from __future__ import division
from __future__ import print_function

import tensorflow.compat.v2 as tf


from absl.testing import parameterized
import numpy as np


            

Reported by Pylint.

              import tensorflow.compat.v2 as tf


from absl.testing import parameterized
import numpy as np

import keras
from keras import combinations
from keras.feature_column import sequence_feature_column as ksfc

            

Reported by Pylint.

              
    self.assertEqual(new_layer.name, orig_layer.name)
    self.assertEqual(new_layer.trainable, trainable)
    self.assertLen(new_layer._feature_columns, 1)
    self.assertEqual(new_layer._feature_columns[0].name, 'a')

  def test_serialization_sequence_features(self):
    rating = tf.feature_column.sequence_numeric_column('rating')
    sequence_feature = ksfc.SequenceFeatures([rating])

            

Reported by Pylint.

                  self.assertEqual(new_layer.name, orig_layer.name)
    self.assertEqual(new_layer.trainable, trainable)
    self.assertLen(new_layer._feature_columns, 1)
    self.assertEqual(new_layer._feature_columns[0].name, 'a')

  def test_serialization_sequence_features(self):
    rating = tf.feature_column.sequence_numeric_column('rating')
    sequence_feature = ksfc.SequenceFeatures([rating])
    config = keras.layers.serialize(sequence_feature)

            

Reported by Pylint.

                  }

    fc_layer, _ = ksfc.SequenceFeatures(cols)(input_layers)
    # TODO(tibell): Figure out the right dtype and apply masking.
    # sequence_length_mask = array_ops.sequence_mask(sequence_length)
    # x = keras.layers.GRU(32)(fc_layer, mask=sequence_length_mask)
    x = keras.layers.GRU(32)(fc_layer)
    output = keras.layers.Dense(10)(x)


            

Reported by Pylint.

              

def _initialized_session(config=None):
  sess = tf.compat.v1.Session(config=config)
  sess.run(tf.compat.v1.global_variables_initializer())
  sess.run(tf.compat.v1.tables_initializer())
  return sess



            

Reported by Pylint.

              
def _initialized_session(config=None):
  sess = tf.compat.v1.Session(config=config)
  sess.run(tf.compat.v1.global_variables_initializer())
  sess.run(tf.compat.v1.tables_initializer())
  return sess


@combinations.generate(combinations.combine(mode=['graph', 'eager']))

            

Reported by Pylint.

              def _initialized_session(config=None):
  sess = tf.compat.v1.Session(config=config)
  sess.run(tf.compat.v1.global_variables_initializer())
  sess.run(tf.compat.v1.tables_initializer())
  return sess


@combinations.generate(combinations.combine(mode=['graph', 'eager']))
class SequenceFeaturesTest(tf.test.TestCase, parameterized.TestCase):

            

Reported by Pylint.

                sess = tf.compat.v1.Session(config=config)
  sess.run(tf.compat.v1.global_variables_initializer())
  sess.run(tf.compat.v1.tables_initializer())
  return sess


@combinations.generate(combinations.combine(mode=['graph', 'eager']))
class SequenceFeaturesTest(tf.test.TestCase, parameterized.TestCase):


            

Reported by Pylint.

              

@combinations.generate(combinations.combine(mode=['graph', 'eager']))
class SequenceFeaturesTest(tf.test.TestCase, parameterized.TestCase):

  @parameterized.named_parameters(
      {'testcase_name': '2D',
       'sparse_input_args_a': {
           # example 0, ids [2]

            

Reported by Pylint.

              # ==============================================================================
"""Tests for image_dataset."""

import tensorflow.compat.v2 as tf

import os
import shutil

import numpy as np

            

Reported by Pylint.

              from keras.preprocessing import image_dataset

try:
  import PIL  # pylint:disable=g-import-not-at-top
except ImportError:
  PIL = None


class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):

            

Reported by Pylint.

              
  def _prepare_directory(self,
                         num_classes=2,
                         grayscale=False,
                         nested_dirs=False,
                         color_mode='rgb',
                         count=16):
    # Get a unique temp directory
    temp_dir = os.path.join(self.get_temp_dir(), str(np.random.randint(1e6)))

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

import os
import shutil

import numpy as np
from keras import keras_parameterized
from keras.preprocessing import image as image_preproc

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

import os
import shutil

import numpy as np
from keras import keras_parameterized
from keras.preprocessing import image as image_preproc
from keras.preprocessing import image_dataset

            

Reported by Pylint.

              from keras.preprocessing import image_dataset

try:
  import PIL  # pylint:disable=g-import-not-at-top
except ImportError:
  PIL = None


class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):

            

Reported by Pylint.

              try:
  import PIL  # pylint:disable=g-import-not-at-top
except ImportError:
  PIL = None


class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):

  def _get_images(self, count=16, color_mode='rgb'):

            

Reported by Pylint.

                PIL = None


class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):

  def _get_images(self, count=16, color_mode='rgb'):
    width = height = 24
    imgs = []
    for _ in range(count):

            

Reported by Pylint.

              
class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):

  def _get_images(self, count=16, color_mode='rgb'):
    width = height = 24
    imgs = []
    for _ in range(count):
      if color_mode == 'grayscale':
        img = np.random.randint(0, 256, size=(height, width, 1))

            

Reported by Pylint.

              
class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):

  def _get_images(self, count=16, color_mode='rgb'):
    width = height = 24
    imgs = []
    for _ in range(count):
      if color_mode == 'grayscale':
        img = np.random.randint(0, 256, size=(height, width, 1))

            

Reported by Pylint.

              # ==============================================================================
"""RaggedKerasTensor tests."""

import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np
from keras import keras_parameterized
from keras import layers

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np
from keras import keras_parameterized
from keras import layers
from keras.engine import training


            

Reported by Pylint.

                  self.assertEqual(inp.ragged_rank, ragged_rank)
    self.assertAllEqual(inp.shape, [batch_size] + list(shape))
    with tf.__internal__.FuncGraph('test').as_default():
      placeholder = inp._to_placeholder()
      self.assertEqual(placeholder.ragged_rank, ragged_rank)
      self.assertAllEqual(placeholder.shape, [batch_size] + list(shape))

  def test_add(self):
    inp = layers.Input(shape=[None], ragged=True)

            

Reported by Pylint.

              from keras.engine import training


class RaggedKerasTensorTest(keras_parameterized.TestCase):

  @parameterized.parameters(
      {'batch_size': None, 'shape': (None, 5), 'ragged_rank': 1},
      {'batch_size': None, 'shape': (None, 3, 5), 'ragged_rank': 1},
      {'batch_size': None, 'shape': (5, None), 'ragged_rank': 2},

            

Reported by Pylint.

              
class RaggedKerasTensorTest(keras_parameterized.TestCase):

  @parameterized.parameters(
      {'batch_size': None, 'shape': (None, 5), 'ragged_rank': 1},
      {'batch_size': None, 'shape': (None, 3, 5), 'ragged_rank': 1},
      {'batch_size': None, 'shape': (5, None), 'ragged_rank': 2},
      {'batch_size': None, 'shape': (3, 5, None), 'ragged_rank': 3},
      {'batch_size': None, 'shape': (None, 3, 5, None), 'ragged_rank': 4},

            

Reported by Pylint.

                    {'batch_size': 4, 'shape': (3, 5, None), 'ragged_rank': 3},
      {'batch_size': 7, 'shape': (None, 3, 5, None), 'ragged_rank': 4},
      {'batch_size': 12, 'shape': (2, 3, None, 4, 5, None), 'ragged_rank': 6},
  )
  def test_to_placeholder(self, shape, batch_size, ragged_rank):
    inp = layers.Input(shape=shape, batch_size=batch_size, ragged=True)
    self.assertEqual(inp.ragged_rank, ragged_rank)
    self.assertAllEqual(inp.shape, [batch_size] + list(shape))
    with tf.__internal__.FuncGraph('test').as_default():

            

Reported by Pylint.

                    {'batch_size': 7, 'shape': (None, 3, 5, None), 'ragged_rank': 4},
      {'batch_size': 12, 'shape': (2, 3, None, 4, 5, None), 'ragged_rank': 6},
  )
  def test_to_placeholder(self, shape, batch_size, ragged_rank):
    inp = layers.Input(shape=shape, batch_size=batch_size, ragged=True)
    self.assertEqual(inp.ragged_rank, ragged_rank)
    self.assertAllEqual(inp.shape, [batch_size] + list(shape))
    with tf.__internal__.FuncGraph('test').as_default():
      placeholder = inp._to_placeholder()

            

Reported by Pylint.

                    {'batch_size': 12, 'shape': (2, 3, None, 4, 5, None), 'ragged_rank': 6},
  )
  def test_to_placeholder(self, shape, batch_size, ragged_rank):
    inp = layers.Input(shape=shape, batch_size=batch_size, ragged=True)
    self.assertEqual(inp.ragged_rank, ragged_rank)
    self.assertAllEqual(inp.shape, [batch_size] + list(shape))
    with tf.__internal__.FuncGraph('test').as_default():
      placeholder = inp._to_placeholder()
      self.assertEqual(placeholder.ragged_rank, ragged_rank)

            

Reported by Pylint.

                )
  def test_to_placeholder(self, shape, batch_size, ragged_rank):
    inp = layers.Input(shape=shape, batch_size=batch_size, ragged=True)
    self.assertEqual(inp.ragged_rank, ragged_rank)
    self.assertAllEqual(inp.shape, [batch_size] + list(shape))
    with tf.__internal__.FuncGraph('test').as_default():
      placeholder = inp._to_placeholder()
      self.assertEqual(placeholder.ragged_rank, ragged_rank)
      self.assertAllEqual(placeholder.shape, [batch_size] + list(shape))

            

Reported by Pylint.

                def test_to_placeholder(self, shape, batch_size, ragged_rank):
    inp = layers.Input(shape=shape, batch_size=batch_size, ragged=True)
    self.assertEqual(inp.ragged_rank, ragged_rank)
    self.assertAllEqual(inp.shape, [batch_size] + list(shape))
    with tf.__internal__.FuncGraph('test').as_default():
      placeholder = inp._to_placeholder()
      self.assertEqual(placeholder.ragged_rank, ragged_rank)
      self.assertAllEqual(placeholder.shape, [batch_size] + list(shape))


            

Reported by Pylint.

              # pylint: disable=protected-access
"""Utilities related to layer/model functionality."""

import tensorflow.compat.v2 as tf

import functools
import weakref

import numpy as np

            

Reported by Pylint.

              import weakref

import numpy as np
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.utils.get_source_inputs')
def get_source_inputs(tensor, layer=None, node_index=None):
  """Returns the list of input tensors necessary to compute `tensor`.

            

Reported by Pylint.

              
def filter_empty_layer_containers(layer_list):
  """Filter out empty Layer-like containers and uniquify."""
  # TODO(b/130381733): Make this an attribute in base_layer.Layer.
  existing = set()
  to_visit = layer_list[::-1]
  while to_visit:
    obj = to_visit.pop()
    if id(obj) in existing:

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

import functools
import weakref

import numpy as np
from tensorflow.python.util.tf_export import keras_export


            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

import functools
import weakref

import numpy as np
from tensorflow.python.util.tf_export import keras_export



            

Reported by Pylint.

              
@keras_export('keras.utils.get_source_inputs')
def get_source_inputs(tensor, layer=None, node_index=None):
  """Returns the list of input tensors necessary to compute `tensor`.

  Output will always be a list of tensors
  (potentially with 1 element).

  Args:

            

Reported by Pylint.

                Returns:
      List of input tensors.
  """
  if not hasattr(tensor, '_keras_history'):
    return tensor

  if layer is None or node_index:
    layer, node_index, _ = tensor._keras_history
  if not layer._inbound_nodes:

            

Reported by Pylint.

                    List of input tensors.
  """
  if not hasattr(tensor, '_keras_history'):
    return tensor

  if layer is None or node_index:
    layer, node_index, _ = tensor._keras_history
  if not layer._inbound_nodes:
    return [tensor]

            

Reported by Pylint.

                if not hasattr(tensor, '_keras_history'):
    return tensor

  if layer is None or node_index:
    layer, node_index, _ = tensor._keras_history
  if not layer._inbound_nodes:
    return [tensor]
  else:
    node = layer._inbound_nodes[node_index]

            

Reported by Pylint.

                  return tensor

  if layer is None or node_index:
    layer, node_index, _ = tensor._keras_history
  if not layer._inbound_nodes:
    return [tensor]
  else:
    node = layer._inbound_nodes[node_index]
    if node.is_input:

            

Reported by Pylint.

              # ==============================================================================
"""TensorFlow-related utilities."""

import tensorflow.compat.v2 as tf

import collections
import copy
import numpy as np
from tensorflow.python.framework import ops

            

Reported by Pylint.

              import collections
import copy
import numpy as np
from tensorflow.python.framework import ops
from keras import backend as K
from keras.engine import keras_tensor
from keras.utils import object_identity
from keras.utils import tf_contextlib
from tensorflow.python.util.tf_export import keras_export

            

Reported by Pylint.

              from keras.engine import keras_tensor
from keras.utils import object_identity
from keras.utils import tf_contextlib
from tensorflow.python.util.tf_export import keras_export


def is_tensor_or_tensor_list(v):
  v = tf.nest.flatten(v)
  if v and isinstance(v[0], tf.Tensor):

            

Reported by Pylint.

                elif isinstance(tensor, tf.Variable):
    # Variables that are output of a Keras Layer in Functional API mode
    # should be considered symbolic.
    # TODO(omalleyt): We need a better way to check this in order to
    # enable `run_eagerly=True` for Models containing Layers that
    # return Variables as outputs.
    return (getattr(tensor, '_keras_history', False) or
            not tf.executing_eagerly())
  elif isinstance(tensor, tuple(_user_convertible_tensor_types)):

            

Reported by Pylint.

                Args:
    cls: A `class` type which shall be regarded as a symbolic `Tensor`.
  """
  global _user_convertible_tensor_types
  if cls not in _user_convertible_tensor_types:
    keras_tensor.register_keras_tensor_specialization(
        cls, keras_tensor.UserRegisteredTypeKerasTensor)
  _user_convertible_tensor_types.add(cls)


            

Reported by Pylint.

                if isinstance(t, tf.TypeSpec):
    spec = t
  elif is_extension_type(t):
    # TODO(b/148821952): Should these specs have a name attr?
    spec = t._type_spec
  elif (hasattr(t, '_keras_history') and
        hasattr(t._keras_history[0], '_type_spec')):
    return t._keras_history[0]._type_spec
  elif hasattr(t, 'shape') and hasattr(t, 'dtype'):

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

import collections
import copy
import numpy as np
from tensorflow.python.framework import ops
from keras import backend as K
from keras.engine import keras_tensor

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

import collections
import copy
import numpy as np
from tensorflow.python.framework import ops
from keras import backend as K
from keras.engine import keras_tensor
from keras.utils import object_identity

            

Reported by Pylint.

              from keras.engine import keras_tensor
from keras.utils import object_identity
from keras.utils import tf_contextlib
from tensorflow.python.util.tf_export import keras_export


def is_tensor_or_tensor_list(v):
  v = tf.nest.flatten(v)
  if v and isinstance(v[0], tf.Tensor):

            

Reported by Pylint.

              from tensorflow.python.util.tf_export import keras_export


def is_tensor_or_tensor_list(v):
  v = tf.nest.flatten(v)
  if v and isinstance(v[0], tf.Tensor):
    return True
  else:
    return False

            

Reported by Pylint.

              # limitations under the License.
# ==============================================================================
"""Contains the TFOpLambda layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

from keras import backend as K
from keras.engine import keras_tensor

            

Reported by Pylint.

              # limitations under the License.
# ==============================================================================
"""Contains the TFOpLambda layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

from keras import backend as K
from keras.engine import keras_tensor

            

Reported by Pylint.

              # limitations under the License.
# ==============================================================================
"""Contains the TFOpLambda layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

from keras import backend as K
from keras.engine import keras_tensor

            

Reported by Pylint.

              # ==============================================================================
"""Contains the TFOpLambda layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

from keras import backend as K
from keras.engine import keras_tensor
from keras.engine.base_layer import Layer

            

Reported by Pylint.

              """Contains the TFOpLambda layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import,g-bad-import-order
import tensorflow.compat.v2 as tf
# pylint: enable=g-bad-import-order

from keras import backend as K
from keras.engine import keras_tensor
from keras.engine.base_layer import Layer


            

Reported by Pylint.

              from keras.engine import keras_tensor
from keras.engine.base_layer import Layer

from tensorflow.python.platform import tf_logging
from tensorflow.python.util.tf_export import get_canonical_name_for_symbol
from tensorflow.python.util.tf_export import get_symbol_from_name


class ClassMethod(Layer):

            

Reported by Pylint.

              from keras.engine.base_layer import Layer

from tensorflow.python.platform import tf_logging
from tensorflow.python.util.tf_export import get_canonical_name_for_symbol
from tensorflow.python.util.tf_export import get_symbol_from_name


class ClassMethod(Layer):
  """Wraps a TF API Class's class method  in a `Layer` object.

            

Reported by Pylint.

              
from tensorflow.python.platform import tf_logging
from tensorflow.python.util.tf_export import get_canonical_name_for_symbol
from tensorflow.python.util.tf_export import get_symbol_from_name


class ClassMethod(Layer):
  """Wraps a TF API Class's class method  in a `Layer` object.


            

Reported by Pylint.

                  self._expects_training_arg = False
    self._expects_mask_arg = False

  def call(self, args, kwargs):
    return getattr(self.cls_ref, self.method_name)(*args, **kwargs)

  def get_config(self):
    if not self.cls_symbol:
      raise ValueError(

            

Reported by Pylint.

                  return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config, custom_objects=None):
    config = config.copy()
    symbol_name = config.pop('cls_symbol')
    cls_ref = get_symbol_from_name(symbol_name)
    if not cls_ref:
      raise ValueError(f'TensorFlow symbol `{symbol_name}` could not be found.')

            

Reported by Pylint.

              # ==============================================================================
"""Tests temporal sample weights correctness using Keras model."""

import tensorflow.compat.v2 as tf

import numpy as np

from keras import keras_parameterized
from keras import layers

            

Reported by Pylint.

              
import numpy as np

from keras import keras_parameterized
from keras import layers
from keras import metrics
from keras import optimizer_v2
from keras import testing_utils


            

Reported by Pylint.

              import numpy as np

from keras import keras_parameterized
from keras import layers
from keras import metrics
from keras import optimizer_v2
from keras import testing_utils



            

Reported by Pylint.

              
from keras import keras_parameterized
from keras import layers
from keras import metrics
from keras import optimizer_v2
from keras import testing_utils


class Bias(layers.Layer):

            

Reported by Pylint.

              from keras import keras_parameterized
from keras import layers
from keras import metrics
from keras import optimizer_v2
from keras import testing_utils


class Bias(layers.Layer):
  """Layer that add a bias to its inputs."""

            

Reported by Pylint.

              from keras import layers
from keras import metrics
from keras import optimizer_v2
from keras import testing_utils


class Bias(layers.Layer):
  """Layer that add a bias to its inputs."""


            

Reported by Pylint.

              class Bias(layers.Layer):
  """Layer that add a bias to its inputs."""

  def build(self, input_shape):
    self.bias = self.add_variable('bias', (1,), initializer='zeros')

  def call(self, inputs):
    return inputs + self.bias


            

Reported by Pylint.

                """Layer that add a bias to its inputs."""

  def build(self, input_shape):
    self.bias = self.add_variable('bias', (1,), initializer='zeros')

  def call(self, inputs):
    return inputs + self.bias

  def compute_output_shape(self, input_shape):

            

Reported by Pylint.

                      sample_weight_mode=[None, 'temporal'])
    fn(model)

    # TODO(b/129700800): Enable after bug is fixed.
    # model = get_compiled_multi_io_model_temporal(sample_weight_mode={
    #     'output_2': 'temporal'
    # })
    # fn(model)


            

Reported by Pylint.

              

class Bias(layers.Layer):
  """Layer that add a bias to its inputs."""

  def build(self, input_shape):
    self.bias = self.add_variable('bias', (1,), initializer='zeros')

  def call(self, inputs):

            

Reported by Pylint.

              # ==============================================================================
"""Tests for data_utils."""

import tensorflow.compat.v2 as tf

from itertools import cycle
import os
import tarfile
import urllib

            

Reported by Pylint.

                  path = keras.utils.data_utils.get_file('test.txt', origin,
                                           untar=True, cache_subdir=dest_dir)
    filepath = path + '.tar.gz'
    hashval_sha256 = keras.utils.data_utils._hash_file(filepath)
    hashval_md5 = keras.utils.data_utils._hash_file(filepath, algorithm='md5')
    path = keras.utils.data_utils.get_file(
        'test.txt', origin, md5_hash=hashval_md5,
        untar=True, cache_subdir=dest_dir)
    path = keras.utils.data_utils.get_file(

            

Reported by Pylint.

                                                         untar=True, cache_subdir=dest_dir)
    filepath = path + '.tar.gz'
    hashval_sha256 = keras.utils.data_utils._hash_file(filepath)
    hashval_md5 = keras.utils.data_utils._hash_file(filepath, algorithm='md5')
    path = keras.utils.data_utils.get_file(
        'test.txt', origin, md5_hash=hashval_md5,
        untar=True, cache_subdir=dest_dir)
    path = keras.utils.data_utils.get_file(
        filepath, origin, file_hash=hashval_sha256,

            

Reported by Pylint.

                  origin = urllib.parse.urljoin(
        'file://', urllib.request.pathname2url(os.path.abspath(zip_file_path)))

    hashval_sha256 = keras.utils.data_utils._hash_file(zip_file_path)
    hashval_md5 = keras.utils.data_utils._hash_file(zip_file_path,
                                                    algorithm='md5')
    path = keras.utils.data_utils.get_file(
        'test', origin, md5_hash=hashval_md5,
        extract=True, cache_subdir=dest_dir)

            

Reported by Pylint.

                      'file://', urllib.request.pathname2url(os.path.abspath(zip_file_path)))

    hashval_sha256 = keras.utils.data_utils._hash_file(zip_file_path)
    hashval_md5 = keras.utils.data_utils._hash_file(zip_file_path,
                                                    algorithm='md5')
    path = keras.utils.data_utils.get_file(
        'test', origin, md5_hash=hashval_md5,
        extract=True, cache_subdir=dest_dir)
    path = keras.utils.data_utils.get_file(

            

Reported by Pylint.

                                             (zip_file_path, True)]:
      origin = urllib.parse.urljoin(
          'file://', urllib.request.pathname2url(os.path.abspath(file_path)))
      hashval_sha256 = keras.utils.data_utils._hash_file(file_path)
      path = keras.utils.data_utils.get_file(
          origin=origin,
          file_hash=hashval_sha256,
          extract=extract,
          cache_subdir=dest_dir)

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

from itertools import cycle
import os
import tarfile
import urllib
import zipfile


            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

from itertools import cycle
import os
import tarfile
import urllib
import zipfile

import numpy as np

            

Reported by Pylint.

              
from itertools import cycle
import os
import tarfile
import urllib
import zipfile

import numpy as np


            

Reported by Pylint.

              from itertools import cycle
import os
import tarfile
import urllib
import zipfile

import numpy as np

import keras

            

Reported by Pylint.

              # ==============================================================================
"""Tests for kernelized.py."""

import tensorflow.compat.v2 as tf

import functools
import math
import os
import shutil

            

Reported by Pylint.

              import os
import shutil

from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import test_util
from keras import backend as keras_backend
from keras import combinations
from keras import initializers

            

Reported by Pylint.

              
from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import test_util
from keras import backend as keras_backend
from keras import combinations
from keras import initializers
from keras import testing_utils
from keras.engine import base_layer_utils

            

Reported by Pylint.

                def _assert_all_close(self, expected, actual, atol=0.001):
    if not tf.executing_eagerly():
      with self.cached_session() as sess:
        keras_backend._initialize_variables(sess)
        self.assertAllClose(expected, actual, atol=atol)
    else:
      self.assertAllClose(expected, actual, atol=atol)

  @testing_utils.run_v2_only

            

Reported by Pylint.

                  abs_error = tf.abs(exact_kernel_value - approx_kernel_value)
    if not tf.executing_eagerly():
      with self.cached_session() as sess:
        keras_backend._initialize_variables(sess)
        abs_error_eval = sess.run([abs_error])
        self.assertGreater(abs_error_eval[0][0], 0.05)
        self.assertLess(abs_error_eval[0][0], 0.5)
    else:
      self.assertGreater(abs_error, 0.05)

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

import functools
import math
import os
import shutil

from absl.testing import parameterized

            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

import functools
import math
import os
import shutil

from absl.testing import parameterized
import numpy as np

            

Reported by Pylint.

              
import functools
import math
import os
import shutil

from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import test_util

            

Reported by Pylint.

              import functools
import math
import os
import shutil

from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import test_util
from keras import backend as keras_backend

            

Reported by Pylint.

              

def _exact_gaussian(stddev):
  return functools.partial(
      kernelized_utils.exact_gaussian_kernel, stddev=stddev)


def _exact_laplacian(stddev):
  return functools.partial(

            

Reported by Pylint.

              # ==============================================================================
r"""Benchmarks on Keras components with different Keras model types."""

import tensorflow as tf

import time

import numpy as np


            

Reported by Pylint.

              
import numpy as np

from tensorflow.python.eager import context
from tensorflow.python.eager.context import get_executor


class SubclassedKerasModel(tf.keras.Model):


            

Reported by Pylint.

              import numpy as np

from tensorflow.python.eager import context
from tensorflow.python.eager.context import get_executor


class SubclassedKerasModel(tf.keras.Model):

  def __init__(self, initializer="ones"):

            

Reported by Pylint.

              
import tensorflow as tf

import time

import numpy as np

from tensorflow.python.eager import context
from tensorflow.python.eager.context import get_executor

            

Reported by Pylint.

              from tensorflow.python.eager.context import get_executor


class SubclassedKerasModel(tf.keras.Model):

  def __init__(self, initializer="ones"):
    super(SubclassedKerasModel, self).__init__()
    self.layer_a = tf.keras.layers.Dense(
        64, kernel_initializer=initializer, bias_initializer="zeros")

            

Reported by Pylint.

              from tensorflow.python.eager.context import get_executor


class SubclassedKerasModel(tf.keras.Model):

  def __init__(self, initializer="ones"):
    super(SubclassedKerasModel, self).__init__()
    self.layer_a = tf.keras.layers.Dense(
        64, kernel_initializer=initializer, bias_initializer="zeros")

            

Reported by Pylint.

              
class SubclassedKerasModel(tf.keras.Model):

  def __init__(self, initializer="ones"):
    super(SubclassedKerasModel, self).__init__()
    self.layer_a = tf.keras.layers.Dense(
        64, kernel_initializer=initializer, bias_initializer="zeros")
    self.layer_b = tf.keras.layers.Dense(
        128, kernel_initializer=initializer, bias_initializer="zeros")

            

Reported by Pylint.

              class SubclassedKerasModel(tf.keras.Model):

  def __init__(self, initializer="ones"):
    super(SubclassedKerasModel, self).__init__()
    self.layer_a = tf.keras.layers.Dense(
        64, kernel_initializer=initializer, bias_initializer="zeros")
    self.layer_b = tf.keras.layers.Dense(
        128, kernel_initializer=initializer, bias_initializer="zeros")
    self.layer_c = tf.keras.layers.Dense(

            

Reported by Pylint.

              class SubclassedKerasModel(tf.keras.Model):

  def __init__(self, initializer="ones"):
    super(SubclassedKerasModel, self).__init__()
    self.layer_a = tf.keras.layers.Dense(
        64, kernel_initializer=initializer, bias_initializer="zeros")
    self.layer_b = tf.keras.layers.Dense(
        128, kernel_initializer=initializer, bias_initializer="zeros")
    self.layer_c = tf.keras.layers.Dense(

            

Reported by Pylint.

              
  def __init__(self, initializer="ones"):
    super(SubclassedKerasModel, self).__init__()
    self.layer_a = tf.keras.layers.Dense(
        64, kernel_initializer=initializer, bias_initializer="zeros")
    self.layer_b = tf.keras.layers.Dense(
        128, kernel_initializer=initializer, bias_initializer="zeros")
    self.layer_c = tf.keras.layers.Dense(
        256, kernel_initializer=initializer, bias_initializer="zeros")

            

Reported by Pylint.