text: """
   Copyright (c) 2020 Intel Corporation
   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
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
  """
  
  import tensorflow as tf
  from tensorflow.python.ops.init_ops import Constant
  import numpy as np
  
  from nncf import create_compressed_model
  from nncf.configs.config import Config
  
  from examples.tensorflow.common.object_detection.datasets.builder import COCODatasetBuilder
  
  
  def get_conv_init_value(shape, value):
      mask = np.eye(shape[0], shape[1])
      mask = np.expand_dims(mask, axis=(2, 3))
      value = np.full(shape, value)
      value += mask
      return value
  
  
  def get_empty_config(input_sample_sizes=None):
      if input_sample_sizes is None:
          input_sample_sizes = [1, 4, 4, 1]
  
      def _create_input_info():
          if isinstance(input_sample_sizes, tuple):
              return [{"sample_size": sizes} for sizes in input_sample_sizes]
          return [{"sample_size": input_sample_sizes}]
  
      config = Config({
          "model": "basic_sparse_conv",
          "input_info": _create_input_info()
      })
      return config
  
  
  def get_mock_model(input_shape=(1,)):
      inputs = tf.keras.Input(shape=input_shape)
      outputs = tf.keras.layers.Dense(1)(inputs)
      return tf.keras.Model(inputs=inputs, outputs=outputs)
  
  
  def get_basic_conv_test_model(input_shape=(4, 4, 1), out_channels=2, kernel_size=2, weight_init=-1., bias_init=-2.):
      inputs = tf.keras.Input(shape=input_shape)
      outputs = create_conv(input_shape[-1], out_channels, kernel_size, weight_init, bias_init)(inputs)
      return tf.keras.Model(inputs=inputs, outputs=outputs)
  
  
  def create_compressed_model_and_algo_for_test(model, config):
      assert isinstance(config, Config)
      tf.keras.backend.clear_session()
      algo, model = create_compressed_model(model, config)
      return model, algo
  
  
  def create_conv(in_channels, out_channels, kernel_size, weight_init, bias_init):
      weight_init = get_conv_init_value((kernel_size, kernel_size, in_channels, out_channels), weight_init)
      conv = tf.keras.layers.Conv2D(out_channels,
                                    kernel_size,
                                    kernel_initializer=Constant(weight_init),
                                    bias_initializer=Constant(bias_init))
      return conv
  
  
  def check_equal(test, reference, rtol=1e-4):
      test = test.numpy()
      reference = reference.numpy()
      for i, (x, y) in enumerate(zip(test, reference)):
          np.testing.assert_allclose(x, y, rtol=rtol, err_msg="Index: {}".format(i))
  
  
  class MockCOCODatasetBuilder(COCODatasetBuilder):
      @property
      def num_examples(self):
          return 5
  
  
  def get_coco_dataset_builders(config, strategy):
      num_devices = strategy.num_replicas_in_sync if strategy else 1
  
      train_builder = MockCOCODatasetBuilder(config=config,
                                             is_train=True,
                                             num_devices=num_devices)
  
      val_builder = MockCOCODatasetBuilder(config=config,
                                           is_train=False,
                                           num_devices=num_devices)
  
      return train_builder, val_builder
  