# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Keras 3D transposed convolution layer (sometimes called deconvolution)."""


import tensorflow.compat.v2 as tf

from tf_keras.src import activations
from tf_keras.src import constraints
from tf_keras.src import initializers
from tf_keras.src import regularizers
from tf_keras.src.dtensor import utils
from tf_keras.src.engine.base_layer import Layer
from tf_keras.src.engine.input_spec import InputSpec
from tf_keras.src.layers.convolutional.conv3d import Conv3D
from tf_keras.src.utils import conv_utils

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


@keras_export(
    "keras.layers.Conv3DTranspose", "keras.layers.Convolution3DTranspose"
)
class Conv3DTranspose(Conv3D):
    """Transposed convolution layer (sometimes called Deconvolution).

    The need for transposed convolutions generally arises
    from the desire to use a transformation going in the opposite direction
    of a normal convolution, i.e., from something that has the shape of the
    output of some convolution to something that has the shape of its input
    while maintaining a connectivity pattern that is compatible with
    said convolution.

    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers or `None`, does not include the sample axis),
    e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3
    channels if `data_format="channels_last"`.

    Args:
      filters: Integer, the dimensionality of the output space
        (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of 3 integers, specifying the
        depth, height and width of the 3D convolution 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 convolution along the depth, height
          and width.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Specifying any stride value != 1 is incompatible with specifying
        any `dilation_rate` value != 1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros
        evenly to the left/right or up/down of the input such that output has
        the same height/width dimension as the input.
      output_padding: An integer or tuple/list of 3 integers,
        specifying the amount of padding along the depth, height, and
        width.
        Can be a single integer to specify the same value for all
        spatial dimensions.
        The amount of output padding along a given dimension must be
        lower than the stride along that same dimension.
        If set to `None` (default), the output shape is inferred.
      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, depth, height, width, channels)` while `channels_first`
        corresponds to inputs with shape
        `(batch_size, channels, depth, height, width)`.
        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'.
      dilation_rate: an integer or tuple/list of 3 integers, specifying
        the dilation rate to use for dilated convolution.
        Can be a single integer to specify the same value for
        all spatial dimensions.
        Currently, specifying any `dilation_rate` value != 1 is
        incompatible with specifying any stride value != 1.
      activation: Activation function to use.
        If you don't specify anything, no activation is applied
        (see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix
        (see `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector
        (see `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to
        the `kernel` weights matrix
        (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector
        (see `keras.regularizers`).
      activity_regularizer: Regularizer function applied to
        the output of the layer (its "activation")
        (see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix
        (see `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector
        (see `keras.constraints`).

    Input shape:
      5D tensor with shape:
      `(batch_size, channels, depth, rows, cols)` if
      data_format='channels_first'
      or 5D tensor with shape:
      `(batch_size, depth, rows, cols, channels)` if
      data_format='channels_last'.

    Output shape:
      5D tensor with shape:
      `(batch_size, filters, new_depth, new_rows, new_cols)` if
        data_format='channels_first'
      or 5D tensor with shape:
      `(batch_size, new_depth, new_rows, new_cols, filters)` if
        data_format='channels_last'.
      `depth` and `rows` and `cols` values might have changed due to padding.
      If `output_padding` is specified::
      ```
      new_depth = ((depth - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
      output_padding[0])
      new_rows = ((rows - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
      output_padding[1])
      new_cols = ((cols - 1) * strides[2] + kernel_size[2] - 2 * padding[2] +
      output_padding[2])
      ```

    Returns:
      A tensor of rank 5 representing
      `activation(conv3dtranspose(inputs, kernel) + bias)`.

    Raises:
      ValueError: if `padding` is "causal".
      ValueError: when both `strides` > 1 and `dilation_rate` > 1.

    References:
      - [A guide to convolution arithmetic for deep
        learning](https://arxiv.org/abs/1603.07285v1)
      - [Deconvolutional
        Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
    """

    @utils.allow_initializer_layout
    def __init__(
        self,
        filters,
        kernel_size,
        strides=(1, 1, 1),
        padding="valid",
        output_padding=None,
        data_format=None,
        dilation_rate=(1, 1, 1),
        activation=None,
        use_bias=True,
        kernel_initializer="glorot_uniform",
        bias_initializer="zeros",
        kernel_regularizer=None,
        bias_regularizer=None,
        activity_regularizer=None,
        kernel_constraint=None,
        bias_constraint=None,
        **kwargs,
    ):
        super().__init__(
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilation_rate=dilation_rate,
            activation=activations.get(activation),
            use_bias=use_bias,
            kernel_initializer=initializers.get(kernel_initializer),
            bias_initializer=initializers.get(bias_initializer),
            kernel_regularizer=regularizers.get(kernel_regularizer),
            bias_regularizer=regularizers.get(bias_regularizer),
            activity_regularizer=regularizers.get(activity_regularizer),
            kernel_constraint=constraints.get(kernel_constraint),
            bias_constraint=constraints.get(bias_constraint),
            **kwargs,
        )

        self.output_padding = output_padding
        if self.output_padding is not None:
            self.output_padding = conv_utils.normalize_tuple(
                self.output_padding, 3, "output_padding", allow_zero=True
            )
            for stride, out_pad in zip(self.strides, self.output_padding):
                if out_pad >= stride:
                    raise ValueError(
                        "Strides must be greater than output padding. "
                        f"Received strides={self.strides}, "
                        f"output_padding={self.output_padding}."
                    )

    def build(self, input_shape):
        input_shape = tf.TensorShape(input_shape)
        if len(input_shape) != 5:
            raise ValueError(
                "Inputs should have rank 5. "
                f"Received input_shape={input_shape}."
            )
        channel_axis = self._get_channel_axis()
        if input_shape.dims[channel_axis].value is None:
            raise ValueError(
                "The channel dimension of the inputs "
                "to `Conv3DTranspose` should be defined. "
                f"The input_shape received is {input_shape}, "
                f"where axis {channel_axis} (0-based) "
                "is the channel dimension, which found to be `None`."
            )
        input_dim = int(input_shape[channel_axis])
        kernel_shape = self.kernel_size + (self.filters, input_dim)
        self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})

        self.kernel = self.add_weight(
            "kernel",
            shape=kernel_shape,
            initializer=self.kernel_initializer,
            regularizer=self.kernel_regularizer,
            constraint=self.kernel_constraint,
            trainable=True,
            dtype=self.dtype,
        )
        if self.use_bias:
            self.bias = self.add_weight(
                "bias",
                shape=(self.filters,),
                initializer=self.bias_initializer,
                regularizer=self.bias_regularizer,
                constraint=self.bias_constraint,
                trainable=True,
                dtype=self.dtype,
            )
        else:
            self.bias = None
        # Call Layer.build() to skip Conv.build() which we override here.
        Layer.build(self, input_shape)

    def call(self, inputs):
        inputs_shape = tf.shape(inputs)
        batch_size = inputs_shape[0]
        if self.data_format == "channels_first":
            d_axis, h_axis, w_axis = 2, 3, 4
        else:
            d_axis, h_axis, w_axis = 1, 2, 3

        depth = inputs_shape[d_axis]
        height = inputs_shape[h_axis]
        width = inputs_shape[w_axis]

        kernel_d, kernel_h, kernel_w = self.kernel_size
        stride_d, stride_h, stride_w = self.strides

        if self.output_padding is None:
            out_pad_d = out_pad_h = out_pad_w = None
        else:
            out_pad_d, out_pad_h, out_pad_w = self.output_padding

        # Infer the dynamic output shape:
        out_depth = conv_utils.deconv_output_length(
            depth,
            kernel_d,
            padding=self.padding,
            output_padding=out_pad_d,
            stride=stride_d,
        )
        out_height = conv_utils.deconv_output_length(
            height,
            kernel_h,
            padding=self.padding,
            output_padding=out_pad_h,
            stride=stride_h,
        )
        out_width = conv_utils.deconv_output_length(
            width,
            kernel_w,
            padding=self.padding,
            output_padding=out_pad_w,
            stride=stride_w,
        )
        if self.data_format == "channels_first":
            output_shape = (
                batch_size,
                self.filters,
                out_depth,
                out_height,
                out_width,
            )
            strides = (1, 1, stride_d, stride_h, stride_w)
        else:
            output_shape = (
                batch_size,
                out_depth,
                out_height,
                out_width,
                self.filters,
            )
            strides = (1, stride_d, stride_h, stride_w, 1)

        output_shape_tensor = tf.stack(output_shape)
        outputs = tf.nn.conv3d_transpose(
            inputs,
            self.kernel,
            output_shape_tensor,
            strides,
            data_format=conv_utils.convert_data_format(
                self.data_format, ndim=5
            ),
            padding=self.padding.upper(),
        )

        if not tf.executing_eagerly() and inputs.shape.rank:
            # Infer the static output shape:
            out_shape = self.compute_output_shape(inputs.shape)
            outputs.set_shape(out_shape)

        if self.use_bias:
            outputs = tf.nn.bias_add(
                outputs,
                self.bias,
                data_format=conv_utils.convert_data_format(
                    self.data_format, ndim=4
                ),
            )

        if self.activation is not None:
            return self.activation(outputs)
        return outputs

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        output_shape = list(input_shape)
        if self.data_format == "channels_first":
            c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
        else:
            c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3

        kernel_d, kernel_h, kernel_w = self.kernel_size
        stride_d, stride_h, stride_w = self.strides

        if self.output_padding is None:
            out_pad_d = out_pad_h = out_pad_w = None
        else:
            out_pad_d, out_pad_h, out_pad_w = self.output_padding

        output_shape[c_axis] = self.filters
        output_shape[d_axis] = conv_utils.deconv_output_length(
            output_shape[d_axis],
            kernel_d,
            padding=self.padding,
            output_padding=out_pad_d,
            stride=stride_d,
        )
        output_shape[h_axis] = conv_utils.deconv_output_length(
            output_shape[h_axis],
            kernel_h,
            padding=self.padding,
            output_padding=out_pad_h,
            stride=stride_h,
        )
        output_shape[w_axis] = conv_utils.deconv_output_length(
            output_shape[w_axis],
            kernel_w,
            padding=self.padding,
            output_padding=out_pad_w,
            stride=stride_w,
        )
        return tf.TensorShape(output_shape)

    def get_config(self):
        config = super().get_config()
        config.pop("dilation_rate")
        config["output_padding"] = self.output_padding
        return config


# Alias

Convolution3DTranspose = Conv3DTranspose