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"""TN-BERT TNTransformerExpandCondense employing Expand-Condense layer instead of Dense."""
# pylint: disable=g-classes-have-attributes
import gin
import tensorflow as tf, tf_keras

from official.modeling import tf_utils
from official.nlp.modeling.layers.tn_expand_condense import TNExpandCondense


@tf_keras.utils.register_keras_serializable(package="Text")
@gin.configurable
class TNTransformerExpandCondense(tf_keras.layers.Layer):
  """Transformer layer using tensor network Expand-Condense layer.

  This layer implements the Transformer from transformer.py, with a single
  tensor network layer replacing the usual intermediate and output Dense
  layers.

  Args:
    num_attention_heads: Number of attention heads.
    intermediate_size: Size of the intermediate layer.
    intermediate_activation: Activation for the intermediate layer.
    dropout_rate: Dropout probability for the post-attention and output dropout.
    attention_dropout_rate: Dropout probability for within the attention layer.
    output_range: the sequence output range, [0, output_range) by slicing the
      target sequence. `None` means the target sequence is not sliced.
    kernel_initializer: Initializer for dense layer kernels.
    bias_initializer: Initializer for dense layer biases.
    kernel_regularizer: Regularizer for dense layer kernels.
    bias_regularizer: Regularizer for dense layer biases.
    activity_regularizer: Regularizer for dense layer activity.
    kernel_constraint: Constraint for dense layer kernels.
    bias_constraint: Constraint for dense layer kernels.
    use_bias: Whether to enable use_bias in attention layer. If set to False,
      use_bias in attention layer is disabled.
    norm_first: Whether to normalize inputs to attention and intermediate dense
      layers. If set False, output of attention and intermediate dense layers is
      normalized.
    norm_epsilon: Epsilon value to initialize normalization layers.
    intermediate_dropout: Dropout probability for intermediate_dropout_layer.
    attention_initializer: Initializer for kernels of attention layers. If set
      `None`, attention layers use kernel_initializer as initializer for kernel.
  """

  def __init__(self,
               num_attention_heads,
               intermediate_size,
               intermediate_activation,
               dropout_rate=0.0,
               attention_dropout_rate=0.0,
               output_range=None,
               kernel_initializer="glorot_uniform",
               bias_initializer="zeros",
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               use_bias=True,
               norm_first=False,
               norm_epsilon=1e-12,
               intermediate_dropout=0.0,
               attention_initializer=None,
               **kwargs):
    super().__init__(**kwargs)

    self._num_heads = num_attention_heads
    self._intermediate_size = intermediate_size
    self._intermediate_activation = intermediate_activation
    self._attention_dropout_rate = attention_dropout_rate
    self._dropout_rate = dropout_rate
    self._output_range = output_range
    self._kernel_initializer = tf_keras.initializers.get(kernel_initializer)
    self._bias_initializer = tf_keras.initializers.get(bias_initializer)
    self._kernel_regularizer = tf_keras.regularizers.get(kernel_regularizer)
    self._bias_regularizer = tf_keras.regularizers.get(bias_regularizer)
    self._activity_regularizer = tf_keras.regularizers.get(activity_regularizer)
    self._kernel_constraint = tf_keras.constraints.get(kernel_constraint)
    self._bias_constraint = tf_keras.constraints.get(bias_constraint)
    self._use_bias = use_bias
    self._norm_first = norm_first
    self._norm_epsilon = norm_epsilon
    self._intermediate_dropout = intermediate_dropout
    if attention_initializer:
      self._attention_initializer = tf_keras.initializers.get(
          attention_initializer)
    else:
      self._attention_initializer = tf_utils.clone_initializer(
          self._kernel_initializer)

  def build(self, input_shape):
    input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
    input_tensor_shape = tf.TensorShape(input_tensor)
    if len(input_tensor_shape.as_list()) != 3:
      raise ValueError(
          "TNTransformerExpandCondense expects a three-dimensional input of "
          "shape [batch, sequence, width].")
    batch_size, sequence_length, hidden_size = input_tensor_shape

    if len(input_shape) == 2:
      mask_tensor_shape = tf.TensorShape(input_shape[1])
      expected_mask_tensor_shape = tf.TensorShape(
          [batch_size, sequence_length, sequence_length])
      if not expected_mask_tensor_shape.is_compatible_with(mask_tensor_shape):
        raise ValueError(
            "When passing a mask tensor to TNTransformerExpandCondense, the "
            "mask tensor must be of shape [batch, "
            "sequence_length, sequence_length] (here %s). Got a "
            "mask tensor of shape %s." %
            (expected_mask_tensor_shape, mask_tensor_shape))
    if hidden_size % self._num_heads != 0:
      raise ValueError(
          "The input size (%d) is not a multiple of the number of attention "
          "heads (%d)" % (hidden_size, self._num_heads))
    self._attention_head_size = int(hidden_size // self._num_heads)
    common_kwargs = dict(
        kernel_regularizer=self._kernel_regularizer,
        bias_regularizer=self._bias_regularizer,
        activity_regularizer=self._activity_regularizer,
        kernel_constraint=self._kernel_constraint,
        bias_constraint=self._bias_constraint)
    self._attention_layer = tf_keras.layers.MultiHeadAttention(
        num_heads=self._num_heads,
        key_dim=self._attention_head_size,
        dropout=self._attention_dropout_rate,
        use_bias=self._use_bias,
        kernel_initializer=self._attention_initializer,
        bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
        name="self_attention",
        **common_kwargs)
    self._attention_dropout = tf_keras.layers.Dropout(rate=self._dropout_rate)
    # Use float32 in layernorm for numeric stability.
    # It is probably safe in mixed_float16, but we haven't validated this yet.
    self._attention_layer_norm = (
        tf_keras.layers.LayerNormalization(
            name="self_attention_layer_norm",
            axis=-1,
            epsilon=self._norm_epsilon,
            dtype=tf.float32))

    # Substitute Dense layers with a single Expand-Condense layer.
    self._output_dense = TNExpandCondense(
        4,
        use_bias=True,
        activation=self._intermediate_activation,
        kernel_initializer=self._kernel_initializer,
        bias_initializer=self._bias_initializer)

    self._output_dropout = tf_keras.layers.Dropout(rate=self._dropout_rate)
    # Use float32 in layernorm for numeric stability.
    self._output_layer_norm = tf_keras.layers.LayerNormalization(
        name="output_layer_norm",
        axis=-1,
        epsilon=self._norm_epsilon,
        dtype=tf.float32)

    super().build(input_shape)

  def get_config(self):
    config = {
        "num_attention_heads":
            self._num_heads,
        "intermediate_size":
            self._intermediate_size,
        "intermediate_activation":
            self._intermediate_activation,
        "dropout_rate":
            self._dropout_rate,
        "attention_dropout_rate":
            self._attention_dropout_rate,
        "output_range":
            self._output_range,
        "kernel_initializer":
            tf_keras.initializers.serialize(self._kernel_initializer),
        "bias_initializer":
            tf_keras.initializers.serialize(self._bias_initializer),
        "kernel_regularizer":
            tf_keras.regularizers.serialize(self._kernel_regularizer),
        "bias_regularizer":
            tf_keras.regularizers.serialize(self._bias_regularizer),
        "activity_regularizer":
            tf_keras.regularizers.serialize(self._activity_regularizer),
        "kernel_constraint":
            tf_keras.constraints.serialize(self._kernel_constraint),
        "bias_constraint":
            tf_keras.constraints.serialize(self._bias_constraint),
        "use_bias":
            self._use_bias,
        "norm_first":
            self._norm_first,
        "norm_epsilon":
            self._norm_epsilon,
        "intermediate_dropout":
            self._intermediate_dropout,
        "attention_initializer":
            tf_keras.initializers.serialize(self._attention_initializer)
    }
    base_config = super().get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs):
    if isinstance(inputs, (list, tuple)) and len(inputs) == 2:
      input_tensor, attention_mask = inputs
    else:
      input_tensor, attention_mask = (inputs, None)

    if self._output_range:
      target_tensor = input_tensor[:, 0:self._output_range, :]
      attention_mask = attention_mask[:, 0:self._output_range, :]
    else:
      if self._norm_first:
        source_tensor = input_tensor
        input_tensor = self._attention_layer_norm(input_tensor)
      target_tensor = input_tensor

    attention_output = self._attention_layer(
        query=target_tensor, value=input_tensor, attention_mask=attention_mask)
    attention_output = self._attention_dropout(attention_output)
    if self._norm_first:
      attention_output = source_tensor + attention_output
    else:
      attention_output = self._attention_layer_norm(target_tensor +
                                                    attention_output)
    if self._norm_first:
      source_attention_output = attention_output
      attention_output = self._output_layer_norm(attention_output)

    layer_output = self._output_dense(attention_output)
    layer_output = self._output_dropout(layer_output)
    # During mixed precision training, attention_output is from layer norm and
    # is always fp32 for now. Cast layer_output to fp32 for the subsequent
    # add.
    layer_output = tf.cast(layer_output, tf.float32)
    if self._norm_first:
      layer_output = source_attention_output + layer_output
    else:
      layer_output = self._output_layer_norm(layer_output + attention_output)

    return layer_output
