text: # Copyright 2020 The HuggingFace 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.
  
  from dataclasses import dataclass
  from typing import Optional, Tuple
  
  import torch
  
  from .file_utils import ModelOutput
  
  
  @dataclass
  class BaseModelOutput(ModelOutput):
      """
      Base class for model's outputs, with potential hidden states and attentions.
  
      Args:
          last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class BaseModelOutputWithPooling(ModelOutput):
      """
      Base class for model's outputs that also contains a pooling of the last hidden states.
  
      Args:
          last_hidden_state (:obj:`torch.FloatTensor` 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:`torch.FloatTensor` 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.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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: torch.FloatTensor = None
      pooler_output: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class BaseModelOutputWithPast(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:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
  
              If :obj:`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:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
              ``config.is_encoder_decoder=True`` 2 additional tensors of shape :obj:`(batch_size, num_heads,
              encoder_sequence_length, embed_size_per_head)`.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
              ``config.is_encoder_decoder=True`` in the cross-attention blocks) that can be used (see
              :obj:`past_key_values` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class BaseModelOutputWithCrossAttentions(ModelOutput):
      """
      Base class for model's outputs, with potential hidden states and attentions.
  
      Args:
          last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` and ``config.add_cross_attention=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
      """
  
      last_hidden_state: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
      """
      Base class for model's outputs that also contains a pooling of the last hidden states.
  
      Args:
          last_hidden_state (:obj:`torch.FloatTensor` 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:`torch.FloatTensor` 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.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` and ``config.add_cross_attention=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
              ``config.is_encoder_decoder=True`` 2 additional tensors of shape :obj:`(batch_size, num_heads,
              encoder_sequence_length, embed_size_per_head)`.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
              ``config.is_encoder_decoder=True`` in the cross-attention blocks) that can be used (see
              :obj:`past_key_values` input) to speed up sequential decoding.
      """
  
      last_hidden_state: torch.FloatTensor = None
      pooler_output: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class BaseModelOutputWithPastAndCrossAttentions(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:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
  
              If :obj:`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:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
              ``config.is_encoder_decoder=True`` 2 additional tensors of shape :obj:`(batch_size, num_heads,
              encoder_sequence_length, embed_size_per_head)`.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
              ``config.is_encoder_decoder=True`` in the cross-attention blocks) that can be used (see
              :obj:`past_key_values` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` and ``config.add_cross_attention=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
      """
  
      last_hidden_state: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class Seq2SeqModelOutput(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:`torch.FloatTensor` 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 :obj:`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:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
              shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
              blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class CausalLMOutput(ModelOutput):
      """
      Base class for causal language model (or autoregressive) outputs.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Language modeling loss (for next-token prediction).
          logits (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class CausalLMOutputWithPast(ModelOutput):
      """
      Base class for causal language model (or autoregressive) outputs.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Language modeling loss (for next-token prediction).
          logits (:obj:`torch.FloatTensor` 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:`tuple(tupel(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
              :obj:`past_key_values` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      last_hidden_state: Optional[torch.FloatTensor] = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class CausalLMOutputWithCrossAttentions(ModelOutput):
      """
      Base class for causal language model (or autoregressive) outputs.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Language modeling loss (for next-token prediction).
          logits (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Cross attentions weights after the attention softmax, used to compute the weighted average in the
              cross-attention heads.
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`torch.FloatTensor` tuples of length :obj:`config.n_layers`, with each tuple containing the
              cached key, value states of the self-attention and the cross-attention layers if model is used in
              encoder-decoder setting. Only relevant if ``config.is_decoder = True``.
  
              Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
              :obj:`past_key_values` input) to speed up sequential decoding.
      """
  
      loss: Optional[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class SequenceClassifierOutputWithPast(ModelOutput):
      """
      Base class for outputs of sentence classification models.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          past_key_values (:obj:`tuple(tupel(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
              :obj:`past_key_values` input) to speed up sequential decoding.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class MaskedLMOutput(ModelOutput):
      """
      Base class for masked language models outputs.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Masked language modeling (MLM) loss.
          logits (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class Seq2SeqLMOutput(ModelOutput):
      """
      Base class for sequence-to-sequence language models outputs.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Language modeling loss.
          logits (:obj:`torch.FloatTensor` 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:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
              shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
              blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class NextSentencePredictorOutput(ModelOutput):
      """
      Base class for outputs of models predicting if two sentences are consecutive or not.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):
              Next sequence prediction (classification) loss.
          logits (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class SequenceClassifierOutput(ModelOutput):
      """
      Base class for outputs of sentence classification models.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class Seq2SeqSequenceClassifierOutput(ModelOutput):
      """
      Base class for outputs of sequence-to-sequence sentence classification models.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
              shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
              blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class MultipleChoiceModelOutput(ModelOutput):
      """
      Base class for outputs of multiple choice models.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification loss.
          logits (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class TokenClassifierOutput(ModelOutput):
      """
      Base class for outputs of token classification models.
  
      Args:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
              Classification loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
              Classification scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class QuestionAnsweringModelOutput(ModelOutput):
      """
      Base class for outputs of question answering models.
  
      Args:
          loss (:obj:`torch.FloatTensor` 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:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
              Span-start scores (before SoftMax).
          end_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
              Span-end scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      start_logits: torch.FloatTensor = None
      end_logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  @dataclass
  class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
      """
      Base class for outputs of sequence-to-sequence question answering models.
  
      Args:
          loss (:obj:`torch.FloatTensor` 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:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
              Span-start scores (before SoftMax).
          end_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
              Span-end scores (before SoftMax).
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
              Tuple of :obj:`tuple(torch.FloatTensor)` of length :obj:`config.n_layers`, with each tuple having 2 tensors
              of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
              shape :obj:`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
              blocks) that can be used (see :obj:`past_key_values` input) to speed up sequential decoding.
          decoder_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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.
          cross_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
              weighted average in the cross-attention heads.
          encoder_last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (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(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (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[torch.FloatTensor] = None
      start_logits: torch.FloatTensor = None
      end_logits: torch.FloatTensor = None
      past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
      decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
      cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
      encoder_last_hidden_state: Optional[torch.FloatTensor] = None
      encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
  