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


            

Reported by Pylint.

              import numpy as np
import torch

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


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

            

Reported by Pylint.

              ))


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

            

Reported by Pylint.

                              ],

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

            

Reported by Pylint.

              import enum

# Quantization type (dynamic quantization, static quantization).
# Should match the c++ enum in quantization_type.h
class QuantType(enum.IntEnum):
    DYNAMIC = 0
    STATIC = 1
    QAT = 2
    WEIGHT_ONLY = 3

            

Reported by Pylint.

              
# Quantization type (dynamic quantization, static quantization).
# Should match the c++ enum in quantization_type.h
class QuantType(enum.IntEnum):
    DYNAMIC = 0
    STATIC = 1
    QAT = 2
    WEIGHT_ONLY = 3


            

Reported by Pylint.

                  WEIGHT_ONLY = 3


def quant_type_to_str(quant_type):
    m = {
        QuantType.STATIC: "static",
        QuantType.DYNAMIC: "dynamic",
        QuantType.QAT: "qat",
        QuantType.WEIGHT_ONLY: "weight_only",

            

Reported by Pylint.

              

def quant_type_to_str(quant_type):
    m = {
        QuantType.STATIC: "static",
        QuantType.DYNAMIC: "dynamic",
        QuantType.QAT: "qat",
        QuantType.WEIGHT_ONLY: "weight_only",
    }

            

Reported by Pylint.

              REGULAR_SIZES.sort()

class SpectralOpFuzzer(benchmark.Fuzzer):
    def __init__(self, *, seed: int, dtype=torch.float64,
                 cuda: bool = False, probability_regular: float = 1.0):
        super().__init__(
            parameters=[
                # Dimensionality of x. (e.g. 1D, 2D, or 3D.)
                FuzzedParameter("ndim", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),

            

Reported by Pylint.

              import math

import torch
from torch.utils import benchmark
from torch.utils.benchmark import FuzzedParameter, FuzzedTensor, ParameterAlias


__all__ = ['SpectralOpFuzzer']


            

Reported by Pylint.

              MIN_DIM_SIZE = 16
MAX_DIM_SIZE = 16 * 1024

def power_range(upper_bound, base):
    return (base ** i for i in range(int(math.log(upper_bound, base)) + 1))

# List of regular numbers from MIN_DIM_SIZE to MAX_DIM_SIZE
# These numbers factorize into multiples of prime factors 2, 3, and 5 only
# and are usually the fastest in FFT implementations.

            

Reported by Pylint.

                              REGULAR_SIZES.append(ijk)
REGULAR_SIZES.sort()

class SpectralOpFuzzer(benchmark.Fuzzer):
    def __init__(self, *, seed: int, dtype=torch.float64,
                 cuda: bool = False, probability_regular: float = 1.0):
        super().__init__(
            parameters=[
                # Dimensionality of x. (e.g. 1D, 2D, or 3D.)

            

Reported by Pylint.

              from .activation import MultiheadAttention
from .rnn import LSTM
from .rnn import LSTMCell

__all__ = [
    'LSTM',
    'LSTMCell',
    'MultiheadAttention',
]

            

Reported by Pylint.

              from .activation import MultiheadAttention
from .rnn import LSTM
from .rnn import LSTMCell

__all__ = [
    'LSTM',
    'LSTMCell',
    'MultiheadAttention',
]

            

Reported by Pylint.

              from .activation import MultiheadAttention
from .rnn import LSTM
from .rnn import LSTMCell

__all__ = [
    'LSTM',
    'LSTMCell',
    'MultiheadAttention',
]

            

Reported by Pylint.

              from .activation import MultiheadAttention
from .rnn import LSTM
from .rnn import LSTMCell

__all__ = [
    'LSTM',
    'LSTMCell',
    'MultiheadAttention',
]

            

Reported by Pylint.

              

class BinaryOpFuzzer(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", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),


            

Reported by Pylint.

              import numpy as np
import torch

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


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

            

Reported by Pylint.

              ))


class BinaryOpFuzzer(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", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),

            

Reported by Pylint.

                              ],

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

            

Reported by Pylint.

              import torch  # noqa: F401


def div_int_nofuture():
    return 1 / 2


def div_float_nofuture():
    return 3.14 / 0.125

            

Reported by Pylint.

              import torch  # noqa: F401


def div_int_nofuture():
    return 1 / 2


def div_float_nofuture():
    return 3.14 / 0.125

            

Reported by Pylint.

              import torch  # noqa: F401


def div_int_nofuture():
    return 1 / 2


def div_float_nofuture():
    return 3.14 / 0.125

            

Reported by Pylint.

                  return 1 / 2


def div_float_nofuture():
    return 3.14 / 0.125

            

Reported by Pylint.

                  [('start_node', Node), ('end_node', Node), ('base_op_node', Node)]
)

# TODO(future PR): see if we can use typing_extensions's TypedDict instead
# to properly type the various keys
# {
#   # one of NSSingleResultValuesType
#   'type': 'weight',
#   # the values of type specified above

            

Reported by Pylint.

              import enum
from typing import NamedTuple

from torch.fx.graph import Node

from typing import Dict, Any, List, Union, Callable

class NSSingleResultValuesType(str, enum.Enum):
    WEIGHT = 'weight'

            

Reported by Pylint.

              
from torch.fx.graph import Node

from typing import Dict, Any, List, Union, Callable

class NSSingleResultValuesType(str, enum.Enum):
    WEIGHT = 'weight'
    NODE_OUTPUT = 'node_output'
    NODE_INPUT = 'node_input'

            

Reported by Pylint.

              
from typing import Dict, Any, List, Union, Callable

class NSSingleResultValuesType(str, enum.Enum):
    WEIGHT = 'weight'
    NODE_OUTPUT = 'node_output'
    NODE_INPUT = 'node_input'

NSSubgraph = NamedTuple(

            

Reported by Pylint.

              from .join import Join
from .join import Joinable
from .join import JoinHook

            

Reported by Pylint.

              from .join import Join
from .join import Joinable
from .join import JoinHook

            

Reported by Pylint.

              from .join import Join
from .join import Joinable
from .join import JoinHook

            

Reported by Pylint.

              from .join import Join
from .join import Joinable
from .join import JoinHook

            

Reported by Pylint.

              

@contextmanager
def range(msg, *args, **kwargs):
    """
    Context manager / decorator that pushes an NVTX range at the beginning
    of its scope, and pops it at the end. If extra arguments are given,
    they are passed as arguments to msg.format().


            

Reported by Pylint.

              from contextlib import contextmanager

try:
    from torch._C import _nvtx
except ImportError:
    class _NVTXStub(object):
        @staticmethod
        def _fail(*args, **kwargs):
            raise RuntimeError("NVTX functions not installed. Are you sure you have a CUDA build?")

            

Reported by Pylint.

              try:
    from torch._C import _nvtx
except ImportError:
    class _NVTXStub(object):
        @staticmethod
        def _fail(*args, **kwargs):
            raise RuntimeError("NVTX functions not installed. Are you sure you have a CUDA build?")

        rangePushA = _fail

            

Reported by Pylint.

              try:
    from torch._C import _nvtx
except ImportError:
    class _NVTXStub(object):
        @staticmethod
        def _fail(*args, **kwargs):
            raise RuntimeError("NVTX functions not installed. Are you sure you have a CUDA build?")

        rangePushA = _fail

            

Reported by Pylint.

              import os
from install.download_bin import download, PYTORCH_ROOT  # type: ignore[import]

# This dictionary maps each platform to the S3 object URL for its clang-format binary.
PLATFORM_TO_CF_URL = {
    "Darwin": "https://oss-clang-format.s3.us-east-2.amazonaws.com/mac/clang-format-mojave",
    "Linux": "https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/clang-format-linux64",
}


            

Reported by Pylint.

              CLANG_FORMAT_DIR = os.path.join(PYTORCH_ROOT, ".clang-format-bin")
CLANG_FORMAT_PATH = os.path.join(CLANG_FORMAT_DIR, "clang-format")

def get_and_check_clang_format(verbose: bool = False) -> bool:
    return bool(download("clang-format", CLANG_FORMAT_DIR, PLATFORM_TO_CF_URL, PLATFORM_TO_HASH))

            

Reported by Pylint.

              import os
from install.download_bin import download, PYTORCH_ROOT  # type: ignore[import]

# This dictionary maps each platform to the S3 object URL for its clang-format binary.
PLATFORM_TO_CF_URL = {
    "Darwin": "https://oss-clang-format.s3.us-east-2.amazonaws.com/mac/clang-format-mojave",
    "Linux": "https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/clang-format-linux64",
}


            

Reported by Pylint.

              CLANG_FORMAT_DIR = os.path.join(PYTORCH_ROOT, ".clang-format-bin")
CLANG_FORMAT_PATH = os.path.join(CLANG_FORMAT_DIR, "clang-format")

def get_and_check_clang_format(verbose: bool = False) -> bool:
    return bool(download("clang-format", CLANG_FORMAT_DIR, PLATFORM_TO_CF_URL, PLATFORM_TO_HASH))

            

Reported by Pylint.