import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from caffe2.python import core, utils
from hypothesis import given, settings


class OrderSwitchOpsTest(hu.HypothesisTestCase):

            

Reported by Pylint.

              import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from caffe2.python import core, utils
from hypothesis import given, settings


class OrderSwitchOpsTest(hu.HypothesisTestCase):
    @given(
        X=hu.tensor(min_dim=3, max_dim=5, min_value=1, max_value=5),

            

Reported by Pylint.

              

import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from caffe2.python import core, utils
from hypothesis import given, settings


class OrderSwitchOpsTest(hu.HypothesisTestCase):

            

Reported by Pylint.

              from hypothesis import given, settings


class OrderSwitchOpsTest(hu.HypothesisTestCase):
    @given(
        X=hu.tensor(min_dim=3, max_dim=5, min_value=1, max_value=5),
        engine=st.sampled_from(["", "CUDNN"]),
        **hu.gcs
    )

            

Reported by Pylint.

                      engine=st.sampled_from(["", "CUDNN"]),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_nchw2nhwc(self, X, engine, gc, dc):
        op = core.CreateOperator("NCHW2NHWC", ["X"], ["Y"], engine=engine)

        def nchw2nhwc_ref(X):
            return (utils.NCHW2NHWC(X),)

            

Reported by Pylint.

                      engine=st.sampled_from(["", "CUDNN"]),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_nchw2nhwc(self, X, engine, gc, dc):
        op = core.CreateOperator("NCHW2NHWC", ["X"], ["Y"], engine=engine)

        def nchw2nhwc_ref(X):
            return (utils.NCHW2NHWC(X),)

            

Reported by Pylint.

                      engine=st.sampled_from(["", "CUDNN"]),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_nchw2nhwc(self, X, engine, gc, dc):
        op = core.CreateOperator("NCHW2NHWC", ["X"], ["Y"], engine=engine)

        def nchw2nhwc_ref(X):
            return (utils.NCHW2NHWC(X),)

            

Reported by Pylint.

                      engine=st.sampled_from(["", "CUDNN"]),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_nchw2nhwc(self, X, engine, gc, dc):
        op = core.CreateOperator("NCHW2NHWC", ["X"], ["Y"], engine=engine)

        def nchw2nhwc_ref(X):
            return (utils.NCHW2NHWC(X),)

            

Reported by Pylint.

                  )
    @settings(deadline=10000)
    def test_nchw2nhwc(self, X, engine, gc, dc):
        op = core.CreateOperator("NCHW2NHWC", ["X"], ["Y"], engine=engine)

        def nchw2nhwc_ref(X):
            return (utils.NCHW2NHWC(X),)

        self.assertReferenceChecks(gc, op, [X], nchw2nhwc_ref)

            

Reported by Pylint.

                  def test_nchw2nhwc(self, X, engine, gc, dc):
        op = core.CreateOperator("NCHW2NHWC", ["X"], ["Y"], engine=engine)

        def nchw2nhwc_ref(X):
            return (utils.NCHW2NHWC(X),)

        self.assertReferenceChecks(gc, op, [X], nchw2nhwc_ref)
        self.assertGradientChecks(gc, op, [X], 0, [0])
        self.assertDeviceChecks(dc, op, [X], [0])

            

Reported by Pylint.

              import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given, settings
import hypothesis.strategies as st


class LpnormTest(hu.HypothesisTestCase):
    @given(inputs=hu.tensors(n=1,

            

Reported by Pylint.

              from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given, settings
import hypothesis.strategies as st


class LpnormTest(hu.HypothesisTestCase):
    @given(inputs=hu.tensors(n=1,
                             min_dim=1,

            

Reported by Pylint.

              




import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given, settings

            

Reported by Pylint.

              import hypothesis.strategies as st


class LpnormTest(hu.HypothesisTestCase):
    @given(inputs=hu.tensors(n=1,
                             min_dim=1,
                             max_dim=3,
                             dtype=np.float32),
           **hu.gcs)

            

Reported by Pylint.

                                           dtype=np.float32),
           **hu.gcs)
    @settings(deadline=10000)
    def test_Lp_Norm(self, inputs, gc, dc):
        X = inputs[0]
        # avoid kinks by moving away from 0
        X += 0.02 * np.sign(X)
        X[X == 0.0] += 0.02
        self.ws.create_blob("X").feed(X)

            

Reported by Pylint.

                                           dtype=np.float32),
           **hu.gcs)
    @settings(deadline=10000)
    def test_Lp_Norm(self, inputs, gc, dc):
        X = inputs[0]
        # avoid kinks by moving away from 0
        X += 0.02 * np.sign(X)
        X[X == 0.0] += 0.02
        self.ws.create_blob("X").feed(X)

            

Reported by Pylint.

                                           dtype=np.float32),
           **hu.gcs)
    @settings(deadline=10000)
    def test_Lp_Norm(self, inputs, gc, dc):
        X = inputs[0]
        # avoid kinks by moving away from 0
        X += 0.02 * np.sign(X)
        X[X == 0.0] += 0.02
        self.ws.create_blob("X").feed(X)

            

Reported by Pylint.

                                           dtype=np.float32),
           **hu.gcs)
    @settings(deadline=10000)
    def test_Lp_Norm(self, inputs, gc, dc):
        X = inputs[0]
        # avoid kinks by moving away from 0
        X += 0.02 * np.sign(X)
        X[X == 0.0] += 0.02
        self.ws.create_blob("X").feed(X)

            

Reported by Pylint.

                         **hu.gcs)
    @settings(deadline=10000)
    def test_Lp_Norm(self, inputs, gc, dc):
        X = inputs[0]
        # avoid kinks by moving away from 0
        X += 0.02 * np.sign(X)
        X[X == 0.0] += 0.02
        self.ws.create_blob("X").feed(X)
        op = core.CreateOperator(

            

Reported by Pylint.

                  def test_Lp_Norm(self, inputs, gc, dc):
        X = inputs[0]
        # avoid kinks by moving away from 0
        X += 0.02 * np.sign(X)
        X[X == 0.0] += 0.02
        self.ws.create_blob("X").feed(X)
        op = core.CreateOperator(
            'LpNorm',
            ['X'],

            

Reported by Pylint.

              
from caffe2.python import core
from functools import partial
from hypothesis import strategies as st

import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import math
import numpy as np

            

Reported by Pylint.

              def _data_and_scale(
        data_min_size=4, data_max_size=10,
        examples_min_number=1, examples_max_number=4,
        dtype=np.float32, elements=None):
    params_ = st.tuples(
        st.integers(min_value=examples_min_number,
                    max_value=examples_max_number),
        st.integers(min_value=data_min_size,
                    max_value=data_max_size),

            

Reported by Pylint.

                  return (output, )


def grad(output_grad, ref_outputs, inputs):
    return (divide_by_square_root(output_grad, inputs[1])[0],
            None)


class TestSquareRootDivide(serial.SerializedTestCase):

            

Reported by Pylint.

              class TestSquareRootDivide(serial.SerializedTestCase):
    @serial.given(data_and_scale=_data_and_scale(),
           **hu.gcs_cpu_only)
    def test_square_root_divide(self, data_and_scale, gc, dc):
        self.assertReferenceChecks(
            device_option=gc,
            op=core.CreateOperator("SquareRootDivide",
                                   ["data", "scale"],
                                   ["output"]),

            

Reported by Pylint.

              




from caffe2.python import core
from functools import partial
from hypothesis import strategies as st


            

Reported by Pylint.

              

from caffe2.python import core
from functools import partial
from hypothesis import strategies as st

import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import math

            

Reported by Pylint.

              
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import math
import numpy as np


def _data_and_scale(
        data_min_size=4, data_max_size=10,

            

Reported by Pylint.

              import numpy as np


def _data_and_scale(
        data_min_size=4, data_max_size=10,
        examples_min_number=1, examples_max_number=4,
        dtype=np.float32, elements=None):
    params_ = st.tuples(
        st.integers(min_value=examples_min_number,

            

Reported by Pylint.

                  )


def divide_by_square_root(data, scale):
    output = np.copy(data)
    num_examples = len(scale)

    assert num_examples == data.shape[0]
    assert len(data.shape) == 2

            

Reported by Pylint.

                  output = np.copy(data)
    num_examples = len(scale)

    assert num_examples == data.shape[0]
    assert len(data.shape) == 2

    for i in range(0, num_examples):
        if scale[i] > 0:
            output[i] = np.multiply(data[i], 1 / math.sqrt(scale[i]))

            

Reported by Bandit.

              


from hypothesis import given, settings
import numpy as np

from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu


            

Reported by Pylint.

                      **hu.gcs
    )
    @settings(deadline=10000)
    def test_enforce_finite(self, X, gc, dc):

        def all_finite_value(X):
            if X.size <= 0:
                return True


            

Reported by Pylint.

                      **hu.gcs
    )
    @settings(deadline=10000)
    def test_enforce_finite(self, X, gc, dc):

        def all_finite_value(X):
            if X.size <= 0:
                return True


            

Reported by Pylint.

                      ),
        **hu.gcs
    )
    def test_enforce_finite_device_check(self, X, gc, dc):
        op = core.CreateOperator(
            "EnforceFinite",
            ["X"],
            [],
        )

            

Reported by Pylint.

              




from hypothesis import given, settings
import numpy as np

from caffe2.python import core, workspace

            

Reported by Pylint.

              import caffe2.python.hypothesis_test_util as hu


class TestEnforceFinite(hu.HypothesisTestCase):
    @given(
        X=hu.tensor(
            # allow empty
            min_value=0,
            elements=hu.floats(allow_nan=True, allow_infinity=True),

            

Reported by Pylint.

                      ),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_enforce_finite(self, X, gc, dc):

        def all_finite_value(X):
            if X.size <= 0:
                return True

            

Reported by Pylint.

                      ),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_enforce_finite(self, X, gc, dc):

        def all_finite_value(X):
            if X.size <= 0:
                return True

            

Reported by Pylint.

                      ),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_enforce_finite(self, X, gc, dc):

        def all_finite_value(X):
            if X.size <= 0:
                return True

            

Reported by Pylint.

                      ),
        **hu.gcs
    )
    @settings(deadline=10000)
    def test_enforce_finite(self, X, gc, dc):

        def all_finite_value(X):
            if X.size <= 0:
                return True

            

Reported by Pylint.

              import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial

from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np


class TestMarginRankingCriterion(serial.SerializedTestCase):

            

Reported by Pylint.

              import caffe2.python.serialized_test.serialized_test_util as serial

from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np


class TestMarginRankingCriterion(serial.SerializedTestCase):
    @given(N=st.integers(min_value=10, max_value=20),

            

Reported by Pylint.

              




from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial


            

Reported by Pylint.

              import numpy as np


class TestMarginRankingCriterion(serial.SerializedTestCase):
    @given(N=st.integers(min_value=10, max_value=20),
           seed=st.integers(min_value=0, max_value=65535),
           margin=st.floats(min_value=-0.5, max_value=0.5),
           **hu.gcs)
    @settings(deadline=10000)

            

Reported by Pylint.

                         margin=st.floats(min_value=-0.5, max_value=0.5),
           **hu.gcs)
    @settings(deadline=10000)
    def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
        np.random.seed(seed)
        X1 = np.random.randn(N).astype(np.float32)
        X2 = np.random.randn(N).astype(np.float32)
        Y = np.random.choice([-1, 1], size=N).astype(np.int32)
        op = core.CreateOperator(

            

Reported by Pylint.

                         margin=st.floats(min_value=-0.5, max_value=0.5),
           **hu.gcs)
    @settings(deadline=10000)
    def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
        np.random.seed(seed)
        X1 = np.random.randn(N).astype(np.float32)
        X2 = np.random.randn(N).astype(np.float32)
        Y = np.random.choice([-1, 1], size=N).astype(np.int32)
        op = core.CreateOperator(

            

Reported by Pylint.

                         margin=st.floats(min_value=-0.5, max_value=0.5),
           **hu.gcs)
    @settings(deadline=10000)
    def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
        np.random.seed(seed)
        X1 = np.random.randn(N).astype(np.float32)
        X2 = np.random.randn(N).astype(np.float32)
        Y = np.random.choice([-1, 1], size=N).astype(np.int32)
        op = core.CreateOperator(

            

Reported by Pylint.

                         margin=st.floats(min_value=-0.5, max_value=0.5),
           **hu.gcs)
    @settings(deadline=10000)
    def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
        np.random.seed(seed)
        X1 = np.random.randn(N).astype(np.float32)
        X2 = np.random.randn(N).astype(np.float32)
        Y = np.random.choice([-1, 1], size=N).astype(np.int32)
        op = core.CreateOperator(

            

Reported by Pylint.

                         margin=st.floats(min_value=-0.5, max_value=0.5),
           **hu.gcs)
    @settings(deadline=10000)
    def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
        np.random.seed(seed)
        X1 = np.random.randn(N).astype(np.float32)
        X2 = np.random.randn(N).astype(np.float32)
        Y = np.random.choice([-1, 1], size=N).astype(np.int32)
        op = core.CreateOperator(

            

Reported by Pylint.

                  @settings(deadline=10000)
    def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
        np.random.seed(seed)
        X1 = np.random.randn(N).astype(np.float32)
        X2 = np.random.randn(N).astype(np.float32)
        Y = np.random.choice([-1, 1], size=N).astype(np.int32)
        op = core.CreateOperator(
            "MarginRankingCriterion", ["X1", "X2", "Y"], ["loss"],
            margin=margin)

            

Reported by Pylint.

              # LICENSE file in the root directory of this source tree.
import copy

from torch import nn

from torch.distributed.pipeline.sync.skip import Namespace, skippable, stash


def test_namespace_difference():

            

Reported by Pylint.

              
from torch import nn

from torch.distributed.pipeline.sync.skip import Namespace, skippable, stash


def test_namespace_difference():
    ns1 = Namespace()
    ns2 = Namespace()

            

Reported by Pylint.

              # Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import copy

from torch import nn

            

Reported by Pylint.

              from torch.distributed.pipeline.sync.skip import Namespace, skippable, stash


def test_namespace_difference():
    ns1 = Namespace()
    ns2 = Namespace()
    assert ns1 != ns2



            

Reported by Pylint.

              def test_namespace_difference():
    ns1 = Namespace()
    ns2 = Namespace()
    assert ns1 != ns2


def test_namespace_copy():
    ns = Namespace()
    assert copy.copy(ns) == ns

            

Reported by Bandit.

                  assert ns1 != ns2


def test_namespace_copy():
    ns = Namespace()
    assert copy.copy(ns) == ns
    assert copy.copy(ns) is not ns



            

Reported by Pylint.

              

def test_namespace_copy():
    ns = Namespace()
    assert copy.copy(ns) == ns
    assert copy.copy(ns) is not ns


def test_skippable_repr():

            

Reported by Pylint.

              
def test_namespace_copy():
    ns = Namespace()
    assert copy.copy(ns) == ns
    assert copy.copy(ns) is not ns


def test_skippable_repr():
    @skippable(stash=["hello"])

            

Reported by Bandit.

              def test_namespace_copy():
    ns = Namespace()
    assert copy.copy(ns) == ns
    assert copy.copy(ns) is not ns


def test_skippable_repr():
    @skippable(stash=["hello"])
    class Hello(nn.Module):

            

Reported by Bandit.

                  assert copy.copy(ns) is not ns


def test_skippable_repr():
    @skippable(stash=["hello"])
    class Hello(nn.Module):
        def __init__(self):
            super().__init__()
            self.conv = nn.Conv2d(1, 1, 1)

            

Reported by Pylint.

              """
import torch
import sys
from .reductions import init_reductions
import multiprocessing

__all__ = ['set_sharing_strategy', 'get_sharing_strategy',
           'get_all_sharing_strategies']


            

Reported by Pylint.

              
"""Add helper function to spawn N processes and wait for completion of any of
them. This depends `mp.get_context` which was added in Python 3.4."""
from .spawn import spawn, SpawnContext, start_processes, ProcessContext, \
    ProcessRaisedException, ProcessExitedException


if sys.platform == 'darwin' or sys.platform == 'win32':
    _sharing_strategy = 'file_system'

            

Reported by Pylint.

                         'get_all_sharing_strategies']


from multiprocessing import *  # noqa: F403


__all__ += multiprocessing.__all__  # type: ignore[attr-defined]



            

Reported by Pylint.

              
# This call adds a Linux specific prctl(2) wrapper function to this module.
# See https://github.com/pytorch/pytorch/pull/14391 for more information.
torch._C._multiprocessing_init()


"""Add helper function to spawn N processes and wait for completion of any of
them. This depends `mp.get_context` which was added in Python 3.4."""
from .spawn import spawn, SpawnContext, start_processes, ProcessContext, \

            

Reported by Pylint.

              
# This call adds a Linux specific prctl(2) wrapper function to this module.
# See https://github.com/pytorch/pytorch/pull/14391 for more information.
torch._C._multiprocessing_init()


"""Add helper function to spawn N processes and wait for completion of any of
them. This depends `mp.get_context` which was added in Python 3.4."""
from .spawn import spawn, SpawnContext, start_processes, ProcessContext, \

            

Reported by Pylint.

              torch._C._multiprocessing_init()


"""Add helper function to spawn N processes and wait for completion of any of
them. This depends `mp.get_context` which was added in Python 3.4."""
from .spawn import spawn, SpawnContext, start_processes, ProcessContext, \
    ProcessRaisedException, ProcessExitedException



            

Reported by Pylint.

                      new_strategy (str): Name of the selected strategy. Should be one of
            the values returned by :func:`get_all_sharing_strategies()`.
    """
    global _sharing_strategy
    assert new_strategy in _all_sharing_strategies
    _sharing_strategy = new_strategy


def get_sharing_strategy():

            

Reported by Pylint.

              contents, and we recommend referring to very good docs of the original module.
"""
import torch
import sys
from .reductions import init_reductions
import multiprocessing

__all__ = ['set_sharing_strategy', 'get_sharing_strategy',
           'get_all_sharing_strategies']

            

Reported by Pylint.

              import torch
import sys
from .reductions import init_reductions
import multiprocessing

__all__ = ['set_sharing_strategy', 'get_sharing_strategy',
           'get_all_sharing_strategies']



            

Reported by Pylint.

                         'get_all_sharing_strategies']


from multiprocessing import *  # noqa: F403


__all__ += multiprocessing.__all__  # type: ignore[attr-defined]



            

Reported by Pylint.

              

# Matching method matches the attribute name of current version to the attribute name of `target_version`
def default_matching(name: str, target_version: int) -> str:
    """Default matching method
    """
    return name

# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering.

            

Reported by Pylint.

              
    if type(mod) in module_fetch_book:
        version, param_to_fetch, matching_method = module_fetch_book[type(mod)]
        if version < mod._version:
            raise RuntimeError(f"Fetcher version {version} try to fetch {torch.typename(mod)} version {mod._version}, "
                               "please upgrade the module_fetch_book, open an issue and @842974287 "
                               "or report a bug to AIACC team directly.")
        for attr in param_to_fetch:
            attrs_for_lowering[attr] = getattr(mod, matching_method(attr, mod._version))

            

Reported by Pylint.

                  if type(mod) in module_fetch_book:
        version, param_to_fetch, matching_method = module_fetch_book[type(mod)]
        if version < mod._version:
            raise RuntimeError(f"Fetcher version {version} try to fetch {torch.typename(mod)} version {mod._version}, "
                               "please upgrade the module_fetch_book, open an issue and @842974287 "
                               "or report a bug to AIACC team directly.")
        for attr in param_to_fetch:
            attrs_for_lowering[attr] = getattr(mod, matching_method(attr, mod._version))
    else:

            

Reported by Pylint.

                                             "please upgrade the module_fetch_book, open an issue and @842974287 "
                               "or report a bug to AIACC team directly.")
        for attr in param_to_fetch:
            attrs_for_lowering[attr] = getattr(mod, matching_method(attr, mod._version))
    else:
        raise RuntimeError(f"{torch.typename(mod)} is not in the module_fetch_book yet, "
                           "please add it to the module_fetch_book, open an issue and @842974287 "
                           "or report a bug to AIACC team directly.")
    return attrs_for_lowering

            

Reported by Pylint.

              from torch.fx.graph_module import GraphModule
from typing import Any, Callable, Dict, List, Tuple, Type
import torch
import torch.nn as nn


# Matching method matches the attribute name of current version to the attribute name of `target_version`
def default_matching(name: str, target_version: int) -> str:
    """Default matching method

            

Reported by Pylint.

              from torch.fx.graph_module import GraphModule
from typing import Any, Callable, Dict, List, Tuple, Type
import torch
import torch.nn as nn


# Matching method matches the attribute name of current version to the attribute name of `target_version`
def default_matching(name: str, target_version: int) -> str:
    """Default matching method

            

Reported by Pylint.

              import torch.nn as nn


# Matching method matches the attribute name of current version to the attribute name of `target_version`
def default_matching(name: str, target_version: int) -> str:
    """Default matching method
    """
    return name


            

Reported by Pylint.

                  """
    return name

# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering.
# The first integer in the tuple is the version number of the nn.Module class when we create the parameter list.
# If there's a version mismatch then it means the parameter names in the book might be mismatched with nn.Module.
module_fetch_book: Dict[Type, Tuple[int, List[str], Callable[[str, int], str]]] = {
    torch.nn.modules.linear.Linear: (1, ["weight", "bias"], default_matching),
    torch.nn.modules.conv.Conv2d: (

            

Reported by Pylint.

                  return name

# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering.
# The first integer in the tuple is the version number of the nn.Module class when we create the parameter list.
# If there's a version mismatch then it means the parameter names in the book might be mismatched with nn.Module.
module_fetch_book: Dict[Type, Tuple[int, List[str], Callable[[str, int], str]]] = {
    torch.nn.modules.linear.Linear: (1, ["weight", "bias"], default_matching),
    torch.nn.modules.conv.Conv2d: (
        1, ["weight", "bias", "kernel_size", "stride", "padding", "dilation", "groups", "padding_mode"], default_matching

            

Reported by Pylint.

              
# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering.
# The first integer in the tuple is the version number of the nn.Module class when we create the parameter list.
# If there's a version mismatch then it means the parameter names in the book might be mismatched with nn.Module.
module_fetch_book: Dict[Type, Tuple[int, List[str], Callable[[str, int], str]]] = {
    torch.nn.modules.linear.Linear: (1, ["weight", "bias"], default_matching),
    torch.nn.modules.conv.Conv2d: (
        1, ["weight", "bias", "kernel_size", "stride", "padding", "dilation", "groups", "padding_mode"], default_matching
    ),

            

Reported by Pylint.

              def stub_unpickler():
    return "STUB"

# TODO(VitalyFedyunin): Make sure it works without dill module installed
def list_connected_datapipes(scan_obj):

    f = io.BytesIO()
    p = pickle.Pickler(f)  # Not going to work for lambdas, but dill infinite loops on typing and can't be used as is


            

Reported by Pylint.

                  f = io.BytesIO()
    p = pickle.Pickler(f)  # Not going to work for lambdas, but dill infinite loops on typing and can't be used as is

    def stub_pickler(obj):
        return stub_unpickler, ()

    captured_connections = []

    def reduce_hook(obj):

            

Reported by Pylint.

                  f = io.BytesIO()
    p = pickle.Pickler(f)  # Not going to work for lambdas, but dill infinite loops on typing and can't be used as is

    def stub_pickler(obj):
        return stub_unpickler, ()

    captured_connections = []

    def reduce_hook(obj):

            

Reported by Pylint.

                          captured_connections.append(obj)
            return stub_unpickler, ()

    # TODO(VitalyFedyunin):  Better do it as `with` context for safety
    IterableDataset.set_reduce_ex_hook(reduce_hook)
    p.dump(scan_obj)
    IterableDataset.set_reduce_ex_hook(None)
    return captured_connections


            

Reported by Pylint.

              import io
import pickle

from torch.utils.data import IterableDataset

from typing import Any, Dict

reduce_ex_hook = None


            

Reported by Pylint.

              import io
import pickle

from torch.utils.data import IterableDataset

from typing import Any, Dict

reduce_ex_hook = None


            

Reported by Bandit.

              
from torch.utils.data import IterableDataset

from typing import Any, Dict

reduce_ex_hook = None


def stub_unpickler():

            

Reported by Pylint.

              
from typing import Any, Dict

reduce_ex_hook = None


def stub_unpickler():
    return "STUB"


            

Reported by Pylint.

              reduce_ex_hook = None


def stub_unpickler():
    return "STUB"

# TODO(VitalyFedyunin): Make sure it works without dill module installed
def list_connected_datapipes(scan_obj):


            

Reported by Pylint.

                  return "STUB"

# TODO(VitalyFedyunin): Make sure it works without dill module installed
def list_connected_datapipes(scan_obj):

    f = io.BytesIO()
    p = pickle.Pickler(f)  # Not going to work for lambdas, but dill infinite loops on typing and can't be used as is

    def stub_pickler(obj):

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor


class PixelShuffle(Module):
    r"""Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
    to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor


class PixelShuffle(Module):
    r"""Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
    to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.

            

Reported by Pylint.

                      super(PixelShuffle, self).__init__()
        self.upscale_factor = upscale_factor

    def forward(self, input: Tensor) -> Tensor:
        return F.pixel_shuffle(input, self.upscale_factor)

    def extra_repr(self) -> str:
        return 'upscale_factor={}'.format(self.upscale_factor)


            

Reported by Pylint.

                      super(PixelUnshuffle, self).__init__()
        self.downscale_factor = downscale_factor

    def forward(self, input: Tensor) -> Tensor:
        return F.pixel_unshuffle(input, self.downscale_factor)

    def extra_repr(self) -> str:
        return 'downscale_factor={}'.format(self.downscale_factor)

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor


class PixelShuffle(Module):
    r"""Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
    to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor


class PixelShuffle(Module):
    r"""Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
    to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.

            

Reported by Pylint.

                  with a stride of :math:`1/r`.

    See the paper:
    `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
    by Shi et. al (2016) for more details.

    Args:
        upscale_factor (int): factor to increase spatial resolution by


            

Reported by Pylint.

                      >>> print(output.size())
        torch.Size([1, 1, 12, 12])

    .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
        https://arxiv.org/abs/1609.05158
    """
    __constants__ = ['upscale_factor']
    upscale_factor: int


            

Reported by Pylint.

                  upscale_factor: int

    def __init__(self, upscale_factor: int) -> None:
        super(PixelShuffle, self).__init__()
        self.upscale_factor = upscale_factor

    def forward(self, input: Tensor) -> Tensor:
        return F.pixel_shuffle(input, self.upscale_factor)


            

Reported by Pylint.

                      super(PixelShuffle, self).__init__()
        self.upscale_factor = upscale_factor

    def forward(self, input: Tensor) -> Tensor:
        return F.pixel_shuffle(input, self.upscale_factor)

    def extra_repr(self) -> str:
        return 'upscale_factor={}'.format(self.upscale_factor)


            

Reported by Pylint.