# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# pylint: disable=g-classes-have-attributes
"""Contains the core layers: Dense, Dropout.

Also contains their functional aliases.
"""
from __future__ import absolute_import

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow.compat.v2 as tf

import warnings

from keras import layers as keras_layers
from keras.legacy_tf_layers import base

            

Reported by Pylint.

              
from keras import layers as keras_layers
from keras.legacy_tf_layers import base
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export


@keras_export(v1=['keras.__internal__.legacy.layers.Dense'])
@tf_export(v1=['layers.Dense'])

            

Reported by Pylint.

              from keras import layers as keras_layers
from keras.legacy_tf_layers import base
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export


@keras_export(v1=['keras.__internal__.legacy.layers.Dense'])
@tf_export(v1=['layers.Dense'])
class Dense(keras_layers.Dense, base.Layer):

            

Reported by Pylint.

                ```
  @end_compatibility
  """
  pass


@keras_export(v1=['keras.__internal__.legacy.layers.flatten'])
@tf_export(v1=['layers.flatten'])
def flatten(inputs, name=None, data_format='channels_last'):

            

Reported by Pylint.

              
import tensorflow.compat.v2 as tf

import warnings

from keras import layers as keras_layers
from keras.legacy_tf_layers import base
from tensorflow.python.util.tf_export import keras_export
from tensorflow.python.util.tf_export import tf_export

            

Reported by Pylint.

              @keras_export(v1=['keras.__internal__.legacy.layers.Dense'])
@tf_export(v1=['layers.Dense'])
class Dense(keras_layers.Dense, base.Layer):
  """Densely-connected layer class.

  This layer implements the operation:
  `outputs = activation(inputs * kernel + bias)`
  Where `activation` is the activation function passed as the `activation`
  argument (if not `None`), `kernel` is a weights matrix created by the layer,

            

Reported by Pylint.

                @end_compatibility
  """

  def __init__(self, units,
               activation=None,
               use_bias=True,
               kernel_initializer=None,
               bias_initializer=tf.compat.v1.zeros_initializer(),
               kernel_regularizer=None,

            

Reported by Pylint.

                @end_compatibility
  """

  def __init__(self, units,
               activation=None,
               use_bias=True,
               kernel_initializer=None,
               bias_initializer=tf.compat.v1.zeros_initializer(),
               kernel_regularizer=None,

            

Reported by Pylint.

                             trainable=True,
               name=None,
               **kwargs):
    super(Dense, self).__init__(units=units,
                                activation=activation,
                                use_bias=use_bias,
                                kernel_initializer=kernel_initializer,
                                bias_initializer=bias_initializer,
                                kernel_regularizer=kernel_regularizer,

            

Reported by Pylint.

              # ==============================================================================
"""Benchmark for KPL implementation of vocabulary columns + indicator from lists with varying-length inputs."""

import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup

            

Reported by Pylint.

              
import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm


            

Reported by Pylint.

              import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.

            

Reported by Pylint.

              
import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()

            

Reported by Pylint.

              import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()


            

Reported by Pylint.

              from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()

NUM_REPEATS = 10

            

Reported by Pylint.

              # See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Benchmark for KPL implementation of vocabulary columns + indicator from lists with varying-length inputs."""

import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function

            

Reported by Pylint.

              

def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  vocab_size = 32768
  vocab = fc_bm.create_vocabulary(vocab_size)
  data = fc_bm.create_string_data(
      max_length, batch_size * NUM_REPEATS, vocab, pct_oov=0.15)

            

Reported by Pylint.

              def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  vocab_size = 32768
  vocab = fc_bm.create_vocabulary(vocab_size)
  data = fc_bm.create_string_data(
      max_length, batch_size * NUM_REPEATS, vocab, pct_oov=0.15)

  # Keras implementation

            

Reported by Pylint.

                """Benchmark a variable-length embedding."""
  # Data and constants.
  vocab_size = 32768
  vocab = fc_bm.create_vocabulary(vocab_size)
  data = fc_bm.create_string_data(
      max_length, batch_size * NUM_REPEATS, vocab, pct_oov=0.15)

  # Keras implementation
  model = keras.Sequential()

            

Reported by Pylint.

              # ==============================================================================
"""Benchmark for KPL implementation of weighted embedding column with varying-length inputs."""

import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm


            

Reported by Pylint.

              
import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()

            

Reported by Pylint.

              import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()


            

Reported by Pylint.

              
import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()

NUM_REPEATS = 10

            

Reported by Pylint.

              

### KPL AND FC IMPLEMENTATION BENCHMARKS ###
def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  embedding_size = 32768
  data = fc_bm.create_data(
      max_length, batch_size * NUM_REPEATS, embedding_size - 1, dtype=int)

            

Reported by Pylint.

              
### KPL AND FC IMPLEMENTATION BENCHMARKS ###
def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  embedding_size = 32768
  data = fc_bm.create_data(
      max_length, batch_size * NUM_REPEATS, embedding_size - 1, dtype=int)
  weight = tf.ones_like(data, dtype=tf.float32)

            

Reported by Pylint.

              def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  embedding_size = 32768
  data = fc_bm.create_data(
      max_length, batch_size * NUM_REPEATS, embedding_size - 1, dtype=int)
  weight = tf.ones_like(data, dtype=tf.float32)

  # Keras implementation

            

Reported by Pylint.

                """Benchmark a variable-length embedding."""
  # Data and constants.
  embedding_size = 32768
  data = fc_bm.create_data(
      max_length, batch_size * NUM_REPEATS, embedding_size - 1, dtype=int)
  weight = tf.ones_like(data, dtype=tf.float32)

  # Keras implementation
  data_input = keras.Input(

            

Reported by Pylint.

                embedding_size = 32768
  data = fc_bm.create_data(
      max_length, batch_size * NUM_REPEATS, embedding_size - 1, dtype=int)
  weight = tf.ones_like(data, dtype=tf.float32)

  # Keras implementation
  data_input = keras.Input(
      shape=(None,), ragged=True, name="data", dtype=tf.int64)
  weight_input = keras.Input(

            

Reported by Pylint.

                weight = tf.ones_like(data, dtype=tf.float32)

  # Keras implementation
  data_input = keras.Input(
      shape=(None,), ragged=True, name="data", dtype=tf.int64)
  weight_input = keras.Input(
      shape=(None,), ragged=True, name="weight", dtype=tf.float32)
  embedded_data = keras.layers.Embedding(embedding_size, 256)(data_input)
  weighted_embedding = tf.multiply(

            

Reported by Pylint.

              # ==============================================================================
"""Benchmark for KPL implementation of vocabulary columns + indicator from lists with dense inputs."""

import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup

            

Reported by Pylint.

              
import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm


            

Reported by Pylint.

              import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.

            

Reported by Pylint.

              
import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()

            

Reported by Pylint.

              import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()


            

Reported by Pylint.

              from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()

NUM_REPEATS = 10

            

Reported by Pylint.

              # See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Benchmark for KPL implementation of vocabulary columns + indicator from lists with dense inputs."""

import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function

            

Reported by Pylint.

              

def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  vocab_size = 32768
  vocab = fc_bm.create_vocabulary(vocab_size)
  data = fc_bm.create_string_data(
      max_length, batch_size * NUM_REPEATS, vocab, pct_oov=0.15)

            

Reported by Pylint.

              def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  vocab_size = 32768
  vocab = fc_bm.create_vocabulary(vocab_size)
  data = fc_bm.create_string_data(
      max_length, batch_size * NUM_REPEATS, vocab, pct_oov=0.15)

  # Keras implementation

            

Reported by Pylint.

                """Benchmark a variable-length embedding."""
  # Data and constants.
  vocab_size = 32768
  vocab = fc_bm.create_vocabulary(vocab_size)
  data = fc_bm.create_string_data(
      max_length, batch_size * NUM_REPEATS, vocab, pct_oov=0.15)

  # Keras implementation
  model = keras.Sequential()

            

Reported by Pylint.

              # ==============================================================================
"""Classes and functions implementing to Model SavedModel serialization."""

from keras.saving import saving_utils
from keras.saving.saved_model import constants
from keras.saving.saved_model import layer_serialization
from keras.saving.saved_model import save_impl



            

Reported by Pylint.

              """Classes and functions implementing to Model SavedModel serialization."""

from keras.saving import saving_utils
from keras.saving.saved_model import constants
from keras.saving.saved_model import layer_serialization
from keras.saving.saved_model import save_impl


class ModelSavedModelSaver(layer_serialization.LayerSavedModelSaver):

            

Reported by Pylint.

              
from keras.saving import saving_utils
from keras.saving.saved_model import constants
from keras.saving.saved_model import layer_serialization
from keras.saving.saved_model import save_impl


class ModelSavedModelSaver(layer_serialization.LayerSavedModelSaver):
  """Model SavedModel serialization."""

            

Reported by Pylint.

              from keras.saving import saving_utils
from keras.saving.saved_model import constants
from keras.saving.saved_model import layer_serialization
from keras.saving.saved_model import save_impl


class ModelSavedModelSaver(layer_serialization.LayerSavedModelSaver):
  """Model SavedModel serialization."""


            

Reported by Pylint.

              from keras.saving.saved_model import save_impl


class ModelSavedModelSaver(layer_serialization.LayerSavedModelSaver):
  """Model SavedModel serialization."""

  @property
  def object_identifier(self):
    return constants.MODEL_IDENTIFIER

            

Reported by Pylint.

              

class ModelSavedModelSaver(layer_serialization.LayerSavedModelSaver):
  """Model SavedModel serialization."""

  @property
  def object_identifier(self):
    return constants.MODEL_IDENTIFIER


            

Reported by Pylint.

              class ModelSavedModelSaver(layer_serialization.LayerSavedModelSaver):
  """Model SavedModel serialization."""

  @property
  def object_identifier(self):
    return constants.MODEL_IDENTIFIER

  def _python_properties_internal(self):
    metadata = super(ModelSavedModelSaver, self)._python_properties_internal()

            

Reported by Pylint.

                """Model SavedModel serialization."""

  @property
  def object_identifier(self):
    return constants.MODEL_IDENTIFIER

  def _python_properties_internal(self):
    metadata = super(ModelSavedModelSaver, self)._python_properties_internal()
    # Network stateful property is dependent on the child layers.

            

Reported by Pylint.

                """Model SavedModel serialization."""

  @property
  def object_identifier(self):
    return constants.MODEL_IDENTIFIER

  def _python_properties_internal(self):
    metadata = super(ModelSavedModelSaver, self)._python_properties_internal()
    # Network stateful property is dependent on the child layers.

            

Reported by Pylint.

              
  @property
  def object_identifier(self):
    return constants.MODEL_IDENTIFIER

  def _python_properties_internal(self):
    metadata = super(ModelSavedModelSaver, self)._python_properties_internal()
    # Network stateful property is dependent on the child layers.
    metadata.pop('stateful')

            

Reported by Pylint.

              # ==============================================================================
"""Keras string lookup preprocessing layer."""

# pylint: disable=g-classes-have-attributes

from keras.engine import base_preprocessing_layer
from keras.layers.preprocessing import index_lookup
import numpy as np
import tensorflow.compat.v2 as tf

            

Reported by Pylint.

              from keras.engine import base_preprocessing_layer
from keras.layers.preprocessing import index_lookup
import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export


@keras_export(

            

Reported by Pylint.

              from keras.layers.preprocessing import index_lookup
import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export


@keras_export(
    "keras.layers.IntegerLookup",

            

Reported by Pylint.

              import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export


@keras_export(
    "keras.layers.IntegerLookup",
    "keras.layers.experimental.preprocessing.IntegerLookup",

            

Reported by Pylint.

                  "keras.layers.experimental.preprocessing.IntegerLookup",
    v1=[])
class IntegerLookup(index_lookup.IndexLookup):
  """Reindex integer inputs to be in a contiguous range, via a dict lookup.

  This layer maps a set of arbitrary integer input tokens into indexed
  integer output via a table-based vocabulary lookup. The layer's output indices
  will be contiguously arranged up to the maximum vocab size, even if the input
  tokens are non-continguous or unbounded. The layer supports multiple options

            

Reported by Pylint.

                either directly or via `adapt()` before calling `get_vocabulary()`.
  """

  def __init__(self,
               max_tokens=None,
               num_oov_indices=1,
               mask_token=None,
               oov_token=-1,
               vocabulary=None,

            

Reported by Pylint.

                either directly or via `adapt()` before calling `get_vocabulary()`.
  """

  def __init__(self,
               max_tokens=None,
               num_oov_indices=1,
               mask_token=None,
               oov_token=-1,
               vocabulary=None,

            

Reported by Pylint.

                             sparse=False,
               pad_to_max_tokens=False,
               **kwargs):
    allowed_dtypes = [tf.int64]

    # Support deprecated args for this layer.
    if "max_values" in kwargs:
      logging.log_first_n(logging.WARN,
                          "max_values is deprecated, use max_tokens instead.",

            

Reported by Pylint.

                  allowed_dtypes = [tf.int64]

    # Support deprecated args for this layer.
    if "max_values" in kwargs:
      logging.log_first_n(logging.WARN,
                          "max_values is deprecated, use max_tokens instead.",
                          1)
      max_tokens = kwargs["max_values"]
      del kwargs["max_values"]

            

Reported by Pylint.

              
    # Support deprecated args for this layer.
    if "max_values" in kwargs:
      logging.log_first_n(logging.WARN,
                          "max_values is deprecated, use max_tokens instead.",
                          1)
      max_tokens = kwargs["max_values"]
      del kwargs["max_values"]
    if "mask_value" in kwargs:

            

Reported by Pylint.

              import os
import tempfile

from absl import flags

import tensorflow as tf
import tensorflow_text as tf_text



            

Reported by Pylint.

              
from absl import flags

import tensorflow as tf
import tensorflow_text as tf_text


class ConvertResource(tf.test.TestCase):


            

Reported by Pylint.

              from absl import flags

import tensorflow as tf
import tensorflow_text as tf_text


class ConvertResource(tf.test.TestCase):

  def testConvertResource(self):

            

Reported by Pylint.

              # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

            

Reported by Pylint.

              import tensorflow_text as tf_text


class ConvertResource(tf.test.TestCase):

  def testConvertResource(self):
    """Test general resource inputs don't crash the converter."""
    if not tf.test.is_built_with_cuda():
      self.skipTest('test is only applicable with CUDA')

            

Reported by Pylint.

              import tensorflow_text as tf_text


class ConvertResource(tf.test.TestCase):

  def testConvertResource(self):
    """Test general resource inputs don't crash the converter."""
    if not tf.test.is_built_with_cuda():
      self.skipTest('test is only applicable with CUDA')

            

Reported by Pylint.

              
class ConvertResource(tf.test.TestCase):

  def testConvertResource(self):
    """Test general resource inputs don't crash the converter."""
    if not tf.test.is_built_with_cuda():
      self.skipTest('test is only applicable with CUDA')

    class TokenizeLayer(tf.keras.layers.Layer):

            

Reported by Pylint.

              
class ConvertResource(tf.test.TestCase):

  def testConvertResource(self):
    """Test general resource inputs don't crash the converter."""
    if not tf.test.is_built_with_cuda():
      self.skipTest('test is only applicable with CUDA')

    class TokenizeLayer(tf.keras.layers.Layer):

            

Reported by Pylint.

              class ConvertResource(tf.test.TestCase):

  def testConvertResource(self):
    """Test general resource inputs don't crash the converter."""
    if not tf.test.is_built_with_cuda():
      self.skipTest('test is only applicable with CUDA')

    class TokenizeLayer(tf.keras.layers.Layer):


            

Reported by Pylint.

              
  def testConvertResource(self):
    """Test general resource inputs don't crash the converter."""
    if not tf.test.is_built_with_cuda():
      self.skipTest('test is only applicable with CUDA')

    class TokenizeLayer(tf.keras.layers.Layer):

      def __init__(self, vocab_file):

            

Reported by Pylint.

              # ==============================================================================
"""Keras model saving code."""

import tensorflow.compat.v2 as tf
from keras.saving import hdf5_format
from keras.saving import saving_utils
from keras.saving.saved_model import load as saved_model_load
from keras.saving.saved_model import load_context
from keras.saving.saved_model import save as saved_model_save

            

Reported by Pylint.

              from keras.utils import generic_utils
from keras.utils import traceback_utils
from keras.utils.io_utils import path_to_string
from tensorflow.python.util.tf_export import keras_export

# pylint: disable=g-import-not-at-top
try:
  import h5py
except ImportError:

            

Reported by Pylint.

              from keras.utils.io_utils import path_to_string
from tensorflow.python.util.tf_export import keras_export

# pylint: disable=g-import-not-at-top
try:
  import h5py
except ImportError:
  h5py = None
# pylint: enable=g-import-not-at-top

            

Reported by Pylint.

                import h5py
except ImportError:
  h5py = None
# pylint: enable=g-import-not-at-top


@keras_export('keras.models.save_model')
@traceback_utils.filter_traceback
def save_model(model,

            

Reported by Pylint.

                    ImportError: If save format is hdf5, and h5py is not available.
  """
  # pylint: enable=line-too-long
  from keras.engine import sequential  # pylint: disable=g-import-not-at-top

  default_format = 'tf' if tf.__internal__.tf2.enabled() else 'h5'
  save_format = save_format or default_format

  filepath = path_to_string(filepath)

            

Reported by Pylint.

                if (save_format == 'h5' or
      (h5py is not None and isinstance(filepath, h5py.File)) or
      saving_utils.is_hdf5_filepath(filepath)):
    # TODO(b/130258301): add utility method for detecting model type.
    if (not model._is_graph_network and  # pylint:disable=protected-access
        not isinstance(model, sequential.Sequential)):
      raise NotImplementedError(
          'Saving the model to HDF5 format requires the model to be a '
          'Functional model or a Sequential model. It does not work for '

            

Reported by Pylint.

              
# pylint: disable=g-import-not-at-top
try:
  import h5py
except ImportError:
  h5py = None
# pylint: enable=g-import-not-at-top



            

Reported by Pylint.

              try:
  import h5py
except ImportError:
  h5py = None
# pylint: enable=g-import-not-at-top


@keras_export('keras.models.save_model')
@traceback_utils.filter_traceback

            

Reported by Pylint.

              
@keras_export('keras.models.save_model')
@traceback_utils.filter_traceback
def save_model(model,
               filepath,
               overwrite=True,
               include_optimizer=True,
               save_format=None,
               signatures=None,

            

Reported by Pylint.

                             options=None,
               save_traces=True):
  # pylint: disable=line-too-long
  """Saves a model as a TensorFlow SavedModel or HDF5 file.

  See the [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
  for details.

  Usage:

            

Reported by Pylint.

              # ==============================================================================
"""Tests for keras.layers.preprocessing.normalization."""

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 keras_parameterized

            

Reported by Pylint.

              

def _get_layer_computation_test_cases():
  test_cases = ({
      "adapt_data": np.array([[1.], [2.], [3.], [4.], [5.]], dtype=np.float32),
      "axis": -1,
      "test_data": np.array([[1.], [2.], [3.]], np.float32),
      "expected": np.array([[-1.414214], [-.707107], [0]], np.float32),
      "testcase_name": "2d_single_element"

            

Reported by Pylint.

                        "3d_multiple_axis"
  })

  crossed_test_cases = []
  # Cross above test cases with use_dataset in (True, False)
  for use_dataset in (True, False):
    for case in test_cases:
      case = case.copy()
      if use_dataset:

            

Reported by Pylint.

              
  crossed_test_cases = []
  # Cross above test cases with use_dataset in (True, False)
  for use_dataset in (True, False):
    for case in test_cases:
      case = case.copy()
      if use_dataset:
        case["testcase_name"] = case["testcase_name"] + "_with_dataset"
      case["use_dataset"] = use_dataset

            

Reported by Pylint.

                crossed_test_cases = []
  # Cross above test cases with use_dataset in (True, False)
  for use_dataset in (True, False):
    for case in test_cases:
      case = case.copy()
      if use_dataset:
        case["testcase_name"] = case["testcase_name"] + "_with_dataset"
      case["use_dataset"] = use_dataset
      crossed_test_cases.append(case)

            

Reported by Pylint.

                # Cross above test cases with use_dataset in (True, False)
  for use_dataset in (True, False):
    for case in test_cases:
      case = case.copy()
      if use_dataset:
        case["testcase_name"] = case["testcase_name"] + "_with_dataset"
      case["use_dataset"] = use_dataset
      crossed_test_cases.append(case)


            

Reported by Pylint.

                for use_dataset in (True, False):
    for case in test_cases:
      case = case.copy()
      if use_dataset:
        case["testcase_name"] = case["testcase_name"] + "_with_dataset"
      case["use_dataset"] = use_dataset
      crossed_test_cases.append(case)

  return crossed_test_cases

            

Reported by Pylint.

                  for case in test_cases:
      case = case.copy()
      if use_dataset:
        case["testcase_name"] = case["testcase_name"] + "_with_dataset"
      case["use_dataset"] = use_dataset
      crossed_test_cases.append(case)

  return crossed_test_cases


            

Reported by Pylint.

                    case = case.copy()
      if use_dataset:
        case["testcase_name"] = case["testcase_name"] + "_with_dataset"
      case["use_dataset"] = use_dataset
      crossed_test_cases.append(case)

  return crossed_test_cases



            

Reported by Pylint.

              # ==============================================================================
"""Tests for io_utils."""

import tensorflow.compat.v2 as tf

import builtins
from pathlib import Path

from keras import keras_parameterized

            

Reported by Pylint.

                def test_ask_to_proceed_with_overwrite(self):
    with tf.compat.v1.test.mock.patch.object(builtins, 'input') as mock_log:
      mock_log.return_value = 'y'
      self.assertTrue(io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

      mock_log.return_value = 'n'
      self.assertFalse(
          io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))


            

Reported by Bandit.

              
      mock_log.return_value = 'n'
      self.assertFalse(
          io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

      mock_log.side_effect = ['m', 'y']
      self.assertTrue(io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

      mock_log.side_effect = ['m', 'n']

            

Reported by Bandit.

                        io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

      mock_log.side_effect = ['m', 'y']
      self.assertTrue(io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

      mock_log.side_effect = ['m', 'n']
      self.assertFalse(
          io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))


            

Reported by Bandit.

              
      mock_log.side_effect = ['m', 'n']
      self.assertFalse(
          io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

  def test_path_to_string(self):

    class PathLikeDummy:


            

Reported by Bandit.

              
import tensorflow.compat.v2 as tf

import builtins
from pathlib import Path

from keras import keras_parameterized
from keras.utils import io_utils


            

Reported by Pylint.

              import tensorflow.compat.v2 as tf

import builtins
from pathlib import Path

from keras import keras_parameterized
from keras.utils import io_utils



            

Reported by Pylint.

              from keras.utils import io_utils


class TestIOUtils(keras_parameterized.TestCase):

  def test_ask_to_proceed_with_overwrite(self):
    with tf.compat.v1.test.mock.patch.object(builtins, 'input') as mock_log:
      mock_log.return_value = 'y'
      self.assertTrue(io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

            

Reported by Pylint.

              
class TestIOUtils(keras_parameterized.TestCase):

  def test_ask_to_proceed_with_overwrite(self):
    with tf.compat.v1.test.mock.patch.object(builtins, 'input') as mock_log:
      mock_log.return_value = 'y'
      self.assertTrue(io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

      mock_log.return_value = 'n'

            

Reported by Pylint.

              
class TestIOUtils(keras_parameterized.TestCase):

  def test_ask_to_proceed_with_overwrite(self):
    with tf.compat.v1.test.mock.patch.object(builtins, 'input') as mock_log:
      mock_log.return_value = 'y'
      self.assertTrue(io_utils.ask_to_proceed_with_overwrite('/tmp/not_exists'))

      mock_log.return_value = 'n'

            

Reported by Pylint.