# Copyright 2015 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.
# ==============================================================================
"""Contains the Dropout layer."""


import tensorflow.compat.v2 as tf

from keras import backend
from keras.engine import base_layer
from keras.utils import control_flow_util

# isort: off
from tensorflow.python.util.tf_export import keras_export


@keras_export("keras.layers.Dropout")
class Dropout(base_layer.BaseRandomLayer):
    """Applies Dropout to the input.

    The Dropout layer randomly sets input units to 0 with a frequency of `rate`
    at each step during training time, which helps prevent overfitting.
    Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over
    all inputs is unchanged.

    Note that the Dropout layer only applies when `training` is set to True
    such that no values are dropped during inference. When using `model.fit`,
    `training` will be appropriately set to True automatically, and in other
    contexts, you can set the kwarg explicitly to True when calling the layer.

    (This is in contrast to setting `trainable=False` for a Dropout layer.
    `trainable` does not affect the layer's behavior, as Dropout does
    not have any variables/weights that can be frozen during training.)

    >>> tf.random.set_seed(0)
    >>> layer = tf.keras.layers.Dropout(.2, input_shape=(2,))
    >>> data = np.arange(10).reshape(5, 2).astype(np.float32)
    >>> print(data)
    [[0. 1.]
     [2. 3.]
     [4. 5.]
     [6. 7.]
     [8. 9.]]
    >>> outputs = layer(data, training=True)
    >>> print(outputs)
    tf.Tensor(
    [[ 0.    1.25]
     [ 2.5   3.75]
     [ 5.    6.25]
     [ 7.5   8.75]
     [10.    0.  ]], shape=(5, 2), dtype=float32)

    Args:
      rate: Float between 0 and 1. Fraction of the input units to drop.
      noise_shape: 1D integer tensor representing the shape of the
        binary dropout mask that will be multiplied with the input.
        For instance, if your inputs have shape
        `(batch_size, timesteps, features)` and
        you want the dropout mask to be the same for all timesteps,
        you can use `noise_shape=(batch_size, 1, features)`.
      seed: A Python integer to use as random seed.

    Call arguments:
      inputs: Input tensor (of any rank).
      training: Python boolean indicating whether the layer should behave in
        training mode (adding dropout) or in inference mode (doing nothing).
    """

    def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
        super().__init__(seed=seed, **kwargs)
        if isinstance(rate, (int, float)) and not 0 <= rate <= 1:
            raise ValueError(
                f"Invalid value {rate} received for "
                f"`rate`, expected a value between 0 and 1."
            )
        self.rate = rate
        self.noise_shape = noise_shape
        self.seed = seed
        self.supports_masking = True

    def _get_noise_shape(self, inputs):
        # Subclasses of `Dropout` may implement `_get_noise_shape(self,
        # inputs)`, which will override `self.noise_shape`, and allows for
        # custom noise shapes with dynamically sized inputs.
        if self.noise_shape is None:
            return None

        concrete_inputs_shape = tf.shape(inputs)
        noise_shape = []
        for i, value in enumerate(self.noise_shape):
            noise_shape.append(
                concrete_inputs_shape[i] if value is None else value
            )
        return tf.convert_to_tensor(noise_shape)

    def call(self, inputs, training=None):
        if training is None:
            training = backend.learning_phase()

        def dropped_inputs():
            return self._random_generator.dropout(
                inputs, self.rate, noise_shape=self._get_noise_shape(inputs)
            )

        output = control_flow_util.smart_cond(
            training, dropped_inputs, lambda: tf.identity(inputs)
        )
        return output

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "rate": self.rate,
            "noise_shape": self.noise_shape,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))
