id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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147,689 | import ivy
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
outputs_to_native_arrays,
)
from ivy.func_wrapper import outputs_to_ivy_arrays
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.is_local():
module = ivy.utils._importlib.import_cache[_... | null |
147,690 | jax_enable_x64 = False
def update(value, toggle):
global jax_enable_x64
if value == "jax_enable_x64":
jax_enable_x64 = toggle | null |
147,691 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.is_local():
module = iv... | null |
147,692 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,693 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.is_loca... | null |
147,694 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _remove_axis(shape, axis):
def gumbel(key, shape=(), dtype="float64"):
from ivy.functional.frontends imp... | null |
147,695 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,696 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,697 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def maxwell(key, shape, dtype="float64"):
def rademacher(key, shape, dtype="int64"):
from ivy.functional.fro... | null |
147,698 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,699 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.is_local():
module = iv... | null |
147,700 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,701 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,702 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,703 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def normal(key, shape=(), dtype=None):
seed = _get_seed(key)
return ivy.random_normal(shape=shape, dt... | null |
147,704 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,705 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,706 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,707 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,708 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,709 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
if "PRNGKeyArray" in repr(key):
key = key._base_array
key1, key2 = int(ke... | null |
147,710 | import operator
import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.jax.func_wrapper import (
to_ivy_arrays_and_back,
handle_jax_dtype,
)
def _get_seed(key):
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.is_loca... | null |
147,711 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
import ivy.functional.frontends.scipy as sc_frontend
def minkowski(u, v, p=2, /, *, w=None):
u = _validate_vector(u)
v = _validate_vector(v)
if p <= 0:
raise ValueError("p must be greater than 0")
u_v = u -... | null |
147,712 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def _check_finite(a):
if not ivy.all(ivy.isfinite(a)):
raise ValueError("Array must not contain infs or NaNs")
def eigh_tridiagonal(
d,
e,
/,
*,
eigvals_only=False,
select="a",
select_range... | null |
147,713 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def _check_finite(a):
if not ivy.all(ivy.isfinite(a)):
raise ValueError("Array must not contain infs or NaNs")
def inv(a, /, *, overwrite_a=False, check_finite=True):
if check_finite:
_check_finite(a)
... | null |
147,714 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def kron(a, b):
return ivy.kron(a, b) | null |
147,715 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def _check_finite(a):
def lu_factor(a, /, *, overwrite_a=False, check_finite=True):
if check_finite:
_check_finite(a)
return ivy.lu_factor(a) | null |
147,716 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def _check_finite(a):
if not ivy.all(ivy.isfinite(a)):
raise ValueError("Array must not contain infs or NaNs")
def norm(a, /, *, ord=None, axis=None, keepdims=False, check_finite=True):
if check_finite:
_c... | null |
147,717 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def _check_finite(a):
if not ivy.all(ivy.isfinite(a)):
raise ValueError("Array must not contain infs or NaNs")
def pinv(
a,
/,
*,
atol=None,
rtol=None,
return_rank=False,
cond=None,
rco... | null |
147,718 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def _check_finite(a):
if not ivy.all(ivy.isfinite(a)):
raise ValueError("Array must not contain infs or NaNs")
def svd(
a, /, *, full_matrices=True, compute_uv=True, overwrite_a=False, check_finite=True
):
if ... | null |
147,719 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def _check_finite(a):
if not ivy.all(ivy.isfinite(a)):
raise ValueError("Array must not contain infs or NaNs")
def svdvals(a, /, *, overwrite_a=False, check_finite=True):
if check_finite:
_check_finite(a)
... | null |
147,720 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def tril(m, /, *, k=0):
return ivy.tril(m, k=k) | null |
147,721 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
def triu(m, /, *, k=0):
return ivy.triu(m, k=k) | null |
147,722 | from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
import ivy
def fft(x, n=None, axis=-1, norm="backward", overwrite_x=False):
return ivy.fft(x, axis, norm=norm, n=n) | null |
147,723 | from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
import ivy
def fft2(x, s=None, axes=(-2, -1), norm=None, overwrite_x=False):
return ivy.fft2(x, s=s, dim=axes, norm=norm) | null |
147,724 | from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
import ivy
def dct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False, orthogonalize=None):
return ivy.dct(x, type=type, n=n, axis=axis, norm=norm)
def idct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False, orthogonalize=N... | null |
147,725 | from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
import ivy
def ifft(x, n=None, axis=-1, norm=None, overwrite_x=False):
return ivy.ifft(x, axis, norm=norm, n=n) | null |
147,726 | from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
import ivy
def ifftn(
x, s=None, axes=None, norm=None, overwrite_x=False, workers=None, *, plan=None
):
return ivy.ifftn(x, s=s, axes=axes, norm=norm) | null |
147,727 | from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
import ivy
def rfftn(
x, s=None, axes=None, norm=None, overwrite_x=False, workers=None, *, plan=None
):
return ivy.rfftn(x, s=s, axes=axes, norm=norm) | null |
147,728 | from functools import wraps
def outputs_to_self_class(func):
@wraps(func)
def _outputs_to_self_class(*args, **kwargs):
self_arg = args[0]
return self_arg.__class__(
func(*args, **kwargs),
index=self_arg.index,
columns=self_arg.columns,
dtype=self_... | null |
147,729 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import (
to_ivy_arrays_and_back,
handle_mxnet_out,
)
from ivy.functional.frontends.mxnet.numpy import promote_types_of_mxnet_inputs
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_spec... | null |
147,730 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import (
to_ivy_arrays_and_back,
handle_mxnet_out,
)
from ivy.functional.frontends.mxnet.numpy import promote_types_of_mxnet_inputs
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_spec... | null |
147,731 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import (
to_ivy_arrays_and_back,
)
from ivy.functional.frontends.numpy.func_wrapper import handle_numpy_dtype
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
... | null |
147,732 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,733 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,734 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
def gamma(shape, scale=1.0, size=None, dtype=None, device=None, out=None):
return ivy.experimental.gamma(
shape, scale, shape=size, dtype=dtype, device=device, out=out
)
import ivy
from ivy.utils.exceptions import... | null |
147,735 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,736 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,737 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
def beta(a, b, size=None, dtype=None, device=None):
return ivy.experimental.beta(a, b, shape=size, dtype=dtype, device=device)
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import... | null |
147,738 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,739 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,740 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,741 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
from ivy.functional.frontends.numpy.mathematical_functions.miscellaneous im... | null |
147,742 | import ivy
from ivy.functional.frontends.mxnet.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.numpy.func_wrapper import handle_numpy_dtype
def softmax(data, length=None, axis=-1, temperature=None, use_length=False, dtype=None):
ret = ivy.softmax(data, axis=axis)
if dtype:
ivy.... | null |
147,743 | import functools
import inspect
from typing import Callable
import ivy
from ivy.functional.frontends.mxnet.numpy.ndarray import ndarray
def handle_mxnet_out(fn: Callable) -> Callable:
@functools.wraps(fn)
def _handle_mxnet_out(*args, **kwargs):
if "out" not in kwargs:
keys = list(inspect.si... | null |
147,744 | import functools
import inspect
from typing import Callable
import ivy
from ivy.functional.frontends.mxnet.numpy.ndarray import ndarray
def inputs_to_ivy_arrays(fn: Callable) -> Callable:
def _inputs_to_ivy_arrays_mxnet(*args, **kwargs):
"""Convert `ndarray.NDArray` into `ivy.Array` instances.
Conve... | Wrap `fn` so it receives and returns `ivy.Array` instances. Wrap `fn` so that input arrays are all converted to `ivy.Array` instances and return arrays are all converted to `ndarray.NDArray` instances. |
147,745 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from .gbm import GBLinear
def _binary_prediction(obj, raw_pred):
# apply activation function
pred = obj.pred_transform(raw_pred)
# apply probability thresholding
return ivy.where(pred >= 0.5, 1.0, 0.0) | null |
147,746 | from .core import Booster
class Booster:
def __init__(self, params=None, cache=None, model_file=None, compile=False):
# cache[0] refers to input data while cache[1] refers to input target
n_feat = cache[0].shape[1]
n_inst = cache[0].shape[0]
n_output_group = ivy.unique_values(cache[... | Train a booster with given parameters. Parameters ---------- params Booster params. dtrain Data to be trained. dlabel Training labels. num_boost_round Number of boosting iterations. evals List of validation sets for which metrics will be evaluated during training. Validation metrics will help us track the performance o... |
147,747 | import ivy
from ivy.functional.frontends.xgboost.linear.coordinate_common import (
get_bias_gradient,
coordinate_delta_bias,
update_bias_residual,
coordinate_delta,
)
def coordinate_delta(sum_grad, sum_hess, w, reg_alpha, reg_lambda):
mask = ivy.where(sum_hess < 1e-5, 0.0, 1.0)
sum_grad_l2 = s... | Implements one step of coordinate descent. The original optimizer implements parallel calculations. The below code is an approximation of the original one, but rather than computing the update direction for a single parameter at a time using a for loop and cumulative gradients, it does the update in parallel by means o... |
147,748 | import ivy
from ivy.functional.frontends.xgboost.objective.regression_loss import (
LogisticRegression,
)
from ivy.functional.frontends.xgboost.linear.updater_coordinate import (
coordinate_updater,
)
from copy import deepcopy
def _get_gradient(obj, pred, label, scale_pos_weight):
p = obj.pred_transform(pr... | null |
147,749 | import ivy
from ivy.functional.frontends.xgboost.objective.regression_loss import (
LogisticRegression,
)
from ivy.functional.frontends.xgboost.linear.updater_coordinate import (
coordinate_updater,
)
from copy import deepcopy
def _pred(dt, w, base):
return ivy.matmul(dt, w[:-1]) + w[-1] + base | null |
147,750 | import functools
from typing import Callable
import ivy
import ivy.functional.frontends.onnx as onnx_frontend
def _ivy_array_to_onnx(x):
if isinstance(x, ivy.Array) or ivy.is_native_array(x):
return onnx_frontend.Tensor(x)
return x | null |
147,751 | import functools
from typing import Callable
import ivy
import ivy.functional.frontends.onnx as onnx_frontend
def inputs_to_ivy_arrays(fn: Callable) -> Callable:
def _inputs_to_ivy_arrays_onnx(*args, **kwargs):
"""Convert `Tensor` into `ivy.Array` instances.
Convert all `Tensor` instances in both th... | Wrap `fn` so it receives and returns `ivy.Array` instances. Wrap `fn` so that input arrays are all converted to `ivy.Array` instances and return arrays are all converted to `ndarray.NDArray` instances. |
147,752 | import ivy
from ivy.functional.frontends.onnx.func_wrapper import to_ivy_arrays_and_back
def Abs(input):
return ivy.abs(input) | null |
147,753 | import ivy
from ivy.functional.frontends.onnx.func_wrapper import to_ivy_arrays_and_back
def Acos(input):
return ivy.acos(input) | null |
147,754 | import ivy
from ivy.functional.frontends.onnx.func_wrapper import to_ivy_arrays_and_back
def Acosh(input):
return ivy.acosh(input) | null |
147,755 | import ivy
from ivy.functional.frontends.onnx.func_wrapper import to_ivy_arrays_and_back
def Add(x1, x2):
return ivy.add(x1, x2) | null |
147,756 | import ivy
from ivy.functional.frontends.onnx.func_wrapper import to_ivy_arrays_and_back
def Asin(input):
return ivy.asin(input) | null |
147,757 | import ivy
from ivy.functional.frontends.onnx.func_wrapper import to_ivy_arrays_and_back
def MatMul(x1, x2):
return ivy.matmul(x1, x2) | null |
147,758 | from ivy.functional.frontends.onnx.proto import NodeProto
from ivy_tests.test_ivy.helpers.testing_helpers import _import_fn
class NodeProto:
def __init__(self):
self._fn = None
self._fn_mod = None
self._fn_name = None
self.input = None
self.output = None
self.name = ... | null |
147,759 | import ivy
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_device_and_dtypes
def nms(boxes, scores, iou_threshold):
return ivy.nms(boxes, scores, iou_threshold)
import ivy.functional.frontends.torch as torch
import ... | null |
147,760 | import ivy
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_device_and_dtypes
def box_area(boxes):
return ivy.prod(boxes[..., 2:] - boxes[..., :2], axis=-1)
import ivy.functional.frontends.torch as torch
import ivy
f... | null |
147,761 | import ivy
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_device_and_dtypes
import ivy.functional.frontends.torch as torch
import ivy
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.... | null |
147,762 | import ivy
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_device_and_dtypes
import ivy.functional.frontends.torch as torch
import ivy
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.... | null |
147,763 | import ivy
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_device_and_dtypes
import ivy.functional.frontends.torch as torch
import ivy
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.... | null |
147,764 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def celu(input, alpha=1.0, inplace=False):
return ivy.celu(input, alpha=alpha)
def celu_(input, alpha=1.0):
return celu(input, alpha=alpha, inpla... | null |
147,765 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def elu(input, alpha=1.0, inplace=False):
prod = ivy.multiply(
alpha,
ivy.subtract(ivy.exp(input), 1),
)
return ivy.where(ivy.... | null |
147,766 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def gelu(input, *, approximate="none"):
if approximate == "none":
return ivy.gelu(input, approximate=False)
elif approximate == "tanh":
... | null |
147,767 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def sigmoid(input):
return ivy.sigmoid(input)
def glu(input, dim=-1):
a, b = ivy.split(input, num_or_size_splits=2, axis=dim)
return ivy.mult... | null |
147,768 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def softmax(input, dim=None, _stacklevel=3, dtype=None):
if dtype:
input = ivy.astype(ivy.array(input), ivy.as_ivy_dtype(dtype))
return iv... | null |
147,769 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def hardshrink(input, lambd=0.5):
mask = ivy.logical_or(ivy.greater(input, lambd), ivy.less(input, -lambd))
return ivy.where(mask, input, 0.0) | null |
147,770 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def hardsigmoid(input, inplace=False):
return ivy.divide(ivy.minimum(ivy.maximum(ivy.add(input, 3), 0), 6), 6) | null |
147,771 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def relu6(input, inplace=False):
def hardswish(input, inplace=False):
relu6_val = ivy.relu6(ivy.add(input, 3))
return ivy.multiply(input, ivy.div... | null |
147,772 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def hardtanh(input, min_val=-1.0, max_val=1.0, inplace=False):
def hardtanh_(input, min_val=-1.0, max_val=1.0):
return hardtanh(input, min_val=min_va... | null |
147,773 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def leaky_relu(input, negative_slope=0.01, inplace=False):
return ivy.leaky_relu(input, alpha=negative_slope)
def leaky_relu_(input, negative_slope=0... | null |
147,774 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def local_response_norm(input, size, alpha=0.0001, beta=0.75, k=1.0):
non_batched = input.ndim == 3
if non_batched:
input = ivy.expand_di... | null |
147,775 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def log_softmax(input, dim=None, _stacklevel=3, dtype=None):
if dtype:
input = ivy.astype(ivy.array(input), ivy.as_ivy_dtype(dtype))
if d... | null |
147,776 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def logsigmoid(input):
return ivy.logsigmoid(input) | null |
147,777 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def softplus(input, beta=1, threshold=20):
def tanh(input):
def mish(input, inplace=False):
return ivy.multiply(
input,
ivy.tanh(ivy.... | null |
147,778 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def normalize(input, p=2.0, dim=1, eps=1e-12, out=None):
abs_square = ivy.pow(ivy.abs(input), p)
sum_ = ivy.sum(abs_square, axis=dim, keepdims=Tr... | null |
147,779 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def prelu(input, weight):
return ivy.add(ivy.maximum(0, input), ivy.multiply(weight, ivy.minimum(0, input))) | null |
147,780 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def relu(input, inplace=False):
return ivy.relu(input)
def relu_(input):
return relu(input, inplace=True) | null |
147,781 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def rrelu(input, lower=1.0 / 8, upper=1.0 / 3, training=False, inplace=False):
if training:
# alpha = ivy.random_uniform(low=lower, high=upper... | null |
147,782 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def scaled_dot_product_attention(
query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None
):
return ivy.scaled_dot_product_... | null |
147,783 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def selu(input, inplace=False):
return ivy.selu(input) | null |
147,784 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def sigmoid(input):
return ivy.sigmoid(input)
def silu(input, inplace=False):
return ivy.multiply(input, ivy.sigmoid(input)) | null |
147,785 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def softmax(input, dim=None, _stacklevel=3, dtype=None):
if dtype:
input = ivy.astype(ivy.array(input), ivy.as_ivy_dtype(dtype))
return iv... | null |
147,786 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def softshrink(input, lambd=0.5):
low = ivy.where(ivy.less(input, -lambd), ivy.add(input, lambd), 0)
up = ivy.where(ivy.greater(input, lambd), iv... | null |
147,787 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def softsign(input):
return ivy.divide(input, ivy.add(1, ivy.abs(input))) | null |
147,788 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
def tanh(input):
return ivy.tanh(input)
def tanhshrink(input):
return ivy.subtract(input, ivy.tanh(input)) | null |
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