import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
import unittest

from torch.testing._internal.common_utils import suppress_warnings, num_profiled_runs, run_tests

from torch.testing._internal.jit_utils import JitTestCase

            

Reported by Pylint.

              import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
import unittest

from torch.testing._internal.common_utils import suppress_warnings, num_profiled_runs, run_tests

from torch.testing._internal.jit_utils import JitTestCase

            

Reported by Pylint.

              import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
import unittest

from torch.testing._internal.common_utils import suppress_warnings, num_profiled_runs, run_tests

from torch.testing._internal.jit_utils import JitTestCase

            

Reported by Pylint.

              from torch import nn
import unittest

from torch.testing._internal.common_utils import suppress_warnings, num_profiled_runs, run_tests

from torch.testing._internal.jit_utils import JitTestCase


class BaseTestClass(JitTestCase):

            

Reported by Pylint.

              
from torch.testing._internal.common_utils import suppress_warnings, num_profiled_runs, run_tests

from torch.testing._internal.jit_utils import JitTestCase


class BaseTestClass(JitTestCase):
    def setUp(self):
        self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)

            

Reported by Pylint.

              
class BaseTestClass(JitTestCase):
    def setUp(self):
        self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)
        self.old_profiling_mode = torch._C._jit_set_profiling_mode(True)

        self.old_cpu_fuser_state = torch._C._jit_can_fuse_on_cpu()
        self.old_gpu_fuser_state = torch._C._jit_can_fuse_on_gpu()
        torch._C._jit_override_can_fuse_on_cpu(True)

            

Reported by Pylint.

              
class BaseTestClass(JitTestCase):
    def setUp(self):
        self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)
        self.old_profiling_mode = torch._C._jit_set_profiling_mode(True)

        self.old_cpu_fuser_state = torch._C._jit_can_fuse_on_cpu()
        self.old_gpu_fuser_state = torch._C._jit_can_fuse_on_gpu()
        torch._C._jit_override_can_fuse_on_cpu(True)

            

Reported by Pylint.

              class BaseTestClass(JitTestCase):
    def setUp(self):
        self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)
        self.old_profiling_mode = torch._C._jit_set_profiling_mode(True)

        self.old_cpu_fuser_state = torch._C._jit_can_fuse_on_cpu()
        self.old_gpu_fuser_state = torch._C._jit_can_fuse_on_gpu()
        torch._C._jit_override_can_fuse_on_cpu(True)
        torch._C._jit_override_can_fuse_on_gpu(True)

            

Reported by Pylint.

              class BaseTestClass(JitTestCase):
    def setUp(self):
        self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)
        self.old_profiling_mode = torch._C._jit_set_profiling_mode(True)

        self.old_cpu_fuser_state = torch._C._jit_can_fuse_on_cpu()
        self.old_gpu_fuser_state = torch._C._jit_can_fuse_on_gpu()
        torch._C._jit_override_can_fuse_on_cpu(True)
        torch._C._jit_override_can_fuse_on_gpu(True)

            

Reported by Pylint.

                      self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)
        self.old_profiling_mode = torch._C._jit_set_profiling_mode(True)

        self.old_cpu_fuser_state = torch._C._jit_can_fuse_on_cpu()
        self.old_gpu_fuser_state = torch._C._jit_can_fuse_on_gpu()
        torch._C._jit_override_can_fuse_on_cpu(True)
        torch._C._jit_override_can_fuse_on_gpu(True)
        self.texpr_fuser_state = torch._C._jit_texpr_fuser_enabled()
        torch._C._jit_set_texpr_fuser_enabled(True)

            

Reported by Pylint.

              
known_context_ids = set()

requires_grad_tensor = torch.ones(3, 3, requires_grad=True)

# Send rpc done info and context_id to
# dst_rank = (self.rank + rank_distance) % self.world_size
# we don't need a lock here since the GIL is held while executing remote
# python UDFs, so access is serialized across several workers.

            

Reported by Pylint.

              

def _torch_ones(sizes, requires_grad=False):
    return torch.ones(sizes, requires_grad=requires_grad)

# This method must be called on the rref owner, and verifies that the grad of
# rref tensor equals to the given grad.
def _compare_owner_value(context_id, rref, grad):
    grads = dist_autograd.get_gradients(context_id)

            

Reported by Pylint.

              # rref tensor equals to the given grad.
def _compare_owner_value(context_id, rref, grad):
    grads = dist_autograd.get_gradients(context_id)
    return torch.equal(grads[rref.local_value()], grad)


def create_tensor():
    return torch.ones((3, 3), requires_grad=True)


            

Reported by Pylint.

              

def create_tensor():
    return torch.ones((3, 3), requires_grad=True)


@torch.jit.script
def create_torchscript_tensor() -> torch.Tensor:
    return torch.ones((3, 3)).requires_grad_()

            

Reported by Pylint.

              
@torch.jit.script
def create_torchscript_tensor() -> torch.Tensor:
    return torch.ones((3, 3)).requires_grad_()


def my_py_add(t1, t2):
    return torch.add(t1, t2)


            

Reported by Pylint.

              

def my_py_add(t1, t2):
    return torch.add(t1, t2)


def my_scalar_add(a, b):
    return a + b


            

Reported by Pylint.

              

def my_rref_add(rref_t1, t2):
    ret = torch.add(rref_t1.local_value(), t2)
    return ret


@torch.jit.script
def my_script_add(t1, t2):

            

Reported by Pylint.

              
@torch.jit.script
def my_script_add(t1, t2):
    return torch.add(t1, t2)


@torch.jit.script
def my_script_ref_add(ref_t1: RRef[torch.Tensor], t2: torch.Tensor) -> torch.Tensor:
    t1 = ref_t1.to_here()

            

Reported by Pylint.

              @torch.jit.script
def my_script_ref_add(ref_t1: RRef[torch.Tensor], t2: torch.Tensor) -> torch.Tensor:
    t1 = ref_t1.to_here()
    return torch.add(t1, t2)


def my_nested_rref_add(dst, rref_t1, t2):
    return rpc.rpc_sync(dst, my_rref_add, args=(rref_t1, t2))


            

Reported by Pylint.

                  def _next_rank(self):
        if hasattr(self, "dst_rank"):
            self.dst_rank = (self.dst_rank + 1) % self.world_size
            if self.dst_rank == self.rank:
                return self._next_rank()
        else:
            self.dst_rank = (self.rank + 1) % self.world_size
        return self.dst_rank


            

Reported by Pylint.

              from torch._C import _infer_size, _add_docstr
from torch._torch_docs import reproducibility_notes, tf32_notes

from .._jit_internal import boolean_dispatch, _overload, BroadcastingList1, BroadcastingList2, BroadcastingList3
from ..overrides import (
    has_torch_function, has_torch_function_unary, has_torch_function_variadic,
    handle_torch_function)
from . import _reduction as _Reduction
from . import grad  # noqa: F401

            

Reported by Pylint.

              from torch._torch_docs import reproducibility_notes, tf32_notes

from .._jit_internal import boolean_dispatch, _overload, BroadcastingList1, BroadcastingList2, BroadcastingList3
from ..overrides import (
    has_torch_function, has_torch_function_unary, has_torch_function_variadic,
    handle_torch_function)
from . import _reduction as _Reduction
from . import grad  # noqa: F401
from .modules import utils

            

Reported by Pylint.

              from ..overrides import (
    has_torch_function, has_torch_function_unary, has_torch_function_variadic,
    handle_torch_function)
from . import _reduction as _Reduction
from . import grad  # noqa: F401
from .modules import utils
from .modules.utils import _single, _pair, _triple, _list_with_default



            

Reported by Pylint.

                  has_torch_function, has_torch_function_unary, has_torch_function_variadic,
    handle_torch_function)
from . import _reduction as _Reduction
from . import grad  # noqa: F401
from .modules import utils
from .modules.utils import _single, _pair, _triple, _list_with_default


Tensor = torch.Tensor

            

Reported by Pylint.

                  handle_torch_function)
from . import _reduction as _Reduction
from . import grad  # noqa: F401
from .modules import utils
from .modules.utils import _single, _pair, _triple, _list_with_default


Tensor = torch.Tensor


            

Reported by Pylint.

              from . import _reduction as _Reduction
from . import grad  # noqa: F401
from .modules import utils
from .modules.utils import _single, _pair, _triple, _list_with_default


Tensor = torch.Tensor

conv1d = _add_docstr(

            

Reported by Pylint.

              Tensor = torch.Tensor

conv1d = _add_docstr(
    torch.conv1d,
    r"""
conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 1D convolution over an input signal composed of several input
planes.

            

Reported by Pylint.

              )

conv2d = _add_docstr(
    torch.conv2d,
    r"""
conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 2D convolution over an input image composed of several input
planes.

            

Reported by Pylint.

              )  # noqa: E501

conv3d = _add_docstr(
    torch.conv3d,
    r"""
conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

Applies a 3D convolution over an input image composed of several input
planes.

            

Reported by Pylint.

              )  # noqa: E501

conv_transpose1d = _add_docstr(
    torch.conv_transpose1d,
    r"""
conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

Applies a 1D transposed convolution operator over an input signal
composed of several input planes, sometimes also called "deconvolution".

            

Reported by Pylint.

              import numpy as np

from torch.testing import floating_types_and, integral_types, complex_types, get_all_dtypes
import expecttest
from .._core import \
    (_compare_tensors_internal, _compare_scalars_internal, _compare_return_type)

import torch
import torch.cuda

            

Reported by Pylint.

              
from torch.testing import floating_types_and, integral_types, complex_types, get_all_dtypes
import expecttest
from .._core import \
    (_compare_tensors_internal, _compare_scalars_internal, _compare_return_type)

import torch
import torch.cuda
from torch._utils_internal import get_writable_path

            

Reported by Pylint.

                      assert not failed, "Some test shards have failed"
    elif TEST_SAVE_XML is not None:
        # import here so that non-CI doesn't need xmlrunner installed
        import xmlrunner  # type: ignore[import]
        test_filename = sanitize_test_filename(inspect.getfile(sys._getframe(1)))
        test_report_path = TEST_SAVE_XML + LOG_SUFFIX
        test_report_path = os.path.join(test_report_path, test_filename)
        os.makedirs(test_report_path, exist_ok=True)
        verbose = '--verbose' in argv or '-v' in argv

            

Reported by Pylint.

              
# Dict of NumPy dtype -> torch dtype (when the correspondence exists)
numpy_to_torch_dtype_dict = {
    np.bool_      : torch.bool,
    np.uint8      : torch.uint8,
    np.int8       : torch.int8,
    np.int16      : torch.int16,
    np.int32      : torch.int32,
    np.int64      : torch.int64,

            

Reported by Pylint.

              # Dict of NumPy dtype -> torch dtype (when the correspondence exists)
numpy_to_torch_dtype_dict = {
    np.bool_      : torch.bool,
    np.uint8      : torch.uint8,
    np.int8       : torch.int8,
    np.int16      : torch.int16,
    np.int32      : torch.int32,
    np.int64      : torch.int64,
    np.float16    : torch.float16,

            

Reported by Pylint.

              numpy_to_torch_dtype_dict = {
    np.bool_      : torch.bool,
    np.uint8      : torch.uint8,
    np.int8       : torch.int8,
    np.int16      : torch.int16,
    np.int32      : torch.int32,
    np.int64      : torch.int64,
    np.float16    : torch.float16,
    np.float32    : torch.float32,

            

Reported by Pylint.

                  np.bool_      : torch.bool,
    np.uint8      : torch.uint8,
    np.int8       : torch.int8,
    np.int16      : torch.int16,
    np.int32      : torch.int32,
    np.int64      : torch.int64,
    np.float16    : torch.float16,
    np.float32    : torch.float32,
    np.float64    : torch.float64,

            

Reported by Pylint.

                  np.uint8      : torch.uint8,
    np.int8       : torch.int8,
    np.int16      : torch.int16,
    np.int32      : torch.int32,
    np.int64      : torch.int64,
    np.float16    : torch.float16,
    np.float32    : torch.float32,
    np.float64    : torch.float64,
    np.complex64  : torch.complex64,

            

Reported by Pylint.

                  np.int8       : torch.int8,
    np.int16      : torch.int16,
    np.int32      : torch.int32,
    np.int64      : torch.int64,
    np.float16    : torch.float16,
    np.float32    : torch.float32,
    np.float64    : torch.float64,
    np.complex64  : torch.complex64,
    np.complex128 : torch.complex128

            

Reported by Pylint.

                  np.int16      : torch.int16,
    np.int32      : torch.int32,
    np.int64      : torch.int64,
    np.float16    : torch.float16,
    np.float32    : torch.float32,
    np.float64    : torch.float64,
    np.complex64  : torch.complex64,
    np.complex128 : torch.complex128
}

            

Reported by Pylint.

              import time
from functools import partial, reduce
from future.utils import viewitems, viewkeys
from hypothesis import assume, given, settings, HealthCheck
import hypothesis.strategies as st
import unittest
import threading

from caffe2.python import core, workspace, tt_core, dyndep

            

Reported by Pylint.

              from functools import partial, reduce
from future.utils import viewitems, viewkeys
from hypothesis import assume, given, settings, HealthCheck
import hypothesis.strategies as st
import unittest
import threading

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

            

Reported by Pylint.

                  #       result, deadlines are not enforced on CUDA runs.
    _hypothesis_settings = settings

    def settings(**kwargs):
        if 'deadline' in kwargs:
            kwargs['deadline'] = None
            kwargs.setdefault('max_examples', 50)

        def wrapped(f):

            

Reported by Pylint.

                             "GT": lambda x1, x2: [x1 > x2],
               "GE": lambda x1, x2: [x1 >= x2]}
        for name, ref in viewitems(ops):
            _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
            _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_sum(self, inputs, in_place, gc, dc):

            

Reported by Pylint.

                             "GT": lambda x1, x2: [x1 > x2],
               "GE": lambda x1, x2: [x1 >= x2]}
        for name, ref in viewitems(ops):
            _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
            _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_sum(self, inputs, in_place, gc, dc):

            

Reported by Pylint.

                             "GT": lambda x1, x2: [x1 > x2],
               "GE": lambda x1, x2: [x1 >= x2]}
        for name, ref in viewitems(ops):
            _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
            _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_sum(self, inputs, in_place, gc, dc):

            

Reported by Pylint.

                             "GT": lambda x1, x2: [x1 > x2],
               "GE": lambda x1, x2: [x1 >= x2]}
        for name, ref in viewitems(ops):
            _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
            _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_sum(self, inputs, in_place, gc, dc):

            

Reported by Pylint.

                             "GE": lambda x1, x2: [x1 >= x2]}
        for name, ref in viewitems(ops):
            _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
            _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_sum(self, inputs, in_place, gc, dc):
        op = core.CreateOperator("Sum", ["X1", "X2"],

            

Reported by Pylint.

                             "GE": lambda x1, x2: [x1 >= x2]}
        for name, ref in viewitems(ops):
            _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
            _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_sum(self, inputs, in_place, gc, dc):
        op = core.CreateOperator("Sum", ["X1", "X2"],

            

Reported by Pylint.

                             "GE": lambda x1, x2: [x1 >= x2]}
        for name, ref in viewitems(ops):
            _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self)
            _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self)

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_sum(self, inputs, in_place, gc, dc):
        op = core.CreateOperator("Sum", ["X1", "X2"],

            

Reported by Pylint.

                      torch.save,
        torch.load,
        torch.set_printoptions,
        torch.fork,
        torch.get_default_dtype,
        torch.get_num_interop_threads,
        torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,

            

Reported by Pylint.

                      torch.load,
        torch.set_printoptions,
        torch.fork,
        torch.get_default_dtype,
        torch.get_num_interop_threads,
        torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,

            

Reported by Pylint.

                      torch.set_printoptions,
        torch.fork,
        torch.get_default_dtype,
        torch.get_num_interop_threads,
        torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,

            

Reported by Pylint.

                      torch.fork,
        torch.get_default_dtype,
        torch.get_num_interop_threads,
        torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,

            

Reported by Pylint.

                      torch.get_default_dtype,
        torch.get_num_interop_threads,
        torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,
        torch.merge_type_from_type_comment,

            

Reported by Pylint.

                      torch.get_num_interop_threads,
        torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,
        torch.merge_type_from_type_comment,
        torch.parse_ir,

            

Reported by Pylint.

                      torch.get_num_threads,
        torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,
        torch.merge_type_from_type_comment,
        torch.parse_ir,
        torch.parse_schema,

            

Reported by Pylint.

                      torch.init_num_threads,
        torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,
        torch.merge_type_from_type_comment,
        torch.parse_ir,
        torch.parse_schema,
        torch.parse_type_comment,

            

Reported by Pylint.

                      torch.import_ir_module,
        torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,
        torch.merge_type_from_type_comment,
        torch.parse_ir,
        torch.parse_schema,
        torch.parse_type_comment,
        torch.set_anomaly_enabled,

            

Reported by Pylint.

                      torch.import_ir_module_from_buffer,
        torch.is_anomaly_enabled,
        torch.is_grad_enabled,
        torch.merge_type_from_type_comment,
        torch.parse_ir,
        torch.parse_schema,
        torch.parse_type_comment,
        torch.set_anomaly_enabled,
        torch.set_flush_denormal,

            

Reported by Pylint.

              import torch
import numpy as np

import sys
import math
import warnings
import unittest
from itertools import product, combinations, combinations_with_replacement, permutations
import random

            

Reported by Pylint.

              from itertools import product, combinations, combinations_with_replacement, permutations
import random

from torch.testing._internal.common_utils import (
    TestCase, run_tests, do_test_empty_full, TEST_WITH_ROCM, suppress_warnings,
    torch_to_numpy_dtype_dict, slowTest, make_tensor, TEST_SCIPY, IS_MACOS, IS_PPC,
    IS_WINDOWS)
from torch.testing._internal.common_device_type import (
    instantiate_device_type_tests, deviceCountAtLeast, onlyOnCPUAndCUDA,

            

Reported by Pylint.

                  TestCase, run_tests, do_test_empty_full, TEST_WITH_ROCM, suppress_warnings,
    torch_to_numpy_dtype_dict, slowTest, make_tensor, TEST_SCIPY, IS_MACOS, IS_PPC,
    IS_WINDOWS)
from torch.testing._internal.common_device_type import (
    instantiate_device_type_tests, deviceCountAtLeast, onlyOnCPUAndCUDA,
    onlyCPU, largeTensorTest, precisionOverride, dtypes,
    onlyCUDA, skipCPUIf, dtypesIfCUDA, dtypesIfCPU, skipMeta)

# TODO: refactor tri_tests_args, _compare_trilu_indices, run_additional_tri_tests

            

Reported by Pylint.

                  onlyCUDA, skipCPUIf, dtypesIfCUDA, dtypesIfCPU, skipMeta)

# TODO: refactor tri_tests_args, _compare_trilu_indices, run_additional_tri_tests
from torch.testing._internal.common_methods_invocations import (
    tri_tests_args, _compare_trilu_indices, run_additional_tri_tests)


# TODO: replace with make_tensor
def _generate_input(shape, dtype, device, with_extremal):

            

Reported by Pylint.

              
    @unittest.skipIf(not TEST_SCIPY, "Scipy not found")
    def test_block_diag_scipy(self, device):
        import scipy.linalg
        scipy_tensors_list = [
            [
                1,
                [2],
                [],

            

Reported by Pylint.

                  @dtypesIfCUDA(torch.float, torch.double, torch.bfloat16, torch.half, torch.long)
    @dtypesIfCPU(torch.float, torch.double, torch.long)
    def test_signal_window_functions(self, device, dtype):
        import scipy.signal as signal

        def test(name, kwargs):
            torch_method = getattr(torch, name + '_window')
            if not dtype.is_floating_point:
                with self.assertRaisesRegex(RuntimeError, r'floating point'):

            

Reported by Pylint.

                  onlyCPU, largeTensorTest, precisionOverride, dtypes,
    onlyCUDA, skipCPUIf, dtypesIfCUDA, dtypesIfCPU, skipMeta)

# TODO: refactor tri_tests_args, _compare_trilu_indices, run_additional_tri_tests
from torch.testing._internal.common_methods_invocations import (
    tri_tests_args, _compare_trilu_indices, run_additional_tri_tests)


# TODO: replace with make_tensor

            

Reported by Pylint.

                  tri_tests_args, _compare_trilu_indices, run_additional_tri_tests)


# TODO: replace with make_tensor
def _generate_input(shape, dtype, device, with_extremal):
    if shape == ():
        x = torch.tensor((), dtype=dtype, device=device)
    else:
        if dtype.is_floating_point or dtype.is_complex:

            

Reported by Pylint.

                  return x


# TODO: replace with make_tensor
def _rand_shape(dim, min_size, max_size):
    shape = []
    for i in range(dim):
        shape.append(random.randint(min_size, max_size))
    return tuple(shape)

            

Reported by Pylint.

              # TODO: replace with make_tensor
def _rand_shape(dim, min_size, max_size):
    shape = []
    for i in range(dim):
        shape.append(random.randint(min_size, max_size))
    return tuple(shape)

# Test suite for tensor creation ops
#

            

Reported by Pylint.

              import torch
import numpy as np

import itertools
from itertools import product
import math
import random
import unittest
import warnings

            

Reported by Pylint.

              import operator
from functools import partial

from torch._six import inf, nan
from torch.testing._internal.common_utils import (
    TestCase, iter_indices, TEST_WITH_ASAN, run_tests,
    torch_to_numpy_dtype_dict, make_tensor, TEST_SCIPY, set_default_dtype)
from torch.testing._internal.common_device_type import (
    instantiate_device_type_tests, onlyCUDA, onlyCPU, dtypes, dtypesIfCUDA,

            

Reported by Pylint.

              from functools import partial

from torch._six import inf, nan
from torch.testing._internal.common_utils import (
    TestCase, iter_indices, TEST_WITH_ASAN, run_tests,
    torch_to_numpy_dtype_dict, make_tensor, TEST_SCIPY, set_default_dtype)
from torch.testing._internal.common_device_type import (
    instantiate_device_type_tests, onlyCUDA, onlyCPU, dtypes, dtypesIfCUDA,
    dtypesIfCPU, deviceCountAtLeast, precisionOverride, onlyOnCPUAndCUDA,

            

Reported by Pylint.

              from torch.testing._internal.common_utils import (
    TestCase, iter_indices, TEST_WITH_ASAN, run_tests,
    torch_to_numpy_dtype_dict, make_tensor, TEST_SCIPY, set_default_dtype)
from torch.testing._internal.common_device_type import (
    instantiate_device_type_tests, onlyCUDA, onlyCPU, dtypes, dtypesIfCUDA,
    dtypesIfCPU, deviceCountAtLeast, precisionOverride, onlyOnCPUAndCUDA,
    skipCUDAIfRocm, skipIf)
from torch.testing import all_types_and_complex_and, integral_types_and


            

Reported by Pylint.

                  instantiate_device_type_tests, onlyCUDA, onlyCPU, dtypes, dtypesIfCUDA,
    dtypesIfCPU, deviceCountAtLeast, precisionOverride, onlyOnCPUAndCUDA,
    skipCUDAIfRocm, skipIf)
from torch.testing import all_types_and_complex_and, integral_types_and

if TEST_SCIPY:
    import scipy.special
    import scipy.integrate


            

Reported by Pylint.

              from torch.testing import all_types_and_complex_and, integral_types_and

if TEST_SCIPY:
    import scipy.special
    import scipy.integrate

# TODO: remove this
def _generate_input(shape, dtype, device, with_extremal):
    if shape == ():

            

Reported by Pylint.

              
if TEST_SCIPY:
    import scipy.special
    import scipy.integrate

# TODO: remove this
def _generate_input(shape, dtype, device, with_extremal):
    if shape == ():
        x = torch.tensor((), dtype=dtype, device=device)

            

Reported by Pylint.

                                      actual_first_tensor = torch_op(a_t, b)
                        actual_second_tensor = torch_op(a, b_t)

                        self.assertEqual(actual_scalar, expected_div)
                        self.assertEqual(actual_tensor.item(), expected_div)
                        self.assertEqual(actual_first_tensor, actual_tensor)
                        self.assertEqual(actual_second_tensor, actual_tensor)

            _scalar_helper(operator.truediv, operator.truediv)

            

Reported by Pylint.

                                      actual_second_tensor = torch_op(a, b_t)

                        self.assertEqual(actual_scalar, expected_div)
                        self.assertEqual(actual_tensor.item(), expected_div)
                        self.assertEqual(actual_first_tensor, actual_tensor)
                        self.assertEqual(actual_second_tensor, actual_tensor)

            _scalar_helper(operator.truediv, operator.truediv)
            _scalar_helper(operator.truediv, torch.true_divide)

            

Reported by Pylint.

                                  with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        a_t.clone().floor_divide_(b_t)
                    with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        scripted_floor_divide_tensor(a_t.clone(), b_t)
                    tmp = a_t.clone()
                    with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        tmp //= b_t
                else:
                    # Inplace modification is OK when both or neither tensor is

            

Reported by Pylint.

              from textwrap import dedent
from collections import OrderedDict

from torch import Tensor
import torch
import torch.nn as nn
import types
from torch.testing import FileCheck


            

Reported by Pylint.

              from collections import OrderedDict

from torch import Tensor
import torch
import torch.nn as nn
import types
from torch.testing import FileCheck

# Make the helper files in test/ importable

            

Reported by Pylint.

              
from torch import Tensor
import torch
import torch.nn as nn
import types
from torch.testing import FileCheck

# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))

            

Reported by Pylint.

              import torch
import torch.nn as nn
import types
from torch.testing import FileCheck

# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase

            

Reported by Pylint.

              # Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase

if __name__ == '__main__':
    raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
                       "\tpython test/test_jit.py TESTNAME\n\n"
                       "instead.")

            

Reported by Pylint.

              
        with self.assertRaisesRegexWithHighlight(RuntimeError, "deletion at a single index", "x[1:3]"):
            @torch.jit.script
            def fn(x: List[int]) -> List[int]:
                del x[1:3]
                return x

    def test_list_keyword(self):
        def foo():

            

Reported by Pylint.

                              return self

            def __next__(self):
                if self.value == limit:
                    raise StopIteration()

                ret = self.value
                self.value += 1
                return ret

            

Reported by Pylint.

              
    def test_list_literal(self):
        def reassign():
            x = [1]
            if 1 == 1:
                x = [2, 3]
            return
        self.checkScript(reassign, (), optimize=False)


            

Reported by Pylint.

                      self.checkScript(reassign, (), optimize=False)

        def reassign_arity_change():
            x = [1]
            if 1 == 1:
                x = [1, 2, 3]
            return
        self.checkScript(reassign_arity_change, (), optimize=False)


            

Reported by Pylint.

                      self.checkScript(reassign_arity_change, (), optimize=False)

        def reassign_from_empty_literal():
            x = []
            if 1 == 1:
                x = [1, 2, 3]
            return
        with self.assertRaisesRegexWithHighlight(RuntimeError, r"previously has type List\[Tensor\]", "x"):
            self.checkScript(reassign_from_empty_literal, (), optimize=False)

            

Reported by Pylint.

              from torch.testing._internal.common_utils import TestCase, run_tests
import torch
import torch.nn.functional as F
from torch import Tensor, vmap
import functools
import itertools
import warnings
from torch.testing._internal.common_device_type import instantiate_device_type_tests, \
    skipCUDAIfNoMagma

            

Reported by Pylint.

              from torch.testing._internal.common_utils import TestCase, run_tests
import torch
import torch.nn.functional as F
from torch import Tensor, vmap
import functools
import itertools
import warnings
from torch.testing._internal.common_device_type import instantiate_device_type_tests, \
    skipCUDAIfNoMagma

            

Reported by Pylint.

              from torch.testing._internal.common_utils import TestCase, run_tests
import torch
import torch.nn.functional as F
from torch import Tensor, vmap
import functools
import itertools
import warnings
from torch.testing._internal.common_device_type import instantiate_device_type_tests, \
    skipCUDAIfNoMagma

            

Reported by Pylint.

              from torch.testing._internal.common_utils import TestCase, run_tests
import torch
import torch.nn.functional as F
from torch import Tensor, vmap
import functools
import itertools
import warnings
from torch.testing._internal.common_device_type import instantiate_device_type_tests, \
    skipCUDAIfNoMagma

            

Reported by Pylint.

              import functools
import itertools
import warnings
from torch.testing._internal.common_device_type import instantiate_device_type_tests, \
    skipCUDAIfNoMagma
import types


FALLBACK_REGEX = r'falling back to slow \(for loop( and stack)?\) implementation'

            

Reported by Pylint.

              
class EnableVmapFallbackWarnings:
    def __enter__(self):
        self.prev_state = torch._C._debug_only_are_vmap_fallback_warnings_enabled()
        torch._C._debug_only_display_vmap_fallback_warnings(True)

    def __exit__(self, *ignored):
        torch._C._debug_only_display_vmap_fallback_warnings(self.prev_state)


            

Reported by Pylint.

              class TestVmapAPI(TestCase):
    def test_non_tensor_output_raises(self):
        with self.assertRaisesRegex(ValueError, "got type <class 'float'> as the return"):
            output = vmap(lambda x: 3.14)(torch.ones(3))

        def multiple_outputs(x):
            return x, 3

        with self.assertRaisesRegex(ValueError, "got type <class 'int'> for return 1"):

            

Reported by Pylint.

                      def foo():
            return torch.randn(3)

        def bar(x):
            return torch.randn(3)

        with self.assertRaisesRegex(ValueError, expected_msg):
            vmap(foo)()


            

Reported by Pylint.

                      y = torch.randn(2, 3, 5, 7)

        # Inner vmap has non-zero out_dim
        result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y))(y)
        self.assertEqual(result.shape, (2, 5, 3, 7))
        self.assertEqual(result, y.permute(0, 2, 1, 3))

        # all vmaps have non-zero out_dim
        result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y), out_dims=1)(y)

            

Reported by Pylint.

                      self.assertEqual(result, y.permute(0, 2, 1, 3))

        # all vmaps have non-zero out_dim
        result = vmap(lambda y: vmap(lambda x: x, out_dims=1)(y), out_dims=1)(y)
        self.assertEqual(result.shape, (5, 2, 3, 7))
        self.assertEqual(result, y.permute(2, 0, 1, 3))

        # throwing in some negative out_dims
        result = vmap(lambda y: vmap(lambda x: x, out_dims=-1)(y), out_dims=-1)(y)

            

Reported by Pylint.