id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
149,089 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def multiply(
x1: Union[float, np.ndarray],
x2:... | null |
149,090 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def trapz(
y: np.ndarray,
/,
*,
x: Opt... | null |
149,091 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def tan(x: np.ndarray, /, *, out: Optional[np.ndarray]... | null |
149,092 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def tanh(
x: np.ndarray, /, *, complex_mode="jax",... | null |
149,093 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
import ivy
from ivy.utils.exceptions import handle_exc... | null |
149,094 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def abs(
x: Union[float, np.ndarray],
/,
*,... | null |
149,095 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
import ivy
from ivy.utils.exceptions import handle_exc... | null |
149,096 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
import ivy
from ivy.utils.exceptions import handle_exc... | null |
149,097 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def reciprocal(
x: Union[float, np.ndarray], /, *,... | null |
149,098 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def deg2rad(x: np.ndarray, /, *, out: Optional[np.ndar... | null |
149,099 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def rad2deg(x: np.ndarray, /, *, out: Optional[np.ndar... | null |
149,100 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def isreal(x: np.ndarray, /, *, out: Optional[np.ndarr... | null |
149,101 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def fmod(
x1: np.ndarray,
x2: np.ndarray,
... | null |
149,102 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def angle(
z: np.ndarray,
/,
*,
deg: b... | null |
149,103 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def gcd(
x1: Union[np.ndarray, int, list, tuple],
... | null |
149,104 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def imag(
val: np.ndarray,
/,
*,
out: ... | null |
149,105 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def nan_to_num(
x: np.ndarray,
/,
*,
c... | null |
149,106 | from typing import Union, Optional
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy import promote_types_of_inputs
from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
from . import backend_version
def real(x: np.ndarray, /, *, out: Optional[np.ndarray... | null |
149,107 | import functools
from typing import Callable
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_scalar_output_to_0d_array` function. Write a Python function `def _scalar_output_to_0d_array(function: Callable) -> Callable` to solve the following problem:
Convert scalar o... | Convert scalar outputs to 0d arrays. Sometimes NumPy functions return scalars e.g. `np.add` does when the inputs are both 0 dimensional. We use this wrapper to handle such cases, and convert scalar outputs to 0d arrays, since the array API standard dictates outputs must be arrays. |
149,108 | import os
import time
import logging
import numpy as np
from typing import Union, Optional, Any
import ivy
from ivy.functional.ivy.device import Profiler as BaseProfiler
def as_ivy_dev(device: str, /):
if "gpu" in device:
logging.warning(
"Native Numpy does not support GPU placement, consider us... | null |
149,109 | import os
import time
import logging
import numpy as np
from typing import Union, Optional, Any
import ivy
from ivy.functional.ivy.device import Profiler as BaseProfiler
def clear_cached_mem_on_dev(device: str, /):
return None | null |
149,110 | import os
import time
import logging
import numpy as np
from typing import Union, Optional, Any
import ivy
from ivy.functional.ivy.device import Profiler as BaseProfiler
def tpu_is_available() -> bool:
return False | null |
149,111 | import os
import time
import logging
import numpy as np
from typing import Union, Optional, Any
import ivy
from ivy.functional.ivy.device import Profiler as BaseProfiler
def num_gpus() -> int:
return 0 | null |
149,112 | import os
import time
import logging
import numpy as np
from typing import Union, Optional, Any
import ivy
from ivy.functional.ivy.device import Profiler as BaseProfiler
def gpu_is_available() -> bool:
return False | null |
149,113 | import os
import time
import logging
import numpy as np
from typing import Union, Optional, Any
import ivy
from ivy.functional.ivy.device import Profiler as BaseProfiler
def as_native_dev(device: str, /):
if "gpu" in device:
logging.warning(
"Native Numpy does not support GPU placement, consider... | null |
149,114 | import os
import time
import logging
import numpy as np
from typing import Union, Optional, Any
import ivy
from ivy.functional.ivy.device import Profiler as BaseProfiler
def handle_soft_device_variable(*args, fn, **kwargs):
return fn(*args, **kwargs) | null |
149,115 | import numpy as np
from typing import Optional, Union, Sequence
import ivy
from ivy.functional.ivy.random import (
_check_bounds_and_get_shape,
_randint_check_dtype_and_bound,
_check_valid_scale,
)
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
import ivy
from ivy.utils.... | null |
149,116 | import numpy as np
from typing import Optional, Union, Sequence
import ivy
from ivy.functional.ivy.random import (
_check_bounds_and_get_shape,
_randint_check_dtype_and_bound,
_check_valid_scale,
)
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
import ivy
from ivy.utils.... | null |
149,117 | import numpy as np
from typing import Optional, Union, Sequence
import ivy
from ivy.functional.ivy.random import (
_check_bounds_and_get_shape,
_randint_check_dtype_and_bound,
_check_valid_scale,
)
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
def multinomial(
popul... | null |
149,118 | import numpy as np
from typing import Optional, Union, Sequence
import ivy
from ivy.functional.ivy.random import (
_check_bounds_and_get_shape,
_randint_check_dtype_and_bound,
_check_valid_scale,
)
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
import ivy
from ivy.utils.... | null |
149,119 | import numpy as np
from typing import Optional, Union, Sequence
import ivy
from ivy.functional.ivy.random import (
_check_bounds_and_get_shape,
_randint_check_dtype_and_bound,
_check_valid_scale,
)
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
def seed(*, seed_value: in... | null |
149,120 | import numpy as np
from typing import Optional, Union, Sequence
import ivy
from ivy.functional.ivy.random import (
_check_bounds_and_get_shape,
_randint_check_dtype_and_bound,
_check_valid_scale,
)
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
def shuffle(
x: np.nda... | null |
149,121 | import numpy as np
from typing import Union, Tuple, Optional, Sequence
import ivy
from ivy.functional.ivy.layers import (
_handle_padding,
_deconv_length,
_get_x_data_format,
)
def _dilate_pad_conv_tranpose(
x, filters, strides, padding, dims, dilations, output_shape
):
strides = [strides] * dims if... | null |
149,122 | import numpy as np
from typing import Union, Tuple, Optional, Sequence
import ivy
from ivy.functional.ivy.layers import (
_handle_padding,
_deconv_length,
_get_x_data_format,
)
def _dilate_pad_conv_tranpose(
x, filters, strides, padding, dims, dilations, output_shape
):
def conv2d(
x: np.ndarray,
... | null |
149,123 | import numpy as np
from typing import Union, Tuple, Optional, Sequence
import ivy
from ivy.functional.ivy.layers import (
_handle_padding,
_deconv_length,
_get_x_data_format,
)
def conv2d(
x: np.ndarray,
filters: np.ndarray,
strides: Union[int, Tuple[int, int]],
padding: Union[str, int, Sequ... | null |
149,124 | import numpy as np
from typing import Union, Tuple, Optional, Sequence
import ivy
from ivy.functional.ivy.layers import (
_handle_padding,
_deconv_length,
_get_x_data_format,
)
def _dilate_pad_conv_tranpose(
x, filters, strides, padding, dims, dilations, output_shape
):
def conv3d(
x: np.ndarray,
... | null |
149,125 | import numpy as np
from typing import Union, Tuple, Optional, Sequence
import ivy
from ivy.functional.ivy.layers import (
_handle_padding,
_deconv_length,
_get_x_data_format,
)
def _dilate_pad_conv_tranpose(
x, filters, strides, padding, dims, dilations, output_shape
):
strides = [strides] * dims if... | null |
149,126 | from typing import Optional, Union, Sequence, List
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
from . import backend_version
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.fron... | null |
149,127 | from typing import Optional, Union, Sequence, List
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
from . import backend_version
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.fron... | null |
149,128 | from typing import Optional, Union, Sequence, List
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
from . import backend_version
class Finfo:
def __init__(self, np_finfo: np.finfo):
self._np_finfo = np_fi... | null |
149,129 | from typing import Optional, Union, Sequence, List
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
from . import backend_version
def as_native_dtype(dtype_in: Union[np.dtype, str, bool, int, float], /) -> np.dtype:
... | null |
149,130 | from typing import Optional, Union, Sequence, List
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
from . import backend_version
def as_ivy_dtype(
dtype_in: Union[np.dtype, str, int, float, complex, bool],
/,... | null |
149,131 | from typing import Optional, Union, Sequence, List
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
from . import backend_version
def as_ivy_dtype(
dtype_in: Union[np.dtype, str, int, float, complex, bool],
/,... | null |
149,132 | from typing import Optional, Union, Sequence, List
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
from . import backend_version
ivy_dtype_dict = {
np.dtype("int8"): "int8",
np.dtype("int16"): "int16",
np... | null |
149,133 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,134 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,135 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,136 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,137 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,138 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,139 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,140 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,141 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,142 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,143 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,144 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,145 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,146 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,147 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,148 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,149 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,150 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,151 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,152 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,153 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,154 | import multiprocessing as _multiprocessing
from functools import reduce as _reduce
from numbers import Number
from operator import mul
from typing import Callable, Optional, Sequence, Tuple, Union
import numpy as np
import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.backends.numpy.helpe... | null |
149,155 | import logging
from typing import Callable
def bind_custom_gradient_function(func, custom_grad_fn):
logging.warning(
"NumPy does not support autograd, 'bind_custom_gradient_function' "
"has no effect on the array, as gradients are not supported in the first place."
)
return func | null |
149,156 | import logging
from typing import Callable
def vjp(func: Callable, *primals):
logging.warning(
"NumPy does not support autograd, 'vjp' returns None in place of `vjpfun`."
)
return func(*primals), None | null |
149,157 | import logging
from typing import Callable
def jvp(func: Callable, primals, tangents):
logging.warning(
"NumPy does not support autograd, "
"'jvp' returns None in place of `tangents_out`."
)
return func(*primals), None | null |
149,158 | import numpy as np
from typing import Optional, Union
def invert_permutation(
x: Union[np.ndarray, list, tuple],
/,
) -> np.ndarray:
sorted_indices = np.argsort(x)
inverse = np.zeros_like(sorted_indices)
inverse[sorted_indices] = np.arange(len(x))
inverse_permutation = np.argsort(inverse)
r... | null |
149,159 | import numpy as np
from typing import Optional, Union
def lexsort(
keys: np.ndarray, /, *, axis: int = -1, out: Optional[np.ndarray] = None
) -> np.ndarray:
return np.asarray(np.lexsort(keys, axis=axis)) | null |
149,160 | import numpy as np
from typing import Optional
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
def l1_normalize(
x: np.ndarray,
/,
*,
axis: Optional[int] = None,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if axis is None:
norm = np.sum(np.abs(n... | null |
149,161 | import numpy as np
from typing import Optional
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
def l2_normalize(
x: np.ndarray,
/,
*,
axis: Optional[int] = None,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if axis is None:
denorm = np.linalg.nor... | null |
149,162 | import numpy as np
from typing import Optional
from ivy.func_wrapper import with_unsupported_dtypes
from . import backend_version
def lp_normalize(
x: np.ndarray,
/,
*,
p: float = 2,
axis: Optional[int] = None,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if axis is None:
deno... | null |
149,163 | import logging
import ivy
from ivy.functional.ivy.experimental.sparse_array import (
_is_valid_format,
_verify_bsc_components,
_verify_bsr_components,
_verify_coo_components,
_verify_csc_components,
_verify_csr_components,
)
The provided code snippet includes necessary dependencies for implemen... | Numpy does not support sparse arrays natively. |
149,164 | import logging
import ivy
from ivy.functional.ivy.experimental.sparse_array import (
_is_valid_format,
_verify_bsc_components,
_verify_bsr_components,
_verify_coo_components,
_verify_csc_components,
_verify_csr_components,
)
def _verify_coo_components(indices=None, values=None, dense_shape=None... | null |
149,165 | import logging
import ivy
from ivy.functional.ivy.experimental.sparse_array import (
_is_valid_format,
_verify_bsc_components,
_verify_bsr_components,
_verify_coo_components,
_verify_csc_components,
_verify_csr_components,
)
def native_sparse_array_to_indices_values_and_shape(x):
logging.wa... | null |
149,166 | from typing import Optional, Tuple
import numpy as np
from ivy.func_wrapper import with_supported_dtypes
from . import backend_version
def unravel_index(
indices: np.ndarray,
shape: Tuple[int],
/,
*,
out: Optional[np.ndarray] = None,
) -> Tuple[np.ndarray]:
ret = np.asarray(np.unravel_index(ind... | null |
149,167 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,168 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,169 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,170 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,171 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,172 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,173 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,174 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,175 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,176 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,177 | import math
from typing import Optional, Tuple, Sequence, Union, Any
import numpy as np
import ivy
from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
from ivy.utils.exceptions import IvyNotImplementedException
from .. import backend_version
from ivy.functional.ivy.experimental.linear_algebra im... | null |
149,178 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def vorbis_window(
window_length: np.ndarray,
*,
dtype: np.dtype = np.float32,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
result = []
for i in range(1, window_length * 2, 2):
temp = np.sin(ivy.pi /... | null |
149,179 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def tril_indices(
n_rows: int,
n_cols: Optional[int] = None,
k: int = 0,
/,
*,
device: Optional[str] = None,
) -> Tuple[np.ndarray, ...]:
return tuple(np.asarray(np.tril_indices(n=n_rows, k=k, m=n_cols))) | null |
149,180 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def hann_window(
size: int,
/,
*,
periodic: bool = True,
dtype: Optional[np.dtype] = None,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if size < 2:
return np.ones([size], dtype=dtype)
if per... | null |
149,181 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def kaiser_window(
window_length: int,
periodic: bool = True,
beta: float = 12.0,
*,
dtype: Optional[np.dtype] = None,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if window_length < 2:
return np... | null |
149,182 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def indices(
dimensions: Sequence,
dtype: np.dtype = np.int64,
sparse: bool = False,
) -> Union[np.ndarray, Tuple[np.ndarray, ...]]:
return np.indices(dimensions, dtype=dtype, sparse=sparse) | null |
149,183 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def unsorted_segment_min(
data: np.ndarray,
segment_ids: np.ndarray,
num_segments: int,
) -> np.ndarray:
ivy.utils.assertions.check_unsorted_segment_valid_params(
data, segment_ids, num_segments
)
if data... | null |
149,184 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def blackman_window(
size: int,
/,
*,
periodic: bool = True,
dtype: Optional[np.dtype] = None,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if size < 2:
return np.ones([size], dtype=dtype)
if... | null |
149,185 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def unsorted_segment_sum(
data: np.ndarray,
segment_ids: np.ndarray,
num_segments: int,
) -> np.ndarray:
# Used the same check which is used for unsorted_segment_min as the
# check should be same
# Might require t... | null |
149,186 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def trilu(
x: np.ndarray,
/,
*,
k: int = 0,
upper: bool = True,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if upper:
return np.triu(x, k)
return np.tril(x, k) | null |
149,187 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def mel_weight_matrix(
num_mel_bins: int,
dft_length: int,
sample_rate: int,
lower_edge_hertz: float = 125.0,
upper_edge_hertz: float = 3000.0,
):
lower_edge_hertz = np.array(lower_edge_hertz)
upper_edge_hertz... | null |
149,188 | from typing import Optional, Tuple, Sequence, Union
import numpy as np
import ivy
def unsorted_segment_mean(
data: np.ndarray,
segment_ids: np.ndarray,
num_segments: int,
) -> np.ndarray:
ivy.utils.assertions.check_unsorted_segment_valid_params(
data, segment_ids, num_segments
)
if len... | null |
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