48d |
d = {
1 : 'I',
5 : 'V',
10: 'X',
50: 'L',
100: 'C',
500: 'D',
1000: 'M'
}
e = {
'I': 1,
'V': 5,
'X': 10,
'L': 50,
'C': 100,
'D': 500,
... | [
"\nd = {\n 1 : 'I',\n 5 : 'V',\n 10: 'X',\n 50: 'L',\n 100: 'C',\n 500: 'D',\n 1000: 'M' \n\n }\n\n\ne = {\n 'I': 1,\n 'V': 5,\n 'X': 10,\n 'L': 50,\n 'C': 100,\n ... | false |
7,591 | b6b3d94db62b47aac9bf78e8224a38ccff9335e3 | from . import utility
from . import regular_gram_schmidt as RGSC
from . import custom_gram_schmidt as CGSC
def RegularGramSchmidt():
while True:
vectors = utility.get_matrix_from_user(5)
if len(vectors) > 0:
calc = RGSC.RegularGramSchmidt()
result_matrix = calc.calc(vectors... | [
"from . import utility\nfrom . import regular_gram_schmidt as RGSC\nfrom . import custom_gram_schmidt as CGSC\n\n\ndef RegularGramSchmidt():\n while True:\n vectors = utility.get_matrix_from_user(5)\n if len(vectors) > 0:\n calc = RGSC.RegularGramSchmidt()\n result_matrix = ca... | false |
7,592 | ad024a2001dc6a6fa3a2a9c1b51f79132e914897 | # -*- coding: utf-8 -*-
import requests
import json
url = "http://39.108.188.34:9090/spider/zhongdengdengji.go"
# url = "http://localhost:9090/spider/zhongdengdengji.go"
input = {
"timelimit": "1年",
"title": "GD20190305001",
"maincontractno": "YT20181228001",
"maincontractcurrency": "人民币",
"mainc... | [
"# -*- coding: utf-8 -*-\n\nimport requests\nimport json\n\nurl = \"http://39.108.188.34:9090/spider/zhongdengdengji.go\"\n# url = \"http://localhost:9090/spider/zhongdengdengji.go\"\n\ninput = {\n \"timelimit\": \"1年\",\n \"title\": \"GD20190305001\",\n \"maincontractno\": \"YT20181228001\",\n \"mainco... | false |
7,593 | f51d85ff352d9c84a8ded29ad94b24ca6dda46ad |
'''
IplNorm.py
Description:
Normalizing 0 - 255 initial fingerprint to a normalized image.
Using energy normalization.
Input:
-image
Output:
-norm_im
@author: Edoardo Foco
'''
import cv2
import numpy as np
def normalise(image):
dbl_image = image.astype(float)
#... | [
"\n'''\nIplNorm.py\nDescription: \n Normalizing 0 - 255 initial fingerprint to a normalized image.\n Using energy normalization.\n \n Input:\n -image\n \n Output:\n -norm_im\n@author: Edoardo Foco\n'''\n\nimport cv2\nimport numpy as np\n\ndef normalise(image):\n \n dbl_image = ... | false |
7,594 | 9fd73e0a1dacc46c177f11ce4cf2351b3d622c0d | # Improting Image class from PIL module
from PIL import Image
# Opens a image in RGB mode
im = Image.open("data/frame1.jpg")
# Setting the points for cropped image
left = 155
top = 65
right = 360
bottom = 270
# Cropped image of above dimension
# (It will not change orginal image)
im1 = im.crop((left, top, right, bot... | [
"# Improting Image class from PIL module\nfrom PIL import Image\n\n# Opens a image in RGB mode\nim = Image.open(\"data/frame1.jpg\")\n\n# Setting the points for cropped image\nleft = 155\ntop = 65\nright = 360\nbottom = 270\n\n# Cropped image of above dimension\n# (It will not change orginal image)\nim1 = im.crop((... | false |
7,595 | d7570bbea1e8c7674d507f8e86ce04d22058b21b | class Solution:
def numIdenticalPairs(self, nums: List[int]) -> int:
count = 0
inputLength = len(nums)
for i in range (0, inputLength):
for j in range (i, inputLength - 1):
if (nums[i] == nums[j + 1]): count += 1
return count | [
"class Solution:\n def numIdenticalPairs(self, nums: List[int]) -> int:\n count = 0\n inputLength = len(nums)\n for i in range (0, inputLength):\n for j in range (i, inputLength - 1):\n if (nums[i] == nums[j + 1]): count += 1\n return count",
"class Solution:\n\n def numIdenticalPairs(... | false |
7,596 | ef7fad5019e79950e8fad56404e9ba5d302cfe1c |
def convertEnEntier(nombre):
result = "";
if (nombre == 4):
result = "IV"
if (nombre == 3):
result = "III"
if (nombre == 2):
result = "II"
if (nombre == 1):
result = "I"
return result
print (convertEnEntier(1))
print (convertEnEntier(2))
pri... | [
"\r\ndef convertEnEntier(nombre):\r\n\r\n result = \"\";\r\n if (nombre == 4):\r\n result = \"IV\"\r\n if (nombre == 3):\r\n result = \"III\"\r\n if (nombre == 2):\r\n result = \"II\"\r\n if (nombre == 1):\r\n result = \"I\"\r\n\r\n return result\r\n\r\n\r\nprint (conve... | false |
7,597 | 8b0eed6d1f24b5dd30726ce08c97354a5d5ab69b | # Generated by Django 2.1.2 on 2018-10-25 09:36
import django.contrib.auth.models
import django.contrib.auth.validators
from django.db import migrations, models
import django.utils.timezone
import uuid
class Migration(migrations.Migration):
dependencies = [
('grafit', '0002_article'),
]
operati... | [
"# Generated by Django 2.1.2 on 2018-10-25 09:36\n\nimport django.contrib.auth.models\nimport django.contrib.auth.validators\nfrom django.db import migrations, models\nimport django.utils.timezone\nimport uuid\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('grafit', '0002_article'),\n... | false |
7,598 | 42e16def0fcf234f3d7c2709de36a321d8ddf29e | import collections
import re
from collections import Counter
import operator
import pickle
import math
import json
path='C:/Users/rahul/Desktop/CSCI 544/HW 2/op_spam_train/'
#path=sys.argv[1]
badWordList = ['and','the','was','for']
RE=r'\b[^\W\d_]+\b'
# NEGATIVE TWEETS
c=collections.Counter()
NT="negativeTweets.txt/... | [
"import collections\nimport re\nfrom collections import Counter\nimport operator\nimport pickle\nimport math\nimport json\n\npath='C:/Users/rahul/Desktop/CSCI 544/HW 2/op_spam_train/'\n#path=sys.argv[1]\n\nbadWordList = ['and','the','was','for']\nRE=r'\\b[^\\W\\d_]+\\b'\n\n# NEGATIVE TWEETS\nc=collections.Counter()... | true |
7,599 | 5adb16c654a4e747f803590c42328fa6ba642e95 | import os
from subprocess import Popen, PIPE
from Bio import SeqIO
from Bio.Align.Applications import ClustalOmegaCommandline
from Bio import Phylo
from io import StringIO
# from ete3 import Tree, TreeStyle
import pylab
class TreeDrawer:
def __init__(self, sequences=None):
self.sequences = sequences
... | [
"import os\nfrom subprocess import Popen, PIPE\nfrom Bio import SeqIO\nfrom Bio.Align.Applications import ClustalOmegaCommandline\nfrom Bio import Phylo\nfrom io import StringIO\n# from ete3 import Tree, TreeStyle\nimport pylab\n\n\nclass TreeDrawer:\n\n def __init__(self, sequences=None):\n self.sequence... | false |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.