"""Utilities for preprocessing sequence data."""
# pylint: disable=invalid-name

from keras_preprocessing import sequence

from keras.utils import data_utils
from tensorflow.python.util.tf_export import keras_export

make_sampling_table = sequence.make_sampling_table

            

Reported by Pylint.

              from keras_preprocessing import sequence

from keras.utils import data_utils
from tensorflow.python.util.tf_export import keras_export

make_sampling_table = sequence.make_sampling_table
skipgrams = sequence.skipgrams
# TODO(fchollet): consider making `_remove_long_seq` public.
_remove_long_seq = sequence._remove_long_seq  # pylint: disable=protected-access

            

Reported by Pylint.

              
make_sampling_table = sequence.make_sampling_table
skipgrams = sequence.skipgrams
# TODO(fchollet): consider making `_remove_long_seq` public.
_remove_long_seq = sequence._remove_long_seq  # pylint: disable=protected-access


@keras_export('keras.preprocessing.sequence.TimeseriesGenerator')
class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.Sequence):

            

Reported by Pylint.

              

@keras_export('keras.preprocessing.sequence.TimeseriesGenerator')
class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.Sequence):
  """Utility class for generating batches of temporal data.

  This class takes in a sequence of data-points gathered at
  equal intervals, along with time series parameters such as
  stride, length of history, etc., to produce batches for

            

Reported by Pylint.

              

@keras_export('keras.preprocessing.sequence.TimeseriesGenerator')
class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.Sequence):
  """Utility class for generating batches of temporal data.

  This class takes in a sequence of data-points gathered at
  equal intervals, along with time series parameters such as
  stride, length of history, etc., to produce batches for

            

Reported by Pylint.

                                      np.array([[10], [11]]))
  ```
  """
  pass


@keras_export('keras.preprocessing.sequence.pad_sequences')
def pad_sequences(sequences, maxlen=None, dtype='int32',
                  padding='pre', truncating='pre', value=0.):

            

Reported by Pylint.

              

@keras_export('keras.preprocessing.sequence.TimeseriesGenerator')
class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.Sequence):
  """Utility class for generating batches of temporal data.

  This class takes in a sequence of data-points gathered at
  equal intervals, along with time series parameters such as
  stride, length of history, etc., to produce batches for

            

Reported by Pylint.

              
@keras_export('keras.preprocessing.sequence.TimeseriesGenerator')
class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.Sequence):
  """Utility class for generating batches of temporal data.

  This class takes in a sequence of data-points gathered at
  equal intervals, along with time series parameters such as
  stride, length of history, etc., to produce batches for
  training/validation.

            

Reported by Pylint.

                                      np.array([[10], [11]]))
  ```
  """
  pass


@keras_export('keras.preprocessing.sequence.pad_sequences')
def pad_sequences(sequences, maxlen=None, dtype='int32',
                  padding='pre', truncating='pre', value=0.):

            

Reported by Pylint.

              

@keras_export('keras.preprocessing.sequence.pad_sequences')
def pad_sequences(sequences, maxlen=None, dtype='int32',
                  padding='pre', truncating='pre', value=0.):
  """Pads sequences to the same length.

  This function transforms a list (of length `num_samples`)
  of sequences (lists of integers)

            

Reported by Pylint.

              # ==============================================================================
"""Tests for distributed training utility functions."""

import tensorflow.compat.v2 as tf

from keras import callbacks
from keras.distribute import distributed_training_utils_v1
from keras.optimizer_v2 import adam


            

Reported by Pylint.

              from keras.optimizer_v2 import adam


class DistributedTrainingUtilsTest(tf.test.TestCase):

  def test_validate_callbacks_predefined_callbacks(self):
    supported_predefined_callbacks = [
        callbacks.TensorBoard(),
        callbacks.CSVLogger(filename='./log.csv'),

            

Reported by Pylint.

              from keras.optimizer_v2 import adam


class DistributedTrainingUtilsTest(tf.test.TestCase):

  def test_validate_callbacks_predefined_callbacks(self):
    supported_predefined_callbacks = [
        callbacks.TensorBoard(),
        callbacks.CSVLogger(filename='./log.csv'),

            

Reported by Pylint.

              
class DistributedTrainingUtilsTest(tf.test.TestCase):

  def test_validate_callbacks_predefined_callbacks(self):
    supported_predefined_callbacks = [
        callbacks.TensorBoard(),
        callbacks.CSVLogger(filename='./log.csv'),
        callbacks.EarlyStopping(),
        callbacks.ModelCheckpoint(filepath='./checkpoint'),

            

Reported by Pylint.

              
class DistributedTrainingUtilsTest(tf.test.TestCase):

  def test_validate_callbacks_predefined_callbacks(self):
    supported_predefined_callbacks = [
        callbacks.TensorBoard(),
        callbacks.CSVLogger(filename='./log.csv'),
        callbacks.EarlyStopping(),
        callbacks.ModelCheckpoint(filepath='./checkpoint'),

            

Reported by Pylint.

              class DistributedTrainingUtilsTest(tf.test.TestCase):

  def test_validate_callbacks_predefined_callbacks(self):
    supported_predefined_callbacks = [
        callbacks.TensorBoard(),
        callbacks.CSVLogger(filename='./log.csv'),
        callbacks.EarlyStopping(),
        callbacks.ModelCheckpoint(filepath='./checkpoint'),
        callbacks.TerminateOnNaN(),

            

Reported by Pylint.

                      callbacks.RemoteMonitor()
    ]

    distributed_training_utils_v1.validate_callbacks(
        supported_predefined_callbacks, adam.Adam())

    unsupported_predefined_callbacks = [
        callbacks.ReduceLROnPlateau(),
        callbacks.LearningRateScheduler(schedule=lambda epoch: 0.001)

            

Reported by Pylint.

                  distributed_training_utils_v1.validate_callbacks(
        supported_predefined_callbacks, adam.Adam())

    unsupported_predefined_callbacks = [
        callbacks.ReduceLROnPlateau(),
        callbacks.LearningRateScheduler(schedule=lambda epoch: 0.001)
    ]

    for callback in unsupported_predefined_callbacks:

            

Reported by Pylint.

                      callbacks.LearningRateScheduler(schedule=lambda epoch: 0.001)
    ]

    for callback in unsupported_predefined_callbacks:
      with self.assertRaisesRegex(ValueError,
                                  'You must specify a Keras Optimizer V2'):
        distributed_training_utils_v1.validate_callbacks(
            [callback], tf.compat.v1.train.AdamOptimizer())


            

Reported by Pylint.

                  ]

    for callback in unsupported_predefined_callbacks:
      with self.assertRaisesRegex(ValueError,
                                  'You must specify a Keras Optimizer V2'):
        distributed_training_utils_v1.validate_callbacks(
            [callback], tf.compat.v1.train.AdamOptimizer())



            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_nasnet_large(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_nasnet_large(self):
    app = tf.keras.applications.NASNetLarge
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_nasnet_large(self):
    app = tf.keras.applications.NASNetLarge
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_nasnet_large(self):
    app = tf.keras.applications.NASNetLarge
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_nasnet_large(self):
    app = tf.keras.applications.NASNetLarge
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_nasnet_large(self):
    app = tf.keras.applications.NASNetLarge
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_nasnet_large(self):
    app = tf.keras.applications.NASNetLarge
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])

if __name__ == '__main__':

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_resnet152_v2(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_resnet152_v2(self):
    app = tf.keras.applications.ResNet152V2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_resnet152_v2(self):
    app = tf.keras.applications.ResNet152V2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_resnet152_v2(self):
    app = tf.keras.applications.ResNet152V2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_resnet152_v2(self):
    app = tf.keras.applications.ResNet152V2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_resnet152_v2(self):
    app = tf.keras.applications.ResNet152V2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_resnet152_v2(self):
    app = tf.keras.applications.ResNet152V2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])



            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_inception_resnet_v2(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_inception_resnet_v2(self):
    app = tf.keras.applications.InceptionResNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_inception_resnet_v2(self):
    app = tf.keras.applications.InceptionResNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_inception_resnet_v2(self):
    app = tf.keras.applications.InceptionResNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_inception_resnet_v2(self):
    app = tf.keras.applications.InceptionResNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_inception_resnet_v2(self):
    app = tf.keras.applications.InceptionResNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_inception_resnet_v2(self):
    app = tf.keras.applications.InceptionResNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])



            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_efficient_net_b7(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_efficient_net_b7(self):
    app = tf.keras.applications.EfficientNetB7
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_efficient_net_b7(self):
    app = tf.keras.applications.EfficientNetB7
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_efficient_net_b7(self):
    app = tf.keras.applications.EfficientNetB7
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_efficient_net_b7(self):
    app = tf.keras.applications.EfficientNetB7
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_efficient_net_b7(self):
    app = tf.keras.applications.EfficientNetB7
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_efficient_net_b7(self):
    app = tf.keras.applications.EfficientNetB7
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])

if __name__ == '__main__':

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_densenet_201(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_densenet_201(self):
    app = tf.keras.applications.DenseNet201
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_densenet_201(self):
    app = tf.keras.applications.DenseNet201
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_densenet_201(self):
    app = tf.keras.applications.DenseNet201
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_densenet_201(self):
    app = tf.keras.applications.DenseNet201
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_densenet_201(self):
    app = tf.keras.applications.DenseNet201
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_densenet_201(self):
    app = tf.keras.applications.DenseNet201
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])

if __name__ == '__main__':

            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_vgg19(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_vgg19(self):
    app = tf.keras.applications.VGG19
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_vgg19(self):
    app = tf.keras.applications.VGG19
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_vgg19(self):
    app = tf.keras.applications.VGG19
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_vgg19(self):
    app = tf.keras.applications.VGG19
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_vgg19(self):
    app = tf.keras.applications.VGG19
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_vgg19(self):
    app = tf.keras.applications.VGG19
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])



            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_xception(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_xception(self):
    app = tf.keras.applications.Xception
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_xception(self):
    app = tf.keras.applications.Xception
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_xception(self):
    app = tf.keras.applications.Xception
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_xception(self):
    app = tf.keras.applications.Xception
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_xception(self):
    app = tf.keras.applications.Xception
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_xception(self):
    app = tf.keras.applications.Xception
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])



            

Reported by Pylint.

              from __future__ import division
from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):


            

Reported by Pylint.

              from __future__ import print_function

import tensorflow as tf
from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_mobilenet_v2(self):

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_mobilenet_v2(self):
    app = tf.keras.applications.MobileNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              from keras.benchmarks.saved_model_benchmarks import saved_model_benchmark_util


class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_mobilenet_v2(self):
    app = tf.keras.applications.MobileNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_mobilenet_v2(self):
    app = tf.keras.applications.MobileNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              
class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_mobilenet_v2(self):
    app = tf.keras.applications.MobileNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(

            

Reported by Pylint.

              class BenchmarkSaveApplications(tf.test.Benchmark):

  def benchmark_save_and_load_mobilenet_v2(self):
    app = tf.keras.applications.MobileNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],

            

Reported by Pylint.

              
  def benchmark_save_and_load_mobilenet_v2(self):
    app = tf.keras.applications.MobileNetV2
    save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],

            

Reported by Pylint.

                  save_result, load_result = (
        saved_model_benchmark_util.save_and_load_benchmark(app))

    self.report_benchmark(
        iters=save_result['iters'],
        wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(

            

Reported by Pylint.

                      wall_time=save_result['wall_time'],
        name=save_result['name'])

    self.report_benchmark(
        iters=load_result['iters'],
        wall_time=load_result['wall_time'],
        name=load_result['name'])

if __name__ == '__main__':

            

Reported by Pylint.