text: from dataclasses import dataclass
  from typing import List, Optional, Tuple
  
  import tensorflow as tf
  
  from .file_utils import ModelOutput
  
  
  @dataclass
  class TFBaseModelOutput(ModelOutput):
      """
      Base class for model's outputs, with potential hidden states and attentions.
  
      Args:
          last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          hidden_states (:obj:`tuple(tf.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      last_hidden_state: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFBaseModelOutputWithPooling(ModelOutput):
      """
      Base class for model's outputs that also contains a pooling of the last hidden states.
  
      Args:
          last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
              Last layer hidden-state of the first token of the sequence (classification token)
              further processed by a Linear layer and a Tanh activation function. The Linear
              layer weights are trained from the next sentence prediction (classification)
              objective during pretraining.
  
              This output is usually *not* a good summary
              of the semantic content of the input, you're often better with averaging or pooling
              the sequence of hidden-states for the whole input sequence.
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      last_hidden_state: tf.Tensor = None
      pooler_output: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFBaseModelOutputWithPast(ModelOutput):
      """
      Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
  
      Args:
          last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
  
              If `past_key_values` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
          past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`tf.Tensor` of length :obj:`config.n_layers`,  with each tensor of shape
              :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
              ``past_key_values`` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      last_hidden_state: tf.Tensor = None
      past_key_values: Optional[List[tf.Tensor]] = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFSeq2SeqModelOutput(ModelOutput):
      """
      Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
      decoding.
  
      Args:
          last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the decoder of the model.
  
              If ``decoder_past_key_values`` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
          decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`tf.Tensor` of length :obj:`config.n_layers`,  with each tensor of shape
              :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
      """
  
      last_hidden_state: tf.Tensor = None
      decoder_past_key_values: Optional[List[tf.Tensor]] = None
      decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      decoder_attentions: Optional[Tuple[tf.Tensor]] = None
      encoder_last_hidden_state: Optional[tf.Tensor] = None
      encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      encoder_attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFCausalLMOutput(ModelOutput):
      """
      Base class for causal language model (or autoregressive) outputs.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Language modeling loss (for next-token prediction).
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFCausalLMOutputWithPast(ModelOutput):
      """
      Base class for causal language model (or autoregressive) outputs.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Language modeling loss (for next-token prediction).
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`tf.Tensor` of length :obj:`config.n_layers`,  with each tensor of shape
              :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
              ``past_key_values`` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      past_key_values: Optional[List[tf.Tensor]] = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFMaskedLMOutput(ModelOutput):
      """
      Base class for masked language models outputs.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Masked languaged modeling (MLM) loss.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFSeq2SeqLMOutput(ModelOutput):
      """
      Base class for sequence-to-sequence language models outputs.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Languaged modeling loss.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`tf.Tensor` of length :obj:`config.n_layers`,  with each tensor of shape
              :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      decoder_past_key_values: Optional[List[tf.Tensor]] = None
      decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      decoder_attentions: Optional[Tuple[tf.Tensor]] = None
      encoder_last_hidden_state: Optional[tf.Tensor] = None
      encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      encoder_attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFNextSentencePredictorOutput(ModelOutput):
      """
      Base class for outputs of models predicting if two sentences are consecutive or not.
  
      Args:
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, 2)`):
              Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFSequenceClassifierOutput(ModelOutput):
      """
      Base class for outputs of sentence classification models.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
      """
      Base class for outputs of sequence-to-sequence sentence classification models.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`tf.Tensor` of length :obj:`config.n_layers`,  with each tensor of shape
              :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      decoder_past_key_values: Optional[List[tf.Tensor]] = None
      decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      decoder_attentions: Optional[Tuple[tf.Tensor]] = None
      encoder_last_hidden_state: Optional[tf.Tensor] = None
      encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      encoder_attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFMultipleChoiceModelOutput(ModelOutput):
      """
      Base class for outputs of multiple choice models.
  
      Args:
          loss (:obj:`tf.Tensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification loss.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
              `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
  
              Classification scores (before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFTokenClassifierOutput(ModelOutput):
      """
      Base class for outputs of token classification models.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
              Classification loss.
          logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
              Classification scores (before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[tf.Tensor] = None
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFQuestionAnsweringModelOutput(ModelOutput):
      """
      Base class for outputs of question answering models.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
          start_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-start scores (before SoftMax).
          end_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-end scores (before SoftMax).
          hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[tf.Tensor] = None
      start_logits: tf.Tensor = None
      end_logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @dataclass
  class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
      """
      Base class for outputs of sequence-to-sequence question answering models.
  
      Args:
          loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
          start_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-start scores (before SoftMax).
          end_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-end scores (before SoftMax).
          decoder_past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              List of :obj:`tf.Tensor` of length :obj:`config.n_layers`,  with each tensor of shape
              :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).
  
              Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
              used (see ``decoder_past_key_values`` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
          decoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
          encoder_last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder of the model.
          encoder_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
          encoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`tf.Tensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
              self-attention heads.
      """
  
      loss: Optional[tf.Tensor] = None
      start_logits: tf.Tensor = None
      end_logits: tf.Tensor = None
      decoder_past_key_values: Optional[List[tf.Tensor]] = None
      decoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      decoder_attentions: Optional[Tuple[tf.Tensor]] = None
      encoder_last_hidden_state: Optional[tf.Tensor] = None
      encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
      encoder_attentions: Optional[Tuple[tf.Tensor]] = None
  