# Variables
from ._mappings import get_dynamic_sparse_quantized_mapping
from ._mappings import get_static_sparse_quantized_mapping

# Sparsifier
from .sparsifier.base_sparsifier import BaseSparsifier
from .sparsifier.weight_norm_sparsifier import WeightNormSparsifier

# Scheduler

            

Reported by Pylint.

              # Variables
from ._mappings import get_dynamic_sparse_quantized_mapping
from ._mappings import get_static_sparse_quantized_mapping

# Sparsifier
from .sparsifier.base_sparsifier import BaseSparsifier
from .sparsifier.weight_norm_sparsifier import WeightNormSparsifier

# Scheduler

            

Reported by Pylint.

              from ._mappings import get_static_sparse_quantized_mapping

# Sparsifier
from .sparsifier.base_sparsifier import BaseSparsifier
from .sparsifier.weight_norm_sparsifier import WeightNormSparsifier

# Scheduler
from .scheduler.base_scheduler import BaseScheduler
from .scheduler.lambda_scheduler import LambdaSL

            

Reported by Pylint.

              
# Sparsifier
from .sparsifier.base_sparsifier import BaseSparsifier
from .sparsifier.weight_norm_sparsifier import WeightNormSparsifier

# Scheduler
from .scheduler.base_scheduler import BaseScheduler
from .scheduler.lambda_scheduler import LambdaSL


            

Reported by Pylint.

              from .sparsifier.weight_norm_sparsifier import WeightNormSparsifier

# Scheduler
from .scheduler.base_scheduler import BaseScheduler
from .scheduler.lambda_scheduler import LambdaSL

# Parametrizations
from .sparsifier.utils import FakeSparsity


            

Reported by Pylint.

              
# Scheduler
from .scheduler.base_scheduler import BaseScheduler
from .scheduler.lambda_scheduler import LambdaSL

# Parametrizations
from .sparsifier.utils import FakeSparsity

# === Experimental ===

            

Reported by Pylint.

              from .scheduler.lambda_scheduler import LambdaSL

# Parametrizations
from .sparsifier.utils import FakeSparsity

# === Experimental ===

# Parametrizations
from .experimental.pruner.parametrization import PruningParametrization

            

Reported by Pylint.

              # === Experimental ===

# Parametrizations
from .experimental.pruner.parametrization import PruningParametrization
from .experimental.pruner.parametrization import LinearActivationReconstruction
from .experimental.pruner.parametrization import Conv2dActivationReconstruction

# Pruner
from .experimental.pruner.base_pruner import BasePruner

            

Reported by Pylint.

              
# Parametrizations
from .experimental.pruner.parametrization import PruningParametrization
from .experimental.pruner.parametrization import LinearActivationReconstruction
from .experimental.pruner.parametrization import Conv2dActivationReconstruction

# Pruner
from .experimental.pruner.base_pruner import BasePruner

            

Reported by Pylint.

              # Parametrizations
from .experimental.pruner.parametrization import PruningParametrization
from .experimental.pruner.parametrization import LinearActivationReconstruction
from .experimental.pruner.parametrization import Conv2dActivationReconstruction

# Pruner
from .experimental.pruner.base_pruner import BasePruner

            

Reported by Pylint.

              import torch
from torch.testing._internal.jit_utils import JitTestCase

class TestFuserCommon(JitTestCase):
    def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            

Reported by Pylint.

              import torch
from torch.testing._internal.jit_utils import JitTestCase

class TestFuserCommon(JitTestCase):
    def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            

Reported by Pylint.

              
            x = torch.randn(5, requires_grad=not rq)
            # cause optimization to be created
            for i in range(5):
                fn(x)
            # test fallback when optimization is not applicable
            y = fn(torch.randn(5, requires_grad=rq))
            self.assertEqual(y.requires_grad, rq)

            

Reported by Pylint.

              import torch
from torch.testing._internal.jit_utils import JitTestCase

class TestFuserCommon(JitTestCase):
    def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            

Reported by Pylint.

              import torch
from torch.testing._internal.jit_utils import JitTestCase

class TestFuserCommon(JitTestCase):
    def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            

Reported by Pylint.

              import torch
from torch.testing._internal.jit_utils import JitTestCase

class TestFuserCommon(JitTestCase):
    def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            

Reported by Pylint.

              from torch.testing._internal.jit_utils import JitTestCase

class TestFuserCommon(JitTestCase):
    def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)


            

Reported by Pylint.

              
class TestFuserCommon(JitTestCase):
    def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            x = torch.randn(5, requires_grad=not rq)

            

Reported by Pylint.

                  def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            x = torch.randn(5, requires_grad=not rq)
            # cause optimization to be created
            for i in range(5):

            

Reported by Pylint.

                  def test_autodiff_fallback(self):
        for rq in [True, False]:
            @torch.jit.script
            def fn(x):
                return torch.max(x**2.0, x**3.0)

            x = torch.randn(5, requires_grad=not rq)
            # cause optimization to be created
            for i in range(5):

            

Reported by Pylint.

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


class TestSparseNormalize(hu.HypothesisTestCase):

            

Reported by Pylint.

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


class TestSparseNormalize(hu.HypothesisTestCase):

            

Reported by Pylint.

              import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
from hypothesis import HealthCheck, given, settings


class TestSparseNormalize(hu.HypothesisTestCase):
    @staticmethod
    def ref_normalize(param_in, use_max_norm, norm):

            

Reported by Pylint.

                      **hu.gcs_cpu_only
    )
    def test_sparse_normalize(
        self, inputs, use_max_norm, norm, data_strategy, use_fp16, gc, dc
    ):
        param, grad = inputs
        param += 0.02 * np.sign(param)
        param[param == 0.0] += 0.02


            

Reported by Pylint.

                          norm=norm,
        )

        def ref_sparse_normalize(param, indices, grad=None):
            param_out = np.copy(param)
            for _, index in enumerate(indices):
                param_out[index] = self.ref_normalize(param[index], use_max_norm, norm)
            return (param_out,)


            

Reported by Pylint.

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


class TestSparseNormalize(hu.HypothesisTestCase):

            

Reported by Pylint.

              from hypothesis import HealthCheck, given, settings


class TestSparseNormalize(hu.HypothesisTestCase):
    @staticmethod
    def ref_normalize(param_in, use_max_norm, norm):
        param_norm = np.linalg.norm(param_in) + 1e-12
        if (use_max_norm and param_norm > norm) or not use_max_norm:
            param_in = param_in * norm / param_norm

            

Reported by Pylint.

              
class TestSparseNormalize(hu.HypothesisTestCase):
    @staticmethod
    def ref_normalize(param_in, use_max_norm, norm):
        param_norm = np.linalg.norm(param_in) + 1e-12
        if (use_max_norm and param_norm > norm) or not use_max_norm:
            param_in = param_in * norm / param_norm
        return param_in


            

Reported by Pylint.

                      data_strategy=st.data(),
        use_fp16=st.booleans(),
        **hu.gcs_cpu_only
    )
    def test_sparse_normalize(
        self, inputs, use_max_norm, norm, data_strategy, use_fp16, gc, dc
    ):
        param, grad = inputs
        param += 0.02 * np.sign(param)

            

Reported by Pylint.

                      data_strategy=st.data(),
        use_fp16=st.booleans(),
        **hu.gcs_cpu_only
    )
    def test_sparse_normalize(
        self, inputs, use_max_norm, norm, data_strategy, use_fp16, gc, dc
    ):
        param, grad = inputs
        param += 0.02 * np.sign(param)

            

Reported by Pylint.

              
import numpy as np

from hypothesis import given
import hypothesis.strategies as st

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

            

Reported by Pylint.

              import numpy as np

from hypothesis import given
import hypothesis.strategies as st

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


            

Reported by Pylint.

                      data_len=st.integers(min_value=0, max_value=10000),
        **hu.gcs_cpu_only
    )
    def test_weighted_multi_sample(self, num_samples, data_len, gc, dc):
        weights = np.zeros((data_len))
        expected_indices = []
        if data_len > 0:
            weights[-1] = 1.5
            expected_indices = np.repeat(data_len - 1, num_samples)

            

Reported by Pylint.

              




import numpy as np

from hypothesis import given
import hypothesis.strategies as st

            

Reported by Pylint.

              import caffe2.python.hypothesis_test_util as hu


class TestWeightedMultiSample(hu.HypothesisTestCase):
    @given(
        num_samples=st.integers(min_value=0, max_value=128),
        data_len=st.integers(min_value=0, max_value=10000),
        **hu.gcs_cpu_only
    )

            

Reported by Pylint.

                      num_samples=st.integers(min_value=0, max_value=128),
        data_len=st.integers(min_value=0, max_value=10000),
        **hu.gcs_cpu_only
    )
    def test_weighted_multi_sample(self, num_samples, data_len, gc, dc):
        weights = np.zeros((data_len))
        expected_indices = []
        if data_len > 0:
            weights[-1] = 1.5

            

Reported by Pylint.

                      num_samples=st.integers(min_value=0, max_value=128),
        data_len=st.integers(min_value=0, max_value=10000),
        **hu.gcs_cpu_only
    )
    def test_weighted_multi_sample(self, num_samples, data_len, gc, dc):
        weights = np.zeros((data_len))
        expected_indices = []
        if data_len > 0:
            weights[-1] = 1.5

            

Reported by Pylint.

                      num_samples=st.integers(min_value=0, max_value=128),
        data_len=st.integers(min_value=0, max_value=10000),
        **hu.gcs_cpu_only
    )
    def test_weighted_multi_sample(self, num_samples, data_len, gc, dc):
        weights = np.zeros((data_len))
        expected_indices = []
        if data_len > 0:
            weights[-1] = 1.5

            

Reported by Pylint.

              
        workspace.FeedBlob("weights", weights.astype(np.float32))

        op = core.CreateOperator(
            "WeightedMultiSampling",
            ["weights"],
            ["sample_indices"],
            num_samples=num_samples,
        )

            

Reported by Pylint.

                      workspace.RunOperatorOnce(op2)
        result_indices_2 = workspace.FetchBlob("sample_indices_2")
        if data_len > 0:
            assert len(result_indices_2) == num_samples
            for i in range(num_samples):
                assert 0 <= result_indices_2[i] < data_len
        else:
            assert len(result_indices_2) == 0


            

Reported by Bandit.

              from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest


class TestPad(serial.SerializedTestCase):

            

Reported by Pylint.

                         **hu.gcs)
    def test_crop(self,
                  pad_t, pad_l, pad_b, pad_r,
                  mode,
                  size_w, size_h, size_c, size_n,
                  gc, dc):
        op = core.CreateOperator(
            "PadImage",
            ["X"],

            

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
import hypothesis.strategies as st
import numpy as np

            

Reported by Pylint.

              import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest


class TestPad(serial.SerializedTestCase):
    @serial.given(pad_t=st.integers(-5, 0),
           pad_l=st.integers(-5, 0),

            

Reported by Pylint.

              import unittest


class TestPad(serial.SerializedTestCase):
    @serial.given(pad_t=st.integers(-5, 0),
           pad_l=st.integers(-5, 0),
           pad_b=st.integers(-5, 0),
           pad_r=st.integers(-5, 0),
           mode=st.sampled_from(["constant", "reflect", "edge"]),

            

Reported by Pylint.

                         size_c=st.integers(1, 4),
           size_n=st.integers(1, 4),
           **hu.gcs)
    def test_crop(self,
                  pad_t, pad_l, pad_b, pad_r,
                  mode,
                  size_w, size_h, size_c, size_n,
                  gc, dc):
        op = core.CreateOperator(

            

Reported by Pylint.

                         size_c=st.integers(1, 4),
           size_n=st.integers(1, 4),
           **hu.gcs)
    def test_crop(self,
                  pad_t, pad_l, pad_b, pad_r,
                  mode,
                  size_w, size_h, size_c, size_n,
                  gc, dc):
        op = core.CreateOperator(

            

Reported by Pylint.

                         size_c=st.integers(1, 4),
           size_n=st.integers(1, 4),
           **hu.gcs)
    def test_crop(self,
                  pad_t, pad_l, pad_b, pad_r,
                  mode,
                  size_w, size_h, size_c, size_n,
                  gc, dc):
        op = core.CreateOperator(

            

Reported by Pylint.

                         size_c=st.integers(1, 4),
           size_n=st.integers(1, 4),
           **hu.gcs)
    def test_crop(self,
                  pad_t, pad_l, pad_b, pad_r,
                  mode,
                  size_w, size_h, size_c, size_n,
                  gc, dc):
        op = core.CreateOperator(

            

Reported by Pylint.

                                mode,
                  size_w, size_h, size_c, size_n,
                  gc, dc):
        op = core.CreateOperator(
            "PadImage",
            ["X"],
            ["Y"],
            pad_t=pad_t,
            pad_l=pad_l,

            

Reported by Pylint.

              import numpy

from caffe2.python import core
from hypothesis import given, settings

import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np


            

Reported by Pylint.

              from hypothesis import given, settings

import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np


@st.composite
def _data(draw):

            

Reported by Pylint.

              
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np


@st.composite
def _data(draw):
    return draw(

            

Reported by Pylint.

                      **hu.gcs
    )
    def test_mod(
        self, data, divisor, inplace, sign_follow_divisor, gc, dc
    ):
        if divisor == 0:
            # invalid test case
            return None


            

Reported by Pylint.

              import numpy

from caffe2.python import core
from hypothesis import given, settings

import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np


            

Reported by Pylint.

              
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np


@st.composite
def _data(draw):
    return draw(

            

Reported by Pylint.

                  )


class TestMod(hu.HypothesisTestCase):
    @settings(deadline=None)
    @given(
        data=_data(),
        divisor=st.integers(
            min_value=np.iinfo(np.int64).min, max_value=np.iinfo(np.int64).max

            

Reported by Pylint.

                      inplace=st.booleans(),
        sign_follow_divisor=st.booleans(),
        **hu.gcs
    )
    def test_mod(
        self, data, divisor, inplace, sign_follow_divisor, gc, dc
    ):
        if divisor == 0:
            # invalid test case

            

Reported by Pylint.

                      inplace=st.booleans(),
        sign_follow_divisor=st.booleans(),
        **hu.gcs
    )
    def test_mod(
        self, data, divisor, inplace, sign_follow_divisor, gc, dc
    ):
        if divisor == 0:
            # invalid test case

            

Reported by Pylint.

                      inplace=st.booleans(),
        sign_follow_divisor=st.booleans(),
        **hu.gcs
    )
    def test_mod(
        self, data, divisor, inplace, sign_follow_divisor, gc, dc
    ):
        if divisor == 0:
            # invalid test case

            

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
import hypothesis.strategies as st
import numpy as np
import unittest


class TestMean(serial.SerializedTestCase):

            

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
import hypothesis.strategies as st

            

Reported by Pylint.

              import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest


class TestMean(serial.SerializedTestCase):
    @serial.given(
        k=st.integers(1, 5),

            

Reported by Pylint.

              import unittest


class TestMean(serial.SerializedTestCase):
    @serial.given(
        k=st.integers(1, 5),
        n=st.integers(1, 10),
        m=st.integers(1, 10),
        in_place=st.booleans(),

            

Reported by Pylint.

                      in_place=st.booleans(),
        seed=st.integers(0, 2**32 - 1),
        **hu.gcs
    )
    def test_mean(self, k, n, m, in_place, seed, gc, dc):
        np.random.seed(seed)
        input_names = []
        input_vars = []


            

Reported by Pylint.

                      in_place=st.booleans(),
        seed=st.integers(0, 2**32 - 1),
        **hu.gcs
    )
    def test_mean(self, k, n, m, in_place, seed, gc, dc):
        np.random.seed(seed)
        input_names = []
        input_vars = []


            

Reported by Pylint.

                      in_place=st.booleans(),
        seed=st.integers(0, 2**32 - 1),
        **hu.gcs
    )
    def test_mean(self, k, n, m, in_place, seed, gc, dc):
        np.random.seed(seed)
        input_names = []
        input_vars = []


            

Reported by Pylint.

                      in_place=st.booleans(),
        seed=st.integers(0, 2**32 - 1),
        **hu.gcs
    )
    def test_mean(self, k, n, m, in_place, seed, gc, dc):
        np.random.seed(seed)
        input_names = []
        input_vars = []


            

Reported by Pylint.

                      in_place=st.booleans(),
        seed=st.integers(0, 2**32 - 1),
        **hu.gcs
    )
    def test_mean(self, k, n, m, in_place, seed, gc, dc):
        np.random.seed(seed)
        input_names = []
        input_vars = []


            

Reported by Pylint.

                      in_place=st.booleans(),
        seed=st.integers(0, 2**32 - 1),
        **hu.gcs
    )
    def test_mean(self, k, n, m, in_place, seed, gc, dc):
        np.random.seed(seed)
        input_names = []
        input_vars = []


            

Reported by Pylint.

              import os.path
import sys

import torch


def get_custom_op_library_path():
    if sys.platform.startswith("win32"):
        library_filename = "custom_ops.dll"

            

Reported by Pylint.

                      self.p = torch.nn.Parameter(torch.eye(5))

    @torch.jit.script_method
    def forward(self, input):
        return torch.ops.custom.op_with_defaults(input)[0] + 1


def main():
    parser = argparse.ArgumentParser(

            

Reported by Pylint.

              import argparse
import os.path
import sys

import torch


def get_custom_op_library_path():
    if sys.platform.startswith("win32"):

            

Reported by Pylint.

              import torch


def get_custom_op_library_path():
    if sys.platform.startswith("win32"):
        library_filename = "custom_ops.dll"
    elif sys.platform.startswith("darwin"):
        library_filename = "libcustom_ops.dylib"
    else:

            

Reported by Pylint.

                  else:
        library_filename = "libcustom_ops.so"
    path = os.path.abspath("build/{}".format(library_filename))
    assert os.path.exists(path), path
    return path


class Model(torch.jit.ScriptModule):
    def __init__(self):

            

Reported by Bandit.

                  return path


class Model(torch.jit.ScriptModule):
    def __init__(self):
        super(Model, self).__init__()
        self.p = torch.nn.Parameter(torch.eye(5))

    @torch.jit.script_method

            

Reported by Pylint.

                  return path


class Model(torch.jit.ScriptModule):
    def __init__(self):
        super(Model, self).__init__()
        self.p = torch.nn.Parameter(torch.eye(5))

    @torch.jit.script_method

            

Reported by Pylint.

              
class Model(torch.jit.ScriptModule):
    def __init__(self):
        super(Model, self).__init__()
        self.p = torch.nn.Parameter(torch.eye(5))

    @torch.jit.script_method
    def forward(self, input):
        return torch.ops.custom.op_with_defaults(input)[0] + 1

            

Reported by Pylint.

              class Model(torch.jit.ScriptModule):
    def __init__(self):
        super(Model, self).__init__()
        self.p = torch.nn.Parameter(torch.eye(5))

    @torch.jit.script_method
    def forward(self, input):
        return torch.ops.custom.op_with_defaults(input)[0] + 1


            

Reported by Pylint.

                      self.p = torch.nn.Parameter(torch.eye(5))

    @torch.jit.script_method
    def forward(self, input):
        return torch.ops.custom.op_with_defaults(input)[0] + 1


def main():
    parser = argparse.ArgumentParser(

            

Reported by Pylint.

              


from hypothesis import given
import numpy as np

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


            

Reported by Pylint.

              
            def flatten_ref(X):
                shape = X.shape
                outer = np.prod(shape[:axis]).astype(int)
                inner = np.prod(shape[axis:]).astype(int)
                return np.copy(X).reshape(outer, inner),

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


            

Reported by Pylint.

                          def flatten_ref(X):
                shape = X.shape
                outer = np.prod(shape[:axis]).astype(int)
                inner = np.prod(shape[axis:]).astype(int)
                return np.copy(X).reshape(outer, inner),

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

        # Check over multiple devices

            

Reported by Pylint.

              




from hypothesis import given
import numpy as np

from caffe2.python import core

            

Reported by Pylint.

              import caffe2.python.hypothesis_test_util as hu


class TestFlatten(hu.HypothesisTestCase):
    @given(X=hu.tensor(min_dim=2, max_dim=4),
           **hu.gcs)
    def test_flatten(self, X, gc, dc):
        for axis in range(X.ndim + 1):
            op = core.CreateOperator(

            

Reported by Pylint.

              class TestFlatten(hu.HypothesisTestCase):
    @given(X=hu.tensor(min_dim=2, max_dim=4),
           **hu.gcs)
    def test_flatten(self, X, gc, dc):
        for axis in range(X.ndim + 1):
            op = core.CreateOperator(
                "Flatten",
                ["X"],
                ["Y"],

            

Reported by Pylint.

              class TestFlatten(hu.HypothesisTestCase):
    @given(X=hu.tensor(min_dim=2, max_dim=4),
           **hu.gcs)
    def test_flatten(self, X, gc, dc):
        for axis in range(X.ndim + 1):
            op = core.CreateOperator(
                "Flatten",
                ["X"],
                ["Y"],

            

Reported by Pylint.

              class TestFlatten(hu.HypothesisTestCase):
    @given(X=hu.tensor(min_dim=2, max_dim=4),
           **hu.gcs)
    def test_flatten(self, X, gc, dc):
        for axis in range(X.ndim + 1):
            op = core.CreateOperator(
                "Flatten",
                ["X"],
                ["Y"],

            

Reported by Pylint.

              class TestFlatten(hu.HypothesisTestCase):
    @given(X=hu.tensor(min_dim=2, max_dim=4),
           **hu.gcs)
    def test_flatten(self, X, gc, dc):
        for axis in range(X.ndim + 1):
            op = core.CreateOperator(
                "Flatten",
                ["X"],
                ["Y"],

            

Reported by Pylint.

                         **hu.gcs)
    def test_flatten(self, X, gc, dc):
        for axis in range(X.ndim + 1):
            op = core.CreateOperator(
                "Flatten",
                ["X"],
                ["Y"],
                axis=axis)


            

Reported by Pylint.

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

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


class TestFindOperator(serial.SerializedTestCase):

            

Reported by Pylint.

              import caffe2.python.serialized_test.serialized_test_util as serial

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


class TestFindOperator(serial.SerializedTestCase):


            

Reported by Pylint.

                      idxsize=st.sampled_from([2, 4, 8, 1000, 5000]),
        **hu.gcs)
    @settings(deadline=10000)
    def test_find(self, n, idxsize, gc, dc):
        maxval = 10

        def findop(idx, X):
            res = []
            for j in list(X.flatten()):

            

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 TestFindOperator(serial.SerializedTestCase):

    @given(n=st.sampled_from([1, 4, 8, 31, 79, 150]),
        idxsize=st.sampled_from([2, 4, 8, 1000, 5000]),
        **hu.gcs)
    @settings(deadline=10000)

            

Reported by Pylint.

                      idxsize=st.sampled_from([2, 4, 8, 1000, 5000]),
        **hu.gcs)
    @settings(deadline=10000)
    def test_find(self, n, idxsize, gc, dc):
        maxval = 10

        def findop(idx, X):
            res = []
            for j in list(X.flatten()):

            

Reported by Pylint.

                      idxsize=st.sampled_from([2, 4, 8, 1000, 5000]),
        **hu.gcs)
    @settings(deadline=10000)
    def test_find(self, n, idxsize, gc, dc):
        maxval = 10

        def findop(idx, X):
            res = []
            for j in list(X.flatten()):

            

Reported by Pylint.

                      idxsize=st.sampled_from([2, 4, 8, 1000, 5000]),
        **hu.gcs)
    @settings(deadline=10000)
    def test_find(self, n, idxsize, gc, dc):
        maxval = 10

        def findop(idx, X):
            res = []
            for j in list(X.flatten()):

            

Reported by Pylint.

                      idxsize=st.sampled_from([2, 4, 8, 1000, 5000]),
        **hu.gcs)
    @settings(deadline=10000)
    def test_find(self, n, idxsize, gc, dc):
        maxval = 10

        def findop(idx, X):
            res = []
            for j in list(X.flatten()):

            

Reported by Pylint.

                  def test_find(self, n, idxsize, gc, dc):
        maxval = 10

        def findop(idx, X):
            res = []
            for j in list(X.flatten()):
                i = np.where(idx == j)[0]
                if len(i) == 0:
                    res.append(-1)

            

Reported by Pylint.