text: # coding=utf-8
  # Copyright 2021, Google Inc. and The HuggingFace Inc. team. 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.
  """ TF 2.0 Pegasus model."""
  
  
  import random
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import tensorflow as tf
  
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import (
      TFBaseModelOutput,
      TFBaseModelOutputWithPastAndCrossAttentions,
      TFSeq2SeqLMOutput,
      TFSeq2SeqModelOutput,
  )
  
  # Public API
  from ...modeling_tf_utils import (
      DUMMY_INPUTS,
      TFCausalLanguageModelingLoss,
      TFPreTrainedModel,
      TFSharedEmbeddings,
      TFWrappedEmbeddings,
      keras_serializable,
      unpack_inputs,
  )
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import (
      add_code_sample_docstrings,
      add_end_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      logging,
      replace_return_docstrings,
  )
  from .configuration_pegasus import PegasusConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "google/pegasus-large"
  _CONFIG_FOR_DOC = "PegasusConfig"
  _TOKENIZER_FOR_DOC = "PegasusTokenizer"
  
  
  LARGE_NEGATIVE = -1e8
  
  
  # Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right
  def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
      pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
      decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
      start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id)
      shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
      # replace possible -100 values in labels by `pad_token_id`
      shifted_input_ids = tf.where(
          shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids
      )
  
      if tf.executing_eagerly():
          # "Verify that `labels` has only positive values and -100"
          assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
  
          # Make sure the assertion op is called by wrapping the result in an identity no-op
          with tf.control_dependencies([assert_gte0]):
              shifted_input_ids = tf.identity(shifted_input_ids)
  
      return shifted_input_ids
  
  
  # Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
  def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
      """
      Make causal mask used for bi-directional self-attention.
      """
      bsz = input_ids_shape[0]
      tgt_len = input_ids_shape[1]
      mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
      mask_cond = tf.range(shape_list(mask)[-1])
  
      mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
  
      if past_key_values_length > 0:
          mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
  
      return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
  
  
  # Copied from transformers.models.bart.modeling_tf_bart._expand_mask
  def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
      """
      Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
      """
      src_len = shape_list(mask)[1]
      tgt_len = tgt_len if tgt_len is not None else src_len
      one_cst = tf.constant(1.0)
      mask = tf.cast(mask, dtype=one_cst.dtype)
      expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
  
      return (one_cst - expanded_mask) * LARGE_NEGATIVE
  
  
  # Copied from transformers.models.marian.modeling_tf_marian.TFMarianSinusoidalPositionalEmbedding with Marian->Pegasus
  class TFPegasusSinusoidalPositionalEmbedding(tf.keras.layers.Layer):
      """This module produces sinusoidal positional embeddings of any length."""
  
      def __init__(self, num_positions: int, embedding_dim: int, **kwargs):
          super().__init__(**kwargs)
  
          if embedding_dim % 2 != 0:
              raise NotImplementedError(f"odd embedding_dim {embedding_dim} not supported")
  
          self.embedding_dim = embedding_dim
          self.num_positions = num_positions
  
      def build(self, input_shape: tf.TensorShape):
          """
          Build shared token embedding layer Shared weights logic adapted from
          https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
          """
  
          weight = self._init_weight(self.num_positions, self.embedding_dim)
  
          self.weight = self.add_weight(
              name="embeddings",
              shape=[self.num_positions, self.embedding_dim],
          )
          weight = tf.cast(weight, dtype=self.weight.dtype)
  
          self.weight.assign(weight)
  
          super().build(input_shape)
  
      @staticmethod
      def _init_weight(n_pos: int, dim: int):
          """
          Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
          the 2nd half of the vector. [dim // 2:]
          """
          position_enc = np.array(
              [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
          )
          table = np.zeros_like(position_enc)
          # index 0 is all zero
          table[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
          table[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
          # convert to tensor
          table = tf.convert_to_tensor(table)
          tf.stop_gradient(table)
          return table
  
      def call(
          self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: Optional[tf.Tensor] = None
      ):
          """Input is expected to be of size [bsz x seqlen]."""
          if position_ids is None:
              seq_len = input_shape[1]
              position_ids = tf.range(past_key_values_length, seq_len + past_key_values_length, delta=1, name="range")
          return tf.gather(self.weight, position_ids)
  
  
  # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Pegasus
  class TFPegasusAttention(tf.keras.layers.Layer):
      """Multi-headed attention from "Attention Is All You Need"""
  
      def __init__(
          self,
          embed_dim: int,
          num_heads: int,
          dropout: float = 0.0,
          is_decoder: bool = False,
          bias: bool = True,
          **kwargs,
      ):
          super().__init__(**kwargs)
          self.embed_dim = embed_dim
  
          self.num_heads = num_heads
          self.dropout = tf.keras.layers.Dropout(dropout)
          self.head_dim = embed_dim // num_heads
          if (self.head_dim * num_heads) != self.embed_dim:
              raise ValueError(
                  f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                  f" and `num_heads`: {num_heads})."
              )
          self.scaling = self.head_dim**-0.5
          self.is_decoder = is_decoder
  
          self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
          self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
          self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
          self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
  
      def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
          return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
  
      def call(
          self,
          hidden_states: tf.Tensor,
          key_value_states: Optional[tf.Tensor] = None,
          past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None,
          attention_mask: Optional[tf.Tensor] = None,
          layer_head_mask: Optional[tf.Tensor] = None,
          training: Optional[bool] = False,
      ) -> Tuple[tf.Tensor, Optional[tf.Tensor]]:
          """Input shape: Batch x Time x Channel"""
  
          # if key_value_states are provided this layer is used as a cross-attention layer
          # for the decoder
          is_cross_attention = key_value_states is not None
          bsz, tgt_len, embed_dim = shape_list(hidden_states)
  
          # get query proj
          query_states = self.q_proj(hidden_states) * self.scaling
          # get key, value proj
          if is_cross_attention and past_key_value is not None:
              # reuse k,v, cross_attentions
              key_states = past_key_value[0]
              value_states = past_key_value[1]
          elif is_cross_attention:
              # cross_attentions
              key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
              value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
          elif past_key_value is not None:
              # reuse k, v, self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
              key_states = tf.concat([past_key_value[0], key_states], axis=2)
              value_states = tf.concat([past_key_value[1], value_states], axis=2)
          else:
              # self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
  
          if self.is_decoder:
              # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
              # Further calls to cross_attention layer can then reuse all cross-attention
              # key/value_states (first "if" case)
              # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
              # all previous decoder key/value_states. Further calls to uni-directional self-attention
              # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
              # if encoder bi-directional self-attention `past_key_value` is always `None`
              past_key_value = (key_states, value_states)
  
          proj_shape = (bsz * self.num_heads, -1, self.head_dim)
          query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
          key_states = tf.reshape(key_states, proj_shape)
          value_states = tf.reshape(value_states, proj_shape)
  
          src_len = shape_list(key_states)[1]
          attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
  
          # The tf.debugging asserts are not compliant with XLA then they
          # have to be disabled in other modes than eager.
          if tf.executing_eagerly():
              tf.debugging.assert_equal(
                  shape_list(attn_weights),
                  [bsz * self.num_heads, tgt_len, src_len],
                  message=(
                      f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
                      f" {shape_list(attn_weights)}"
                  ),
              )
  
          if attention_mask is not None:
              # The tf.debugging asserts are not compliant with XLA then they
              # have to be disabled in other modes than eager.
              if tf.executing_eagerly():
                  tf.debugging.assert_equal(
                      shape_list(attention_mask),
                      [bsz, 1, tgt_len, src_len],
                      message=(
                          f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
                          f" {shape_list(attention_mask)}"
                      ),
                  )
  
              attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
              attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
              attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
  
          attn_weights = stable_softmax(attn_weights, axis=-1)
  
          if layer_head_mask is not None:
              # The tf.debugging asserts are not compliant with XLA then they
              # have to be disabled in other modes than eager.
              if tf.executing_eagerly():
                  tf.debugging.assert_equal(
                      shape_list(layer_head_mask),
                      [self.num_heads],
                      message=(
                          f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
                          f" {shape_list(layer_head_mask)}"
                      ),
                  )
  
              attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
                  attn_weights, (bsz, self.num_heads, tgt_len, src_len)
              )
              attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
  
          attn_probs = self.dropout(attn_weights, training=training)
          attn_output = tf.matmul(attn_probs, value_states)
  
          # The tf.debugging asserts are not compliant with XLA then they
          # have to be disabled in other modes than eager.
          if tf.executing_eagerly():
              tf.debugging.assert_equal(
                  shape_list(attn_output),
                  [bsz * self.num_heads, tgt_len, self.head_dim],
                  message=(
                      f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
                      f" {shape_list(attn_output)}"
                  ),
              )
  
          attn_output = tf.transpose(
              tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
          )
          attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
  
          attn_output = self.out_proj(attn_output)
          attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
  
          return attn_output, attn_weights, past_key_value
  
  
  # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Pegasus
  class TFPegasusEncoderLayer(tf.keras.layers.Layer):
      def __init__(self, config: PegasusConfig, **kwargs):
          super().__init__(**kwargs)
          self.embed_dim = config.d_model
          self.self_attn = TFPegasusAttention(
              self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
          )
          self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.activation_fn = get_tf_activation(config.activation_function)
          self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
          self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
          self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
          self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: tf.Tensor,
          layer_head_mask: tf.Tensor,
          training: Optional[bool] = False,
      ):
          """
          Args:
              hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
              attention_mask (`tf.Tensor`): attention mask of size
                  *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
              layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
                  *(encoder_attention_heads,)*
          """
          residual = hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
          hidden_states, self_attn_weights, _ = self.self_attn(
              hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
          )
  
          # The tf.debugging asserts are not compliant with XLA then they
          # have to be disabled in other modes than eager.
          if tf.executing_eagerly():
              tf.debugging.assert_equal(
                  shape_list(hidden_states),
                  shape_list(residual),
                  message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
              )
  
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
  
          residual = hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = self.activation_dropout(hidden_states, training=training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
  
          return hidden_states, self_attn_weights
  
  
  # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Pegasus
  class TFPegasusDecoderLayer(tf.keras.layers.Layer):
      def __init__(self, config: PegasusConfig, **kwargs):
          super().__init__(**kwargs)
          self.embed_dim = config.d_model
          self.self_attn = TFPegasusAttention(
              embed_dim=self.embed_dim,
              num_heads=config.decoder_attention_heads,
              dropout=config.attention_dropout,
              name="self_attn",
              is_decoder=True,
          )
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.activation_fn = get_tf_activation(config.activation_function)
          self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
  
          self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
          self.encoder_attn = TFPegasusAttention(
              self.embed_dim,
              config.decoder_attention_heads,
              dropout=config.attention_dropout,
              name="encoder_attn",
              is_decoder=True,
          )
          self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
          self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
          self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
          self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          encoder_hidden_states: Optional[tf.Tensor] = None,
          encoder_attention_mask: Optional[tf.Tensor] = None,
          layer_head_mask: Optional[tf.Tensor] = None,
          cross_attn_layer_head_mask: Optional[tf.Tensor] = None,
          past_key_value: Optional[Tuple[tf.Tensor]] = None,
          training: Optional[bool] = False,
      ) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
          """
          Args:
              hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
              attention_mask (`tf.Tensor`): attention mask of size
                  *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
              encoder_hidden_states (`tf.Tensor`):
                  cross attention input to the layer of shape *(seq_len, batch, embed_dim)*
              encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
                  *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
              layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
                  *(decoder_attention_heads,)*
              cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module.
                  *(decoder_attention_heads,)*
              past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
          """
          residual = hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Self Attention
          # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
          self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
          # add present self-attn cache to positions 1,2 of present_key_value tuple
          hidden_states, self_attn_weights, present_key_value = self.self_attn(
              hidden_states=hidden_states,
              past_key_value=self_attn_past_key_value,
              attention_mask=attention_mask,
              layer_head_mask=layer_head_mask,
          )
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
  
          # Cross-Attention Block
          cross_attn_present_key_value = None
          cross_attn_weights = None
          if encoder_hidden_states is not None:
              residual = hidden_states
              hidden_states = self.encoder_attn_layer_norm(hidden_states)
  
              # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
              cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
              hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                  hidden_states=hidden_states,
                  key_value_states=encoder_hidden_states,
                  attention_mask=encoder_attention_mask,
                  layer_head_mask=cross_attn_layer_head_mask,
                  past_key_value=cross_attn_past_key_value,
              )
              hidden_states = self.dropout(hidden_states, training=training)
              hidden_states = residual + hidden_states
  
              # add cross-attn to positions 3,4 of present_key_value tuple
              present_key_value = present_key_value + cross_attn_present_key_value
  
          # Fully Connected
          residual = hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = self.activation_dropout(hidden_states, training=training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = self.dropout(hidden_states, training=training)
          hidden_states = residual + hidden_states
  
          return (
              hidden_states,
              self_attn_weights,
              cross_attn_weights,
              present_key_value,
          )
  
  
  class TFPegasusPreTrainedModel(TFPreTrainedModel):
      config_class = PegasusConfig
      base_model_prefix = "model"
  
      @property
      def dummy_inputs(self):
          pad_token = 1
          input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
          decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
          dummy_inputs = {
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": tf.math.not_equal(input_ids, pad_token),
              "input_ids": input_ids,
          }
          return dummy_inputs
  
      @tf.function(
          input_signature=[
              {
                  "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
                  "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
                  "decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
                  "decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
              }
          ]
      )
      # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving
      def serving(self, inputs):
          output = self.call(inputs)
  
          return self.serving_output(output)
  
  
  PEGASUS_START_DOCSTRING = r"""
      This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
      library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
      etc.)
  
      This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
      as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
      behavior.
  
      <Tip>
  
      TF 2.0 models accepts two formats as inputs:
  
      - having all inputs as keyword arguments (like PyTorch models), or
      - having all inputs as a list, tuple or dict in the first positional arguments.
  
      This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
      tensors in the first argument of the model call function: `model(inputs)`.
  
      If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
      first positional argument :
  
      - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
      - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
      `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
      - a dictionary with one or several input Tensors associated to the input names given in the docstring:
      `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
  
      </Tip>
  
      Args:
          config ([`PegasusConfig`]): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
  """
  
  PEGASUS_GENERATION_EXAMPLE = r"""
      Summarization example:
  
      ```python
      >>> from transformers import PegasusTokenizer, TFPegasusForConditionalGeneration
  
      >>> model = TFPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
      >>> tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
  
      >>> ARTICLE_TO_SUMMARIZE = (
      ...     "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
      ...     "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
      ...     "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
      ... )
      >>> inputs = tokenizer(ARTICLE_TO_SUMMARIZE, max_length=1024, return_tensors="tf")
  
      >>> # Generate Summary
      >>> summary_ids = model.generate(input_ids)
      >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
      ```
  """
  
  PEGASUS_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (`tf.Tensor` of shape `({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
              Indices of decoder input sequence tokens in the vocabulary.
  
              Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are decoder input IDs?](../glossary#decoder-input-ids)
  
              Pegasus uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
              `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
              `past_key_values`).
          decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
              will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
          decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
              Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
              range `[0, config.max_position_embeddings - 1]`.
          head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
              Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
              Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
              Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          encoder_outputs (`tf.FloatTensor`, *optional*):
              hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
              of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
          past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
              contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
              If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
              don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
              `decoder_input_ids` of shape `(batch_size, sequence_length)`.
          use_cache (`bool`, *optional*, defaults to `True`):
              If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
              `past_key_values`). Set to `False` during training, `True` during generation output_attentions (`bool`,
              *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions`
              under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the
              value in the config will be used instead.
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
              config will be used instead.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
              used instead.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
              eager mode, in graph mode the value will always be set to True.
          training (`bool`, *optional*, defaults to `False`):
              Whether or not to use the model in training mode (some modules like dropout modules have different
              behaviors between training and evaluation).
  """
  
  
  @keras_serializable
  class TFPegasusEncoder(tf.keras.layers.Layer):
      config_class = PegasusConfig
      """
      Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
      [`TFPegasusEncoderLayer`].
  
      Args:
          config: PegasusConfig
      """
  
      def __init__(self, config: PegasusConfig, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs):
          super().__init__(**kwargs)
          self.config = config
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.layerdrop = config.encoder_layerdrop
          self.padding_idx = config.pad_token_id
          self.max_source_positions = config.max_position_embeddings
          self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
  
          self.embed_tokens = embed_tokens
          self.embed_positions = TFPegasusSinusoidalPositionalEmbedding(
              config.max_position_embeddings,
              config.d_model,
              name="embed_positions",
          )
          self.layers = [TFPegasusEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
          self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
  
      def get_embed_tokens(self):
          return self.embed_tokens
  
      def set_embed_tokens(self, embed_tokens):
          self.embed_tokens = embed_tokens
  
      @unpack_inputs
      def call(
          self,
          input_ids=None,
          inputs_embeds=None,
          attention_mask=None,
          head_mask=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
      ):
          """
          Args:
              input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
                  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                  provide it.
  
                  Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                  [`PreTrainedTokenizer.__call__`] for details.
  
                  [What are input IDs?](../glossary#input-ids)
              attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  [What are attention masks?](../glossary#attention-mask)
              head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
                  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
  
                  - 1 indicates the head is **not masked**,
                  - 0 indicates the head is **masked**.
  
              inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                  This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                  than the model's internal embedding lookup matrix.
              output_attentions (`bool`, *optional*):
                  Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                  returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
                  in the config will be used instead.
              output_hidden_states (`bool`, *optional*):
                  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                  for more detail. This argument can be used only in eager mode, in graph mode the value in the config
                  will be used instead.
              return_dict (`bool`, *optional*):
                  Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
                  in eager mode, in graph mode the value will always be set to True.
              training (`bool`, *optional*, defaults to `False`):
                  Whether or not to use the model in training mode (some modules like dropout modules have different
                  behaviors between training and evaluation).
          """
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = shape_list(input_ids)
          elif inputs_embeds is not None:
              input_shape = shape_list(inputs_embeds)[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if inputs_embeds is None:
              inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  
          embed_pos = self.embed_positions(input_shape)
          hidden_states = inputs_embeds + embed_pos
          hidden_states = self.dropout(hidden_states, training=training)
  
          # check attention mask and invert
          if attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              attention_mask = _expand_mask(attention_mask)
          else:
              attention_mask = None
  
          encoder_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
  
          # check if head_mask has a correct number of layers specified if desired
          # The tf.debugging asserts are not compliant with XLA then they
          # have to be disabled in other modes than eager.
          if head_mask is not None and tf.executing_eagerly():
              tf.debugging.assert_equal(
                  shape_list(head_mask)[0],
                  len(self.layers),
                  message=(
                      f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
                      f" {shape_list(head_mask)[0]}."
                  ),
              )
  
          # encoder layers
          for idx, encoder_layer in enumerate(self.layers):
  
              if output_hidden_states:
                  encoder_states = encoder_states + (hidden_states,)
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              dropout_probability = random.uniform(0, 1)
              if training and (dropout_probability < self.layerdrop):  # skip the layer
                  continue
  
              hidden_states, attn = encoder_layer(
                  hidden_states,
                  attention_mask,
                  head_mask[idx] if head_mask is not None else None,
              )
  
              if output_attentions:
                  all_attentions += (attn,)
  
          hidden_states = self.layer_norm(hidden_states)
  
          if output_hidden_states:
              encoder_states = encoder_states + (hidden_states,)
  
          if not return_dict:
              return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
          return TFBaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
          )
  
  
  @keras_serializable
  class TFPegasusDecoder(tf.keras.layers.Layer):
      config_class = PegasusConfig
      """
      Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFPegasusDecoderLayer`]
  
      Args:
          config: PegasusConfig
          embed_tokens: output embedding
      """
  
      def __init__(self, config: PegasusConfig, embed_tokens: Optional[TFSharedEmbeddings] = None, **kwargs):
          super().__init__(**kwargs)
          self.config = config
          self.padding_idx = config.pad_token_id
          self.embed_tokens = embed_tokens
          self.layerdrop = config.decoder_layerdrop
          self.embed_positions = TFPegasusSinusoidalPositionalEmbedding(
              config.max_position_embeddings,
              config.d_model,
              name="embed_positions",
          )
          self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
          self.layers = [TFPegasusDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
          self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
  
          self.dropout = tf.keras.layers.Dropout(config.dropout)
  
      def get_embed_tokens(self):
          return self.embed_tokens
  
      def set_embed_tokens(self, embed_tokens):
          self.embed_tokens = embed_tokens
  
      @unpack_inputs
      def call(
          self,
          input_ids=None,
          inputs_embeds=None,
          attention_mask=None,
          position_ids=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          head_mask=None,
          cross_attn_head_mask=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
      ):
          r"""
          Args:
              input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
                  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                  provide it.
  
                  Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                  [`PreTrainedTokenizer.__call__`] for details.
  
                  [What are input IDs?](../glossary#input-ids)
              attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  [What are attention masks?](../glossary#attention-mask)
              position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                  Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
                  range `[0, config.max_position_embeddings - 1]`.
              encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
                  Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                  of the decoder.
              encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
                  Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
                  selected in `[0, 1]`:
  
                  - 1 for tokens that are **not masked**,
                  - 0 for tokens that are **masked**.
  
                  [What are attention masks?](../glossary#attention-mask)
              head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
  
                  - 1 indicates the head is **not masked**,
                  - 0 indicates the head is **masked**.
  
              cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                  Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  
                  - 1 indicates the head is **not masked**,
                  - 0 indicates the head is **masked**.
  
              past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                  Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
                  decoding.
  
                  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                  that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                  all ``decoder_input_ids``` of shape `(batch_size, sequence_length)`. inputs_embeds (`tf.Tensor` of
                  shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
                  `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
                  control over how to convert `input_ids` indices into associated vectors than the model's internal
                  embedding lookup matrix.
              output_attentions (`bool`, *optional*):
                  Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                  returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
                  in the config will be used instead.
              output_hidden_states (`bool`, *optional*):
                  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                  for more detail. This argument can be used only in eager mode, in graph mode the value in the config
                  will be used instead.
              return_dict (`bool`, *optional*):
                  Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
                  in eager mode, in graph mode the value will always be set to True.
              training (`bool`, *optional*, defaults to `False`):
                  Whether or not to use the model in training mode (some modules like dropout modules have different
                  behaviors between training and evaluation).
          """
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = shape_list(input_ids)
          elif inputs_embeds is not None:
              input_shape = shape_list(inputs_embeds)[:-1]
          else:
              raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
  
          past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
  
          # embed positions
          if position_ids is None:
              positions = self.embed_positions(input_shape, past_key_values_length)
          else:
              positions = self.embed_positions(input_shape, position_ids=position_ids)
  
          if inputs_embeds is None:
              inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  
          hidden_states = inputs_embeds
  
          # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
          if input_shape[-1] > 1:
              combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
          else:
              combined_attention_mask = _expand_mask(
                  tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
              )
  
          if attention_mask is not None:
              combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
  
          if encoder_hidden_states is not None and encoder_attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
  
          hidden_states = self.dropout(hidden_states + positions, training=training)
  
          # decoder layers
          all_hidden_states = () if output_hidden_states else None
          all_self_attns = () if output_attentions else None
          all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
          present_key_values = () if use_cache else None
  
          # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
          # The tf.debugging asserts are not compliant with XLA then they
          # have to be disabled in other modes than eager.
          for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
              if attn_mask is not None and tf.executing_eagerly():
                  tf.debugging.assert_equal(
                      shape_list(attn_mask)[0],
                      len(self.layers),
                      message=(
                          f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for"
                          f" {shape_list(attn_mask)[0]}."
                      ),
                  )
  
          for idx, decoder_layer in enumerate(self.layers):
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              if output_hidden_states:
                  all_hidden_states += (hidden_states,)
              dropout_probability = random.uniform(0, 1)
  
              if training and (dropout_probability < self.layerdrop):
                  continue
  
              past_key_value = past_key_values[idx] if past_key_values is not None else None
  
              hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
                  hidden_states,
                  attention_mask=combined_attention_mask,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_attention_mask,
                  layer_head_mask=head_mask[idx] if head_mask is not None else None,
                  cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
                  past_key_value=past_key_value,
              )
  
              if use_cache:
                  present_key_values += (present_key_value,)
  
              if output_attentions:
                  all_self_attns += (layer_self_attn,)
  
                  if encoder_hidden_states is not None:
                      all_cross_attns += (layer_cross_attn,)
  
          hidden_states = self.layer_norm(hidden_states)
  
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          if not return_dict:
              return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
          else:
              return TFBaseModelOutputWithPastAndCrossAttentions(
                  last_hidden_state=hidden_states,
                  past_key_values=present_key_values,
                  hidden_states=all_hidden_states,
                  attentions=all_self_attns,
                  cross_attentions=all_cross_attns,
              )
  
  
  @keras_serializable
  class TFPegasusMainLayer(tf.keras.layers.Layer):
      config_class = PegasusConfig
  
      def __init__(self, config: PegasusConfig, **kwargs):
          super().__init__(**kwargs)
  
          self.config = config
          self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
  
          with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
              pass
  
          # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
          embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
          embed_tokens.vocab_size = self.shared.vocab_size
          embed_tokens.hidden_size = self.shared.hidden_size
  
          self.encoder = TFPegasusEncoder(config, embed_tokens, name="encoder")
          self.decoder = TFPegasusDecoder(config, embed_tokens, name="decoder")
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, new_embeddings):
          self.shared.weight = new_embeddings
          self.shared.vocab_size = self.shared.weight.shape[0]
          # retrieve correct absolute scope for embed token wrapper
          with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
              pass
          # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
          embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
          self.encoder.set_embed_tokens(embed_tokens)
          self.decoder.set_embed_tokens(embed_tokens)
  
      @unpack_inputs
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          decoder_position_ids=None,
          head_mask=None,
          decoder_head_mask=None,
          cross_attn_head_mask=None,
          encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs
      ):
  
          if decoder_input_ids is None and decoder_inputs_embeds is None:
              use_cache = False
  
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
  
          if encoder_outputs is None:
              encoder_outputs = self.encoder(
                  input_ids=input_ids,
                  attention_mask=attention_mask,
                  head_mask=head_mask,
                  inputs_embeds=inputs_embeds,
                  output_attentions=output_attentions,
                  output_hidden_states=output_hidden_states,
                  return_dict=return_dict,
                  training=training,
              )
          # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
          elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
              encoder_outputs = TFBaseModelOutput(
                  last_hidden_state=encoder_outputs[0],
                  hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                  attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
              )
          # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
          elif not return_dict and not isinstance(encoder_outputs, tuple):
              encoder_outputs = encoder_outputs.to_tuple()
  
          decoder_outputs = self.decoder(
              decoder_input_ids,
              attention_mask=decoder_attention_mask,
              position_ids=decoder_position_ids,
              encoder_hidden_states=encoder_outputs[0],
              encoder_attention_mask=attention_mask,
              head_mask=decoder_head_mask,
              cross_attn_head_mask=cross_attn_head_mask,
              past_key_values=past_key_values,
              inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
  
          if not return_dict:
              return decoder_outputs + encoder_outputs
  
          return TFSeq2SeqModelOutput(
              last_hidden_state=decoder_outputs.last_hidden_state,
              past_key_values=decoder_outputs.past_key_values,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              cross_attentions=decoder_outputs.cross_attentions,
              encoder_last_hidden_state=encoder_outputs.last_hidden_state,
              encoder_hidden_states=encoder_outputs.hidden_states,
              encoder_attentions=encoder_outputs.attentions,
          )
  
  
  @add_start_docstrings(
      "The bare PEGASUS Model outputting raw hidden-states without any specific head on top.",
      PEGASUS_START_DOCSTRING,
  )
  class TFPegasusModel(TFPegasusPreTrainedModel):
      def __init__(self, config: PegasusConfig, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.model = TFPegasusMainLayer(config, name="model")
  
      def get_encoder(self):
          return self.model.encoder
  
      def get_decoder(self):
          return self.model.decoder
  
      @unpack_inputs
      @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFSeq2SeqModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          decoder_position_ids=None,
          head_mask=None,
          decoder_head_mask=None,
          cross_attn_head_mask=None,
          encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          training=False,
          **kwargs
      ):
  
          outputs = self.model(
              input_ids=input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              decoder_position_ids=decoder_position_ids,
              head_mask=head_mask,
              decoder_head_mask=decoder_head_mask,
              cross_attn_head_mask=cross_attn_head_mask,
              encoder_outputs=encoder_outputs,
              past_key_values=past_key_values,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
  
          return outputs
  
      # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
      def serving_output(self, output):
          pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
          dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
          dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
          cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
          enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
          enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
  
          return TFSeq2SeqModelOutput(
              last_hidden_state=output.last_hidden_state,
              past_key_values=pkv,
              decoder_hidden_states=dec_hs,
              decoder_attentions=dec_attns,
              cross_attentions=cross_attns,
              encoder_last_hidden_state=output.encoder_last_hidden_state,
              encoder_hidden_states=enc_hs,
              encoder_attentions=enc_attns,
          )
  
  
  @add_start_docstrings(
      "The PEGASUS Model with a language modeling head. Can be used for summarization.",
      PEGASUS_START_DOCSTRING,
  )
  class TFPegasusForConditionalGeneration(TFPegasusPreTrainedModel, TFCausalLanguageModelingLoss):
      _keys_to_ignore_on_load_unexpected = [
          r"model.encoder.embed_tokens.weight",
          r"model.decoder.embed_tokens.weight",
      ]
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.model = TFPegasusMainLayer(config, name="model")
          self.use_cache = config.use_cache
          # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
          self.final_logits_bias = self.add_weight(
              name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
          )
  
      def get_decoder(self):
          return self.model.decoder
  
      def get_encoder(self):
          return self.model.encoder
  
      def get_output_embeddings(self):
          return self.get_input_embeddings()
  
      def set_output_embeddings(self, value):
          self.set_input_embeddings(value)
  
      def get_bias(self):
          return {"final_logits_bias": self.final_logits_bias}
  
      def set_bias(self, value):
          self.final_logits_bias = value["final_logits_bias"]
  
      @unpack_inputs
      @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      @add_end_docstrings(PEGASUS_GENERATION_EXAMPLE)
      def call(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          decoder_position_ids=None,
          head_mask=None,
          decoder_head_mask=None,
          cross_attn_head_mask=None,
          encoder_outputs: Optional[TFBaseModelOutput] = None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
          training=False,
      ):
          """
          labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
              Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
              config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
              (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
  
          Returns:
  
          """
  
          if labels is not None:
              labels = tf.where(
                  labels == self.config.pad_token_id,
                  tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
                  labels,
              )
              use_cache = False
              if decoder_input_ids is None:
                  decoder_input_ids = shift_tokens_right(
                      labels, self.config.pad_token_id, self.config.decoder_start_token_id
                  )
  
          outputs = self.model(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              encoder_outputs=encoder_outputs,
              decoder_attention_mask=decoder_attention_mask,
              decoder_position_ids=decoder_position_ids,
              head_mask=head_mask,
              decoder_head_mask=decoder_head_mask,
              cross_attn_head_mask=cross_attn_head_mask,
              past_key_values=past_key_values,
              inputs_embeds=inputs_embeds,
              decoder_inputs_embeds=decoder_inputs_embeds,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          lm_logits = self.model.shared(outputs[0], mode="linear")
          lm_logits = lm_logits + self.final_logits_bias
          masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
  
          if not return_dict:
              output = (lm_logits,) + outputs[1:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
          return TFSeq2SeqLMOutput(
              loss=masked_lm_loss,
              logits=lm_logits,
              past_key_values=outputs.past_key_values,  # index 1 of d outputs
              decoder_hidden_states=outputs.decoder_hidden_states,  # index 2 of d outputs
              decoder_attentions=outputs.decoder_attentions,  # index 3 of d outputs
              cross_attentions=outputs.cross_attentions,  # index 4 of d outputs
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,  # index 0 of encoder outputs
              encoder_hidden_states=outputs.encoder_hidden_states,  # 1 of e out
              encoder_attentions=outputs.encoder_attentions,  # 2 of e out
          )
  
      # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
      def serving_output(self, output):
          pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
          dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
          dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
          cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
          enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
          enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
  
          return TFSeq2SeqLMOutput(
              logits=output.logits,
              past_key_values=pkv,
              decoder_hidden_states=dec_hs,
              decoder_attentions=dec_attns,
              cross_attentions=cross_attns,
              encoder_last_hidden_state=output.encoder_last_hidden_state,
              encoder_hidden_states=enc_hs,
              encoder_attentions=enc_attns,
          )
  
      # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation
      def prepare_inputs_for_generation(
          self,
          decoder_input_ids,
          past=None,
          attention_mask=None,
          decoder_attention_mask=None,
          head_mask=None,
          decoder_head_mask=None,
          cross_attn_head_mask=None,
          use_cache=None,
          encoder_outputs=None,
          **kwargs
      ):
  
          # cut decoder_input_ids if past is used
          if past is not None:
              decoder_input_ids = decoder_input_ids[:, -1:]
  
          if decoder_attention_mask is not None:  # xla
              decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
          elif past is not None:  # no xla + past
              decoder_position_ids = past[0][0].shape[2]
          else:  # no xla + no past
              decoder_position_ids = tf.range(decoder_input_ids.shape[1])
  
          return {
              "input_ids": None,  # encoder_outputs is defined. input_ids not needed
              "encoder_outputs": encoder_outputs,
              "past_key_values": past,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_mask,
              "decoder_attention_mask": decoder_attention_mask,
              "decoder_position_ids": decoder_position_ids,
              "head_mask": head_mask,
              "decoder_head_mask": decoder_head_mask,
              "cross_attn_head_mask": cross_attn_head_mask,
              "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
          }
  
      def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
          return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
  
      @staticmethod
      # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration._reorder_cache
      def _reorder_cache(past, beam_idx):
          reordered_past = ()
          for layer_past in past:
              # cached cross_attention states don't have to be reordered -> they are always the same
              reordered_past += (
                  tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past[:2]) + layer_past[2:],
              )
          return reordered_past
  