# Copyright 2018 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.
# ==============================================================================
"""Fast GRU layer backed by cuDNN."""


import collections

import tensorflow.compat.v2 as tf

from keras.src import constraints
from keras.src import initializers
from keras.src import regularizers
from keras.src.layers.rnn import gru_lstm_utils
from keras.src.layers.rnn.base_cudnn_rnn import _CuDNNRNN

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


@keras_export(v1=["keras.layers.CuDNNGRU"])
class CuDNNGRU(_CuDNNRNN):
    """Fast GRU implementation backed by cuDNN.

    More information about cuDNN can be found on the [NVIDIA
    developer website](https://developer.nvidia.com/cudnn).
    Can only be run on GPU.

    Args:
        units: Positive integer, dimensionality of the output space.
        kernel_initializer: Initializer for the `kernel` weights matrix, used
          for the linear transformation of the inputs.
        recurrent_initializer: Initializer for the `recurrent_kernel` weights
          matrix, used for the linear transformation of the recurrent state.
        bias_initializer: Initializer for the bias vector.
        kernel_regularizer: Regularizer function applied to the `kernel` weights
          matrix.
        recurrent_regularizer: Regularizer function applied to the
          `recurrent_kernel` weights matrix.
        bias_regularizer: Regularizer function applied to the bias vector.
        activity_regularizer: Regularizer function applied to the output of the
          layer (its "activation").
        kernel_constraint: Constraint function applied to the `kernel` weights
          matrix.
        recurrent_constraint: Constraint function applied to the
          `recurrent_kernel` weights matrix.
        bias_constraint: Constraint function applied to the bias vector.
        return_sequences: Boolean. Whether to return the last output in the
          output sequence, or the full sequence.
        return_state: Boolean. Whether to return the last state in addition to
          the output.
        go_backwards: Boolean (default False). If True, process the input
          sequence backwards and return the reversed sequence.
        stateful: Boolean (default False). If True, the last state for each
          sample at index i in a batch will be used as initial state for the
          sample of index i in the following batch.
    """

    def __init__(
        self,
        units,
        kernel_initializer="glorot_uniform",
        recurrent_initializer="orthogonal",
        bias_initializer="zeros",
        kernel_regularizer=None,
        recurrent_regularizer=None,
        bias_regularizer=None,
        activity_regularizer=None,
        kernel_constraint=None,
        recurrent_constraint=None,
        bias_constraint=None,
        return_sequences=False,
        return_state=False,
        go_backwards=False,
        stateful=False,
        **kwargs
    ):
        self.units = units
        cell_spec = collections.namedtuple("cell", "state_size")
        self._cell = cell_spec(state_size=self.units)
        super().__init__(
            return_sequences=return_sequences,
            return_state=return_state,
            go_backwards=go_backwards,
            stateful=stateful,
            **kwargs
        )

        self.kernel_initializer = initializers.get(kernel_initializer)
        self.recurrent_initializer = initializers.get(recurrent_initializer)
        self.bias_initializer = initializers.get(bias_initializer)

        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)

        self.kernel_constraint = constraints.get(kernel_constraint)
        self.recurrent_constraint = constraints.get(recurrent_constraint)
        self.bias_constraint = constraints.get(bias_constraint)

    @property
    def cell(self):
        return self._cell

    def build(self, input_shape):
        super().build(input_shape)
        if isinstance(input_shape, list):
            input_shape = input_shape[0]
        input_dim = int(input_shape[-1])

        self.kernel = self.add_weight(
            shape=(input_dim, self.units * 3),
            name="kernel",
            initializer=self.kernel_initializer,
            regularizer=self.kernel_regularizer,
            constraint=self.kernel_constraint,
        )

        self.recurrent_kernel = self.add_weight(
            shape=(self.units, self.units * 3),
            name="recurrent_kernel",
            initializer=self.recurrent_initializer,
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint,
        )

        self.bias = self.add_weight(
            shape=(self.units * 6,),
            name="bias",
            initializer=self.bias_initializer,
            regularizer=self.bias_regularizer,
            constraint=self.bias_constraint,
        )

        self.built = True

    def _process_batch(self, inputs, initial_state):
        if not self.time_major:
            inputs = tf.transpose(inputs, perm=(1, 0, 2))
        input_h = initial_state[0]
        input_h = tf.expand_dims(input_h, axis=0)

        params = gru_lstm_utils.canonical_to_params(
            weights=[
                self.kernel[:, self.units : self.units * 2],
                self.kernel[:, : self.units],
                self.kernel[:, self.units * 2 :],
                self.recurrent_kernel[:, self.units : self.units * 2],
                self.recurrent_kernel[:, : self.units],
                self.recurrent_kernel[:, self.units * 2 :],
            ],
            biases=[
                self.bias[self.units : self.units * 2],
                self.bias[: self.units],
                self.bias[self.units * 2 : self.units * 3],
                self.bias[self.units * 4 : self.units * 5],
                self.bias[self.units * 3 : self.units * 4],
                self.bias[self.units * 5 :],
            ],
            shape=self._vector_shape,
        )

        args = {
            "input": inputs,
            "input_h": input_h,
            "input_c": 0,
            "params": params,
            "is_training": True,
            "rnn_mode": "gru",
        }

        outputs, h, _, _, _ = tf.raw_ops.CudnnRNNV2(**args)

        if self.stateful or self.return_state:
            h = h[0]
        if self.return_sequences:
            if self.time_major:
                output = outputs
            else:
                output = tf.transpose(outputs, perm=(1, 0, 2))
        else:
            output = outputs[-1]
        return output, [h]

    def get_config(self):
        config = {
            "units": self.units,
            "kernel_initializer": initializers.serialize(
                self.kernel_initializer
            ),
            "recurrent_initializer": initializers.serialize(
                self.recurrent_initializer
            ),
            "bias_initializer": initializers.serialize(self.bias_initializer),
            "kernel_regularizer": regularizers.serialize(
                self.kernel_regularizer
            ),
            "recurrent_regularizer": regularizers.serialize(
                self.recurrent_regularizer
            ),
            "bias_regularizer": regularizers.serialize(self.bias_regularizer),
            "activity_regularizer": regularizers.serialize(
                self.activity_regularizer
            ),
            "kernel_constraint": constraints.serialize(self.kernel_constraint),
            "recurrent_constraint": constraints.serialize(
                self.recurrent_constraint
            ),
            "bias_constraint": constraints.serialize(self.bias_constraint),
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))

