# Copyright 2021 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.

"""Funnel Transformer network."""
# pylint: disable=g-classes-have-attributes
from typing import Union, Collection
from absl import logging
import tensorflow as tf

from official.nlp import keras_nlp


def _pool_and_concat(data, unpool_length: int, stride: int,
                     axes: Union[Collection[int], int]):
  """Pools the data along a given axis with stride.

  It also skips first unpool_length elements.

  Args:
    data: Tensor to be pooled.
    unpool_length: Leading elements to be skipped.
    stride: Stride for the given axis.
    axes: Axes to pool the Tensor.

  Returns:
    Pooled and concatenated Tensor.
  """
  # Wraps the axes as a list.
  if isinstance(axes, int):
    axes = [axes]

  for axis in axes:
    # Skips first `unpool_length` tokens.
    unpool_tensor_shape = [slice(None)] * axis + [slice(None, unpool_length)]
    unpool_tensor = data[unpool_tensor_shape]
    # Pools the second half.
    pool_tensor_shape = [slice(None)] * axis + [
        slice(unpool_length, None, stride)
    ]
    pool_tensor = data[pool_tensor_shape]
    data = tf.concat((unpool_tensor, pool_tensor), axis=axis)
  return data


@tf.keras.utils.register_keras_serializable(package='Text')
class FunnelTransformerEncoder(tf.keras.layers.Layer):
  """Funnel Transformer-based encoder network.

  Funnel Transformer Implementation of https://arxiv.org/abs/2006.03236.
  This implementation utilizes the base framework with Bert
  (https://arxiv.org/abs/1810.04805).
  Its output is compatible with `BertEncoder`.

  Args:
    vocab_size: The size of the token vocabulary.
    hidden_size: The size of the transformer hidden layers.
    num_layers: The number of transformer layers.
    num_attention_heads: The number of attention heads for each transformer. The
      hidden size must be divisible by the number of attention heads.
    max_sequence_length: The maximum sequence length that this encoder can
      consume. If None, max_sequence_length uses the value from sequence length.
      This determines the variable shape for positional embeddings.
    type_vocab_size: The number of types that the 'type_ids' input can take.
    inner_dim: The output dimension of the first Dense layer in a two-layer
      feedforward network for each transformer.
    inner_activation: The activation for the first Dense layer in a two-layer
      feedforward network for each transformer.
    output_dropout: Dropout probability for the post-attention and output
      dropout.
    attention_dropout: The dropout rate to use for the attention layers within
      the transformer layers.
    pool_stride: Pooling stride to compress the sequence length.
    unpool_length: Leading n tokens to be skipped from pooling.
    initializer: The initialzer to use for all weights in this encoder.
    output_range: The sequence output range, [0, output_range), by slicing the
      target sequence of the last transformer layer. `None` means the entire
      target sequence will attend to the source sequence, which yields the full
      output.
    embedding_width: The width of the word embeddings. If the embedding width is
      not equal to hidden size, embedding parameters will be factorized into two
      matrices in the shape of ['vocab_size', 'embedding_width'] and
      ['embedding_width', 'hidden_size'] ('embedding_width' is usually much
      smaller than 'hidden_size').
    embedding_layer: An optional Layer instance which will be called to generate
      embeddings for the input word IDs.
    norm_first: Whether to normalize inputs to attention and intermediate dense
      layers. If set False, output of attention and intermediate dense layers is
      normalized.
  """

  def __init__(
      self,
      vocab_size,
      hidden_size=768,
      num_layers=12,
      num_attention_heads=12,
      max_sequence_length=512,
      type_vocab_size=16,
      inner_dim=3072,
      inner_activation=lambda x: tf.keras.activations.gelu(x, approximate=True),
      output_dropout=0.1,
      attention_dropout=0.1,
      pool_stride=2,
      unpool_length=0,
      initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),
      output_range=None,
      embedding_width=None,
      embedding_layer=None,
      norm_first=False,
      **kwargs):
    super().__init__(**kwargs)
    activation = tf.keras.activations.get(inner_activation)
    initializer = tf.keras.initializers.get(initializer)

    if embedding_width is None:
      embedding_width = hidden_size

    if embedding_layer is None:
      self._embedding_layer = keras_nlp.layers.OnDeviceEmbedding(
          vocab_size=vocab_size,
          embedding_width=embedding_width,
          initializer=initializer,
          name='word_embeddings')
    else:
      self._embedding_layer = embedding_layer

    self._position_embedding_layer = keras_nlp.layers.PositionEmbedding(
        initializer=initializer,
        max_length=max_sequence_length,
        name='position_embedding')

    self._type_embedding_layer = keras_nlp.layers.OnDeviceEmbedding(
        vocab_size=type_vocab_size,
        embedding_width=embedding_width,
        initializer=initializer,
        use_one_hot=True,
        name='type_embeddings')

    self._embedding_norm_layer = tf.keras.layers.LayerNormalization(
        name='embeddings/layer_norm', axis=-1, epsilon=1e-12, dtype=tf.float32)

    self._embedding_dropout = tf.keras.layers.Dropout(
        rate=output_dropout, name='embedding_dropout')

    # We project the 'embedding' output to 'hidden_size' if it is not already
    # 'hidden_size'.
    self._embedding_projection = None
    if embedding_width != hidden_size:
      self._embedding_projection = tf.keras.layers.experimental.EinsumDense(
          '...x,xy->...y',
          output_shape=hidden_size,
          bias_axes='y',
          kernel_initializer=initializer,
          name='embedding_projection')

    self._transformer_layers = []
    self._attention_mask_layer = keras_nlp.layers.SelfAttentionMask(
        name='self_attention_mask')
    for i in range(num_layers):
      layer = keras_nlp.layers.TransformerEncoderBlock(
          num_attention_heads=num_attention_heads,
          inner_dim=inner_dim,
          inner_activation=inner_activation,
          output_dropout=output_dropout,
          attention_dropout=attention_dropout,
          norm_first=norm_first,
          output_range=output_range if i == num_layers - 1 else None,
          kernel_initializer=initializer,
          name='transformer/layer_%d' % i)
      self._transformer_layers.append(layer)

    self._pooler_layer = tf.keras.layers.Dense(
        units=hidden_size,
        activation='tanh',
        kernel_initializer=initializer,
        name='pooler_transform')
    self._att_input_pool_layer = tf.keras.layers.MaxPooling1D(
        pool_size=pool_stride,
        strides=pool_stride,
        padding='same',
        name='att_input_pool_layer')
    self._pool_stride = pool_stride
    self._unpool_length = unpool_length

    self._config = {
        'vocab_size': vocab_size,
        'hidden_size': hidden_size,
        'num_layers': num_layers,
        'num_attention_heads': num_attention_heads,
        'max_sequence_length': max_sequence_length,
        'type_vocab_size': type_vocab_size,
        'inner_dim': inner_dim,
        'inner_activation': tf.keras.activations.serialize(activation),
        'output_dropout': output_dropout,
        'attention_dropout': attention_dropout,
        'initializer': tf.keras.initializers.serialize(initializer),
        'output_range': output_range,
        'embedding_width': embedding_width,
        'embedding_layer': embedding_layer,
        'norm_first': norm_first,
        'pool_stride': pool_stride,
        'unpool_length': unpool_length,
    }

  def call(self, inputs):
    # inputs are [word_ids, mask, type_ids]
    if isinstance(inputs, (list, tuple)):
      logging.warning('List inputs to  %s are discouraged.', self.__class__)
      if len(inputs) == 3:
        word_ids, mask, type_ids = inputs
      else:
        raise ValueError('Unexpected inputs to %s with length at %d.' %
                         (self.__class__, len(inputs)))
    elif isinstance(inputs, dict):
      word_ids = inputs.get('input_word_ids')
      mask = inputs.get('input_mask')
      type_ids = inputs.get('input_type_ids')
    else:
      raise ValueError('Unexpected inputs type to %s.' % self.__class__)

    word_embeddings = self._embedding_layer(word_ids)
    # absolute position embeddings
    position_embeddings = self._position_embedding_layer(word_embeddings)
    type_embeddings = self._type_embedding_layer(type_ids)

    embeddings = tf.keras.layers.add(
        [word_embeddings, position_embeddings, type_embeddings])
    embeddings = self._embedding_norm_layer(embeddings)
    embeddings = self._embedding_dropout(embeddings)

    if self._embedding_projection is not None:
      embeddings = self._embedding_projection(embeddings)

    attention_mask = self._attention_mask_layer(embeddings, mask)

    encoder_outputs = []
    x = embeddings
    # TODO(b/195972228): attention_mask can be co-generated with pooling.
    attention_mask = _pool_and_concat(
        attention_mask,
        unpool_length=self._unpool_length,
        stride=self._pool_stride,
        axes=[1])
    for layer in self._transformer_layers:
      # Pools layer for compressing the query length.
      pooled_inputs = self._att_input_pool_layer(x[:, self._unpool_length:, :])
      query_inputs = tf.concat(
          values=(tf.cast(
              x[:, :self._unpool_length, :],
              dtype=pooled_inputs.dtype), pooled_inputs),
          axis=1)
      x = layer([query_inputs, x, attention_mask])
      # Pools the corresponding attention_mask.
      attention_mask = _pool_and_concat(
          attention_mask,
          unpool_length=self._unpool_length,
          stride=self._pool_stride,
          axes=[1, 2])
      encoder_outputs.append(x)

    last_encoder_output = encoder_outputs[-1]
    first_token_tensor = last_encoder_output[:, 0, :]
    pooled_output = self._pooler_layer(first_token_tensor)

    return dict(
        sequence_output=encoder_outputs[-1],
        pooled_output=pooled_output,
        encoder_outputs=encoder_outputs)

  def get_embedding_table(self):
    return self._embedding_layer.embeddings

  def get_embedding_layer(self):
    return self._embedding_layer

  def get_config(self):
    return dict(self._config)

  @property
  def transformer_layers(self):
    """List of Transformer layers in the encoder."""
    return self._transformer_layers

  @property
  def pooler_layer(self):
    """The pooler dense layer after the transformer layers."""
    return self._pooler_layer

  @classmethod
  def from_config(cls, config, custom_objects=None):
    if 'embedding_layer' in config and config['embedding_layer'] is not None:
      warn_string = (
          'You are reloading a model that was saved with a '
          'potentially-shared embedding layer object. If you contine to '
          'train this model, the embedding layer will no longer be shared. '
          'To work around this, load the model outside of the Keras API.')
      print('WARNING: ' + warn_string)
      logging.warn(warn_string)

    return cls(**config)
