from torch._C import _get_backcompat_keepdim_warn


class Warning(object):
    def __init__(self, setter, getter):
        self.setter = setter
        self.getter = getter

    def set_enabled(self, value):

            

Reported by Pylint.

              from torch._C import _set_backcompat_broadcast_warn
from torch._C import _get_backcompat_broadcast_warn
from torch._C import _set_backcompat_keepdim_warn
from torch._C import _get_backcompat_keepdim_warn


class Warning(object):
    def __init__(self, setter, getter):
        self.setter = setter

            

Reported by Pylint.

              from torch._C import _get_backcompat_keepdim_warn


class Warning(object):
    def __init__(self, setter, getter):
        self.setter = setter
        self.getter = getter

    def set_enabled(self, value):

            

Reported by Pylint.

              from torch._C import _get_backcompat_keepdim_warn


class Warning(object):
    def __init__(self, setter, getter):
        self.setter = setter
        self.getter = getter

    def set_enabled(self, value):

            

Reported by Pylint.

                      self.setter = setter
        self.getter = getter

    def set_enabled(self, value):
        self.setter(value)

    def get_enabled(self):
        return self.getter()


            

Reported by Pylint.

                  def set_enabled(self, value):
        self.setter(value)

    def get_enabled(self):
        return self.getter()

    enabled = property(get_enabled, set_enabled)

broadcast_warning = Warning(_set_backcompat_broadcast_warn, _get_backcompat_broadcast_warn)

            

Reported by Pylint.

              from torch.utils.data import MapDataPipe, functional_datapipe
from typing import Sized, Tuple, TypeVar

T_co = TypeVar('T_co', covariant=True)


@functional_datapipe('concat')
class ConcatMapDataPipe(MapDataPipe):
    r""" :class:`ConcatMapDataPipe`.

            

Reported by Pylint.

              from torch.utils.data import MapDataPipe, functional_datapipe
from typing import Sized, Tuple, TypeVar

T_co = TypeVar('T_co', covariant=True)


@functional_datapipe('concat')
class ConcatMapDataPipe(MapDataPipe):
    r""" :class:`ConcatMapDataPipe`.

            

Reported by Pylint.

              from torch.utils.data import MapDataPipe, functional_datapipe
from typing import Sized, Tuple, TypeVar

T_co = TypeVar('T_co', covariant=True)


@functional_datapipe('concat')
class ConcatMapDataPipe(MapDataPipe):
    r""" :class:`ConcatMapDataPipe`.

            

Reported by Pylint.

              
    def __getitem__(self, index) -> T_co:
        offset = 0
        for dp in self.datapipes:
            if index - offset < len(dp):
                return dp[index - offset]
            else:
                offset += len(dp)
        raise IndexError("Index {} is out of range.".format(index))

            

Reported by Pylint.

                  def __getitem__(self, index) -> T_co:
        offset = 0
        for dp in self.datapipes:
            if index - offset < len(dp):
                return dp[index - offset]
            else:
                offset += len(dp)
        raise IndexError("Index {} is out of range.".format(index))


            

Reported by Pylint.

              # type: ignore[attr-defined]
from .core import unify, reify  # noqa: F403
from .more import unifiable  # noqa: F403
from .variable import var, isvar, vars, variables, Var  # noqa: F403

            

Reported by Pylint.

              # type: ignore[attr-defined]
from .core import unify, reify  # noqa: F403
from .more import unifiable  # noqa: F403
from .variable import var, isvar, vars, variables, Var  # noqa: F403

            

Reported by Pylint.

              # type: ignore[attr-defined]
from .core import unify, reify  # noqa: F403
from .more import unifiable  # noqa: F403
from .variable import var, isvar, vars, variables, Var  # noqa: F403

            

Reported by Pylint.

              # type: ignore[attr-defined]
from .core import unify, reify  # noqa: F403
from .more import unifiable  # noqa: F403
from .variable import var, isvar, vars, variables, Var  # noqa: F403

            

Reported by Pylint.

              # type: ignore[attr-defined]
from .core import unify, reify  # noqa: F403
from .more import unifiable  # noqa: F403
from .variable import var, isvar, vars, variables, Var  # noqa: F403

            

Reported by Pylint.

                          in_features,
            out_features,
            bias=True,
            dtype=torch.qint8):
        super().__init__(in_features, out_features, bias, dtype)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x_dequant = x.dequantize()
        weight_dequant = self._qweight.dequantize()

            

Reported by Pylint.

                      float_result = F.linear(x_dequant, weight_dequant, self._bias)
        float_result = F.relu(float_result, inplace=True)
        # NEEDFIX: we don't have dtype in the Linear module APIs right now!
        result = torch.quantize_per_tensor(
            float_result, self.scale, self.zero_point, torch.quint8)
        return result

    def _get_name(self):
        return "QuantizedLinearReLU(Reference)"

            

Reported by Pylint.

                      float_result = F.relu(float_result, inplace=True)
        # NEEDFIX: we don't have dtype in the Linear module APIs right now!
        result = torch.quantize_per_tensor(
            float_result, self.scale, self.zero_point, torch.quint8)
        return result

    def _get_name(self):
        return "QuantizedLinearReLU(Reference)"

            

Reported by Pylint.

              import torch
import torch.nn.intrinsic as nni
import torch.nn.quantized._reference as nnqr
import torch.nn.functional as F

class LinearReLU(nnqr.Linear):
    _FLOAT_MODULE = nni.LinearReLU

    def __init__(

            

Reported by Pylint.

              import torch.nn.quantized._reference as nnqr
import torch.nn.functional as F

class LinearReLU(nnqr.Linear):
    _FLOAT_MODULE = nni.LinearReLU

    def __init__(
            self,
            in_features,

            

Reported by Pylint.

              import _compat_pickle
import pickle

from .importer import Importer


class PackageUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
    """Package-aware unpickler.


            

Reported by Pylint.

              from .importer import Importer


class PackageUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
    """Package-aware unpickler.

    This behaves the same as a normal unpickler, except it uses `importer` to
    find any global names that it encounters while unpickling.
    """

            

Reported by Pylint.

              import _compat_pickle
import pickle

from .importer import Importer


class PackageUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
    """Package-aware unpickler.


            

Reported by Pylint.

              import _compat_pickle
import pickle

from .importer import Importer


class PackageUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
    """Package-aware unpickler.


            

Reported by Bandit.

              import _compat_pickle
import pickle

from .importer import Importer


class PackageUnpickler(pickle._Unpickler):  # type: ignore[name-defined]
    """Package-aware unpickler.


            

Reported by Pylint.

              from ._core import *  # noqa: F403
from ._asserts import *  # noqa: F403
from ._check_kernel_launches import *  # noqa: F403
from ._deprecated import *  # noqa: F403

            

Reported by Pylint.

              from ._core import *  # noqa: F403
from ._asserts import *  # noqa: F403
from ._check_kernel_launches import *  # noqa: F403
from ._deprecated import *  # noqa: F403

            

Reported by Pylint.

              from ._core import *  # noqa: F403
from ._asserts import *  # noqa: F403
from ._check_kernel_launches import *  # noqa: F403
from ._deprecated import *  # noqa: F403

            

Reported by Pylint.

              from ._core import *  # noqa: F403
from ._asserts import *  # noqa: F403
from ._check_kernel_launches import *  # noqa: F403
from ._deprecated import *  # noqa: F403

            

Reported by Pylint.

              from ._core import *  # noqa: F403
from ._asserts import *  # noqa: F403
from ._check_kernel_launches import *  # noqa: F403
from ._deprecated import *  # noqa: F403

            

Reported by Pylint.

              from . import iter
from . import map

            

Reported by Pylint.

              from . import iter
from . import map

            

Reported by Pylint.

              from . import iter
from . import map

            

Reported by Pylint.

              from . import iter
from . import map

            

Reported by Pylint.

              from . import iter
from . import map

            

Reported by Pylint.

              

class BinaryOpSparseFuzzer(Fuzzer):
    def __init__(self, seed, dtype=torch.float32, cuda=False):
        super().__init__(
            parameters=[
                # Dimensionality of x and y. (e.g. 1D, 2D, or 3D.)
                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
                FuzzedParameter(

            

Reported by Pylint.

              import numpy as np
import torch

from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedSparseTensor


_MIN_DIM_SIZE = 16
_MAX_DIM_SIZE = 16 * 1024 ** 2
_POW_TWO_SIZES = tuple(2 ** i for i in range(

            

Reported by Pylint.

              ))


class BinaryOpSparseFuzzer(Fuzzer):
    def __init__(self, seed, dtype=torch.float32, cuda=False):
        super().__init__(
            parameters=[
                # Dimensionality of x and y. (e.g. 1D, 2D, or 3D.)
                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),

            

Reported by Pylint.

                      super().__init__(
            parameters=[
                # Dimensionality of x and y. (e.g. 1D, 2D, or 3D.)
                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
                FuzzedParameter(
                    name="sparse_dim",
                    distribution={1: 0.4, 2: 0.4, 3: 0.2},
                    strict=True
                ),

            

Reported by Pylint.

                                  distribution={True: 0.5, False: 0.5},
                ),
                # Repeatable entropy for downstream applications.
                FuzzedParameter(name="random_value", minval=0, maxval=2 ** 32 - 1, distribution="uniform"),
            ],
            tensors=[
                FuzzedSparseTensor(
                    name="x",
                    size=("k0", "k1", "k2"),

            

Reported by Pylint.

              ))

class UnaryOpSparseFuzzer(Fuzzer):
    def __init__(self, seed, dtype=torch.float32, cuda=False):
        super().__init__(
            parameters=[
                # Sparse dim parameter of x. (e.g. 1D, 2D, or 3D.)
                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
                FuzzedParameter(

            

Reported by Pylint.

              
import numpy as np
import torch
from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedSparseTensor


_MIN_DIM_SIZE = 16
_MAX_DIM_SIZE = 16 * 1024 ** 2
_POW_TWO_SIZES = tuple(2 ** i for i in range(

            

Reported by Pylint.

                  int(np.log2(_MAX_DIM_SIZE)) + 1,
))

class UnaryOpSparseFuzzer(Fuzzer):
    def __init__(self, seed, dtype=torch.float32, cuda=False):
        super().__init__(
            parameters=[
                # Sparse dim parameter of x. (e.g. 1D, 2D, or 3D.)
                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),

            

Reported by Pylint.

                      super().__init__(
            parameters=[
                # Sparse dim parameter of x. (e.g. 1D, 2D, or 3D.)
                FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
                FuzzedParameter(
                    name="sparse_dim",
                    distribution={1: 0.4, 2: 0.4, 3: 0.2},
                    strict=True
                ),

            

Reported by Pylint.

                                  name="coalesced",
                    distribution={True: 0.5, False: 0.5},
                ),
                FuzzedParameter(name="random_value", minval=0, maxval=2 ** 32 - 1, distribution="uniform"),
            ],
            tensors=[
                FuzzedSparseTensor(
                    name="x",
                    size=("k0", "k1", "k2"),

            

Reported by Pylint.

              import collections
from itertools import repeat
from typing import List, Dict, Any


def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return tuple(x)

            

Reported by Pylint.

              from typing import List, Dict, Any


def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return tuple(x)
        return tuple(repeat(x, n))


            

Reported by Pylint.

              

def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return tuple(x)
        return tuple(repeat(x, n))

    return parse

            

Reported by Pylint.

              _quadruple = _ntuple(4)


def _reverse_repeat_tuple(t, n):
    r"""Reverse the order of `t` and repeat each element for `n` times.

    This can be used to translate padding arg used by Conv and Pooling modules
    to the ones used by `F.pad`.
    """

            

Reported by Pylint.

              _quadruple = _ntuple(4)


def _reverse_repeat_tuple(t, n):
    r"""Reverse the order of `t` and repeat each element for `n` times.

    This can be used to translate padding arg used by Conv and Pooling modules
    to the ones used by `F.pad`.
    """

            

Reported by Pylint.