# Copyright 2023 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
# 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.

"""Utils for testing."""

import tensorflow as tf


class FakeKerasModel(tf.keras.Model):
  """Fake keras model for testing."""

  def __init__(self):
    super().__init__()
    self.dense = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(4, activation=tf.nn.relu)

  def call(self, inputs):  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
    return self.dense2(self.dense(inputs))


class _Dense(tf.Module):
  """A dense layer."""

  def __init__(self, input_dim, output_size, name=None):
    super().__init__(name=name)
    with self.name_scope:
      self.w = tf.Variable(
          tf.random.normal([input_dim, output_size]), name='w')
      self.b = tf.Variable(tf.zeros([output_size]), name='b')

  @tf.Module.with_name_scope
  def __call__(self, x):
    y = tf.matmul(x, self.w) + self.b
    return tf.nn.relu(y)


class FakeModule(tf.Module):
  """Fake model using tf.Module for testing."""

  def __init__(self, input_size, name=None):
    super().__init__(name=name)
    with self.name_scope:
      self.dense = _Dense(input_size, 4, name='dense')
      self.dense2 = _Dense(4, 4, name='dense_1')

  @tf.Module.with_name_scope
  def __call__(self, x):
    return self.dense2(self.dense(x))
