# 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.
# ==============================================================================
"""Keras upsampling layer for 3D inputs."""


import tensorflow.compat.v2 as tf

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

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


@keras_export("keras.layers.UpSampling3D")
class UpSampling3D(Layer):
    """Upsampling layer for 3D inputs.

    Repeats the 1st, 2nd and 3rd dimensions
    of the data by `size[0]`, `size[1]` and `size[2]` respectively.

    Examples:

    >>> input_shape = (2, 1, 2, 1, 3)
    >>> x = tf.constant(1, shape=input_shape)
    >>> y = tf.keras.layers.UpSampling3D(size=2)(x)
    >>> print(y.shape)
    (2, 2, 4, 2, 3)

    Args:
      size: Int, or tuple of 3 integers.
        The upsampling factors for dim1, dim2 and dim3.
      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_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
        while `channels_first` corresponds to inputs with shape
        `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
        When unspecified, uses
        `image_data_format` value found in your Keras config file at
         `~/.keras/keras.json` (if exists) else 'channels_last'.
        Defaults to 'channels_last'.

    Input shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, dim1, dim2, dim3, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, dim1, dim2, dim3)`

    Output shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3,
          channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, upsampled_dim1, upsampled_dim2,
          upsampled_dim3)`
    """

    def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.size = conv_utils.normalize_tuple(size, 3, "size")
        self.input_spec = InputSpec(ndim=5)
        super().__init__(**kwargs)

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        if self.data_format == "channels_first":
            dim1 = (
                self.size[0] * input_shape[2]
                if input_shape[2] is not None
                else None
            )
            dim2 = (
                self.size[1] * input_shape[3]
                if input_shape[3] is not None
                else None
            )
            dim3 = (
                self.size[2] * input_shape[4]
                if input_shape[4] is not None
                else None
            )
            return tf.TensorShape(
                [input_shape[0], input_shape[1], dim1, dim2, dim3]
            )
        else:
            dim1 = (
                self.size[0] * input_shape[1]
                if input_shape[1] is not None
                else None
            )
            dim2 = (
                self.size[1] * input_shape[2]
                if input_shape[2] is not None
                else None
            )
            dim3 = (
                self.size[2] * input_shape[3]
                if input_shape[3] is not None
                else None
            )
            return tf.TensorShape(
                [input_shape[0], dim1, dim2, dim3, input_shape[4]]
            )

    def call(self, inputs):
        return backend.resize_volumes(
            inputs, self.size[0], self.size[1], self.size[2], self.data_format
        )

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

