# 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 2D 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 Pooling2D(Layer):
    """Pooling layer for arbitrary pooling functions, for 2D data (e.g. images).

    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 2 integers:
        (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 2 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, height, width, channels)` while `channels_first` corresponds to
        inputs with shape `(batch, channels, height, width)`.
      name: A string, the name of the layer.
    """

    def __init__(
        self,
        pool_function,
        pool_size,
        strides,
        padding="valid",
        data_format=None,
        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, 2, "pool_size")
        self.strides = conv_utils.normalize_tuple(
            strides, 2, "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=4)

    def call(self, inputs):
        if self.data_format == "channels_last":
            pool_shape = (1,) + self.pool_size + (1,)
            strides = (1,) + self.strides + (1,)
        else:
            pool_shape = (1, 1) + self.pool_size
            strides = (1, 1) + self.strides
        outputs = self.pool_function(
            inputs,
            ksize=pool_shape,
            strides=strides,
            padding=self.padding.upper(),
            data_format=conv_utils.convert_data_format(self.data_format, 4),
        )
        return outputs

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

    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()))