# 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.
# ==============================================================================
"""Private base class for pooling 3D layers."""


import tensorflow.compat.v2 as tf

from keras import backend
from keras.engine.base_layer import Layer
from keras.engine.input_spec import InputSpec
from keras.utils import conv_utils


class Pooling3D(Layer):
    """Pooling layer for arbitrary pooling functions, for 3D inputs.

    This class only exists for code reuse. It will never be an exposed API.

    Args:
      pool_function: The pooling function to apply, e.g. `tf.nn.max_pool2d`.
      pool_size: An integer or tuple/list of 3 integers:
        (pool_depth, pool_height, pool_width)
        specifying the size of the pooling window.
        Can be a single integer to specify the same value for
        all spatial dimensions.
      strides: An integer or tuple/list of 3 integers,
        specifying the strides of the pooling operation.
        Can be a single integer to specify the same value for
        all spatial dimensions.
      padding: A string. The padding method, either 'valid' or 'same'.
        Case-insensitive.
      data_format: A string, one of `channels_last` (default) or
        `channels_first`.
        The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape
        `(batch, depth, height, width, channels)`
        while `channels_first` corresponds to
        inputs with shape `(batch, channels, depth, height, width)`.
      name: A string, the name of the layer.
    """

    def __init__(
        self,
        pool_function,
        pool_size,
        strides,
        padding="valid",
        data_format="channels_last",
        name=None,
        **kwargs
    ):
        super().__init__(name=name, **kwargs)
        if data_format is None:
            data_format = backend.image_data_format()
        if strides is None:
            strides = pool_size
        self.pool_function = pool_function
        self.pool_size = conv_utils.normalize_tuple(pool_size, 3, "pool_size")
        self.strides = conv_utils.normalize_tuple(
            strides, 3, "strides", allow_zero=True
        )
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.input_spec = InputSpec(ndim=5)

    def call(self, inputs):
        pool_shape = (1,) + self.pool_size + (1,)
        strides = (1,) + self.strides + (1,)

        if self.data_format == "channels_first":
            # TF does not support `channels_first` with 3D pooling operations,
            # so we must handle this case manually.
            # TODO(fchollet): remove this when TF pooling is feature-complete.
            inputs = tf.transpose(inputs, (0, 2, 3, 4, 1))

        outputs = self.pool_function(
            inputs,
            ksize=pool_shape,
            strides=strides,
            padding=self.padding.upper(),
        )

        if self.data_format == "channels_first":
            outputs = tf.transpose(outputs, (0, 4, 1, 2, 3))
        return outputs

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        if self.data_format == "channels_first":
            len_dim1 = input_shape[2]
            len_dim2 = input_shape[3]
            len_dim3 = input_shape[4]
        else:
            len_dim1 = input_shape[1]
            len_dim2 = input_shape[2]
            len_dim3 = input_shape[3]
        len_dim1 = conv_utils.conv_output_length(
            len_dim1, self.pool_size[0], self.padding, self.strides[0]
        )
        len_dim2 = conv_utils.conv_output_length(
            len_dim2, self.pool_size[1], self.padding, self.strides[1]
        )
        len_dim3 = conv_utils.conv_output_length(
            len_dim3, self.pool_size[2], self.padding, self.strides[2]
        )
        if self.data_format == "channels_first":
            return tf.TensorShape(
                [input_shape[0], input_shape[1], len_dim1, len_dim2, len_dim3]
            )
        else:
            return tf.TensorShape(
                [input_shape[0], len_dim1, len_dim2, len_dim3, input_shape[4]]
            )

    def get_config(self):
        config = {
            "pool_size": self.pool_size,
            "padding": self.padding,
            "strides": self.strides,
            "data_format": self.data_format,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))