code: # coding=utf-8
  # Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
  #
  # 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.
  """PyTorch BART model, ported from the fairseq repo."""
  import math
  import random
  import warnings
  from typing import Optional, Tuple
  
  import numpy as np
  import torch
  import torch.nn.functional as F
  from torch import nn
  from torch.nn import CrossEntropyLoss
  
  from ...activations import ACT2FN
  from ...file_utils import (
      add_code_sample_docstrings,
      add_end_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import (
      BaseModelOutput,
      BaseModelOutputWithPastAndCrossAttentions,
      Seq2SeqLMOutput,
      Seq2SeqModelOutput,
      Seq2SeqQuestionAnsweringModelOutput,
      Seq2SeqSequenceClassifierOutput,
  )
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_bart import BartConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "BartConfig"
  _TOKENIZER_FOR_DOC = "BartTokenizer"
  
  
  BART_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "facebook/bart-base",
      "facebook/bart-large",
      "facebook/bart-large-mnli",
      "facebook/bart-large-cnn",
      "facebook/bart-large-xsum",
      "facebook/mbart-large-en-ro",
  ]
  # This list is incomplete. See all BART models at https://huggingface.co/models?filter=bart
  
  
  def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
      """
      Shift input ids one token to the right, and wrap the last non pad token (usually <eos>).
      """
      prev_output_tokens = input_ids.clone()
  
      assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
      # replace possible -100 values in labels by `pad_token_id`
      prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
  
      index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
      decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
      prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
      prev_output_tokens[:, 0] = decoder_start_tokens
  
      return prev_output_tokens
  
  
  def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
      """
      Make causal mask used for bi-directional self-attention.
      """
      bsz, tgt_len = input_ids_shape
      mask = torch.full((tgt_len, tgt_len), float("-inf"))
      mask_cond = torch.arange(mask.size(-1))
      mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
      mask = mask.to(dtype)
  
      if past_key_values_length > 0:
          mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
      return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
  
  
  def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
      """
      Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
      """
      bsz, src_len = mask.size()
      tgt_len = tgt_len if tgt_len is not None else src_len
  
      expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
  
      inverted_mask = 1.0 - expanded_mask
  
      return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
  
  
  def BartLayerNorm(normalized_shape: torch.Size, eps: float = 1e-5, elementwise_affine: bool = True):
      if torch.cuda.is_available():
          try:
              from apex.normalization import FusedLayerNorm
  
              return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
          except ImportError:
              pass
      return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
  
  
  class BartLearnedPositionalEmbedding(nn.Embedding):
      """
      This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting
      based on padding_idx or by setting padding_idx to None and ensuring that the appropriate position ids are passed to
      the forward function.
      """
  
      def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, offset: int):
          # Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
          # and adjust num_embeddings appropriately. Other models dont have this hack
          self.offset = offset
          assert padding_idx is not None, "`padding_idx` should not be None, but of type int"
          num_embeddings += offset
          super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)
  
      def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
          """`input_ids_shape` is expected to be [bsz x seqlen]."""
          bsz, seq_len = input_ids_shape[:2]
          positions = torch.arange(
              past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
          )
          return super().forward(positions + self.offset)
  
  
  class BartSinusoidalPositionalEmbedding(nn.Embedding):
      """This module produces sinusoidal positional embeddings of any length."""
  
      def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
          super().__init__(num_positions, embedding_dim)
          self.weight = self._init_weight(self.weight)
  
      @staticmethod
      def _init_weight(out: nn.Parameter):
          """
          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:]
          """
          n_pos, dim = out.shape
          position_enc = np.array(
              [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
          )
          out.requires_grad = False  # set early to avoid an error in pytorch-1.8+
          sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
          out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
          out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
          out.detach_()
          return out
  
      @torch.no_grad()
      def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
          """`input_ids_shape` is expected to be [bsz x seqlen]."""
          bsz, seq_len = input_ids_shape[:2]
          positions = torch.arange(
              past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
          )
          return super().forward(positions)
  
  
  class BartAttention(nn.Module):
      """Multi-headed attention from 'Attention Is All You Need' paper"""
  
      def __init__(
          self,
          embed_dim: int,
          num_heads: int,
          dropout: float = 0.0,
          is_decoder: bool = False,
          bias: bool = True,
      ):
          super().__init__()
          self.embed_dim = embed_dim
          self.num_heads = num_heads
          self.dropout = dropout
          self.head_dim = embed_dim // num_heads
          assert (
              self.head_dim * num_heads == self.embed_dim
          ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads})."
          self.scaling = self.head_dim ** -0.5
          self.is_decoder = is_decoder
  
          self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
  
      def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
          return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          key_value_states: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          attention_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.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 = hidden_states.size()
  
          # 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 = torch.cat([past_key_value[0], key_states], dim=2)
              value_states = torch.cat([past_key_value[1], value_states], dim=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(torch.Tensor, torch.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(torch.Tensor, torch.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 = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
          key_states = key_states.view(*proj_shape)
          value_states = value_states.view(*proj_shape)
  
          src_len = key_states.size(1)
          attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
  
          assert attn_weights.size() == (
              bsz * self.num_heads,
              tgt_len,
              src_len,
          ), f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
  
          if attention_mask is not None:
              assert attention_mask.size() == (
                  bsz,
                  1,
                  tgt_len,
                  src_len,
              ), f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
              attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
              attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
  
          attn_weights = F.softmax(attn_weights, dim=-1)
  
          if output_attentions:
              # this operation is a bit akward, but it's required to
              # make sure that attn_weights keeps its gradient.
              # In order to do so, attn_weights have to reshaped
              # twice and have to be reused in the following
              attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
              attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
          else:
              attn_weights_reshaped = None
  
          attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)
  
          attn_output = torch.bmm(attn_probs, value_states)
  
          assert attn_output.size() == (
              bsz * self.num_heads,
              tgt_len,
              self.head_dim,
          ), f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
  
          attn_output = (
              attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
              .transpose(1, 2)
              .reshape(bsz, tgt_len, embed_dim)
          )
  
          attn_output = self.out_proj(attn_output)
  
          return attn_output, attn_weights_reshaped, past_key_value
  
  
  class BartEncoderLayer(nn.Module):
      def __init__(self, config: BartConfig):
          super().__init__()
          self.embed_dim = config.d_model
          self.self_attn = BartAttention(
              embed_dim=self.embed_dim,
              num_heads=config.encoder_attention_heads,
              dropout=config.attention_dropout,
          )
          self.normalize_before = config.normalize_before
          self.self_attn_layer_norm = BartLayerNorm(self.embed_dim)
          self.dropout = config.dropout
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = config.activation_dropout
          self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
          self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
          self.final_layer_norm = BartLayerNorm(self.embed_dim)
  
      def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, output_attentions: bool = False):
          """
          Args:
              hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              output_attentions (:obj:`bool`): Whether the base model outputs attentions. This requires the attentions tensor to be reshaped in this function.
          """
          residual = hidden_states
          if self.normalize_before:
              hidden_states = self.self_attn_layer_norm(hidden_states)
          hidden_states, attn_weights, _ = self.self_attn(
              hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
          )
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          if not self.normalize_before:
              hidden_states = self.self_attn_layer_norm(hidden_states)
  
          residual = hidden_states
          if self.normalize_before:
              hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          if not self.normalize_before:
              hidden_states = self.final_layer_norm(hidden_states)
          if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
              clamp_value = torch.finfo(hidden_states.dtype).max - 1000
              hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
          return hidden_states, attn_weights
  
  
  class BartDecoderLayer(nn.Module):
      def __init__(self, config: BartConfig):
          super().__init__()
          self.embed_dim = config.d_model
  
          self.self_attn = BartAttention(
              embed_dim=self.embed_dim,
              num_heads=config.decoder_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=True,
          )
          self.dropout = config.dropout
          self.activation_fn = ACT2FN[config.activation_function]
          self.activation_dropout = config.activation_dropout
          self.normalize_before = config.normalize_before
  
          self.self_attn_layer_norm = BartLayerNorm(self.embed_dim)
          self.encoder_attn = BartAttention(
              self.embed_dim,
              config.decoder_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=True,
          )
          self.encoder_attn_layer_norm = BartLayerNorm(self.embed_dim)
          self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
          self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
          self.final_layer_norm = BartLayerNorm(self.embed_dim)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          encoder_hidden_states: Optional[torch.Tensor] = None,
          encoder_attention_mask: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          output_attentions: Optional[torch.Tensor] = False,
      ):
          """
          Args:
              hidden_states (:obj:`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
              attention_mask (:obj:`torch.FloatTensor`): attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              encoder_hidden_states (:obj:`torch.FloatTensor`): cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
              encoder_attention_mask (:obj:`torch.FloatTensor`): encoder attention mask of size
                  `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
              past_key_value (:obj:`Tuple(torch.FloatTensor)`): cached past key and value projection states
              output_attentions (:obj:`bool`): Whether the base model outputs attentions. This requires the attentions tensor to be reshaped in this function.
          """
          residual = hidden_states
          if self.normalize_before:
              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,
              output_attentions=output_attentions,
          )
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          if not self.normalize_before:
              hidden_states = self.self_attn_layer_norm(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
              if self.normalize_before:
                  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,
                  past_key_value=cross_attn_past_key_value,
                  output_attentions=output_attentions,
              )
              hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
              hidden_states = residual + hidden_states
              if not self.normalize_before:
                  hidden_states = self.encoder_attn_layer_norm(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
          if self.normalize_before:
              hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = F.dropout(hidden_states, p=self.activation_dropout, training=self.training)
          hidden_states = self.fc2(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
          hidden_states = residual + hidden_states
          if not self.normalize_before:
              hidden_states = self.final_layer_norm(hidden_states)
  
          return (
              hidden_states,
              self_attn_weights,
              present_key_value,
              cross_attn_weights,
          )
  
  
  class BartClassificationHead(nn.Module):
      """Head for sentence-level classification tasks."""
  
      def __init__(
          self,
          input_dim: int,
          inner_dim: int,
          num_classes: int,
          pooler_dropout: float,
      ):
          super().__init__()
          self.dense = nn.Linear(input_dim, inner_dim)
          self.dropout = nn.Dropout(p=pooler_dropout)
          self.out_proj = nn.Linear(inner_dim, num_classes)
  
      def forward(self, hidden_states: torch.Tensor):
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.dense(hidden_states)
          hidden_states = torch.tanh(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.out_proj(hidden_states)
          return hidden_states
  
  
  class BartPretrainedModel(PreTrainedModel):
      config_class = BartConfig
      base_model_prefix = "model"
  
      def _init_weights(self, module):
          std = self.config.init_std
          if isinstance(module, nn.Linear):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, BartSinusoidalPositionalEmbedding):
              pass
          elif isinstance(module, nn.Embedding):
              module.weight.data.normal_(mean=0.0, std=std)
              if module.padding_idx is not None:
                  module.weight.data[module.padding_idx].zero_()
  
      @property
      def dummy_inputs(self):
          pad_token = self.config.pad_token_id
          input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
          dummy_inputs = {
              "attention_mask": input_ids.ne(pad_token),
              "input_ids": input_ids,
          }
          return dummy_inputs
  
  
  class PretrainedBartModel(BartPretrainedModel):
      def __init_subclass__(self):
          warnings.warn(
              "The class `PretrainedBartModel` has been depreciated, please use `BartPretrainedModel` instead.",
              FutureWarning,
          )
  
  
  BART_START_DOCSTRING = r"""
      This model inherits from :class:`~transformers.PreTrainedModel`. 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 PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
      subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
      general usage and behavior.
  
      Parameters:
          config (:class:`~transformers.BartConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
              weights.
  """
  
  BART_GENERATION_EXAMPLE = r"""
      Summarization example::
  
          >>> from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
  
          >>> # see ``examples/summarization/bart/run_eval.py`` for a longer example
          >>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
          >>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
  
          >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
          >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
  
          >>> # Generate Summary
          >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
          >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
  """
  
  BART_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`torch.LongTensor` of shape :obj:`(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 :class:`~transformers.BartTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`torch.Tensor` of shape :obj:`(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.html#attention-mask>`__
          decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Provide for translation and summarization training. By default, the model will create this tensor by
              shifting the :obj:`input_ids` to the right, following the paper.
          decoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
              Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
              also be used by default.
  
              If you want to change padding behavior, you should read :func:`modeling_bart._prepare_decoder_inputs` and
              modify to your needs. See diagram 1 in `the paper <https://arxiv.org/abs/1910.13461>`__ for more
              information on the default strategy.
          encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`):
              Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`:
              :obj:`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`,
              `optional`) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
              cross-attention of the decoder.
          past_key_values (:obj:`Tuple[Tuple[torch.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(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 :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
              (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
              instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size, sequence_length)`.
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
              vectors than the model's internal embedding lookup matrix.
          decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
              Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
              representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds`
              have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert
              :obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
  
              If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds`
              takes the value of :obj:`inputs_embeds`.
          use_cache (:obj:`bool`, `optional`):
              If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
              decoding (see :obj:`past_key_values`).
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  class BartEncoder(BartPretrainedModel):
      """
      Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
      :class:`BartEncoderLayer`.
  
      Args:
          config: BartConfig
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embedding] = None):
          super().__init__(config)
  
          self.dropout = config.dropout
          self.layerdrop = config.encoder_layerdrop
  
          embed_dim = config.d_model
          self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
          self.padding_idx = config.pad_token_id
          self.max_source_positions = config.max_position_embeddings
  
          if embed_tokens is not None:
              self.embed_tokens = embed_tokens
          else:
              self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
  
          if config.static_position_embeddings:
              self.embed_positions = BartSinusoidalPositionalEmbedding(
                  config.max_position_embeddings, embed_dim, self.padding_idx
              )
          else:
              self.embed_positions = BartLearnedPositionalEmbedding(
                  config.max_position_embeddings,
                  embed_dim,
                  self.padding_idx,
                  config.extra_pos_embeddings,
              )
          self.layers = nn.ModuleList([BartEncoderLayer(config) for _ in range(config.encoder_layers)])
          self.layernorm_embedding = BartLayerNorm(embed_dim) if config.normalize_embedding else nn.Identity()
          # mbart has one extra layer_norm
          self.layer_norm = BartLayerNorm(config.d_model) if config.add_final_layer_norm else None
  
          self.init_weights()
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          Args:
              input_ids (:obj:`torch.LongTensor` of shape :obj:`(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 :class:`~transformers.BartTokenizer`. See
                  :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                  for details.
  
                  `What are input IDs? <../glossary.html#input-ids>`__
              attention_mask (:obj:`torch.Tensor` of shape :obj:`(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.html#attention-mask>`__
              inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
                  Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded
                  representation. This is useful if you want more control over how to convert :obj:`input_ids` indices
                  into associated vectors than the model's internal embedding lookup matrix.
              output_attentions (:obj:`bool`, `optional`):
                  Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                  returned tensors for more detail.
              output_hidden_states (:obj:`bool`, `optional`):
                  Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
                  for more detail.
              return_dict (:obj:`bool`, `optional`):
                  Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # retrieve input_ids and inputs_embeds
          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 = input_ids.size()
              input_ids = input_ids.view(-1, input_shape[-1])
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-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.layernorm_embedding(hidden_states)
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
  
          # expand attention_mask
          if attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
  
          encoder_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
          for encoder_layer in 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 self.training and (dropout_probability < self.layerdrop):  # skip the layer
                  attn = None
              else:
                  hidden_states, attn = encoder_layer(hidden_states, attention_mask, output_attentions=output_attentions)
  
              if output_attentions:
                  all_attentions = all_attentions + (attn,)
  
          if self.layer_norm:
              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 BaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
          )
  
  
  class BartDecoder(BartPretrainedModel):
      """
      Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`BartDecoderLayer`
  
      Args:
          config: BartConfig
          embed_tokens (torch.nn.Embedding): output embedding
      """
  
      def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embedding] = None):
          super().__init__(config)
          self.dropout = config.dropout
          self.layerdrop = config.decoder_layerdrop
          self.do_blenderbot_90_layernorm = config.do_blenderbot_90_layernorm  # layernorm variant
          self.padding_idx = config.pad_token_id
          self.max_target_positions = config.max_position_embeddings
          self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
  
          if embed_tokens is not None:
              self.embed_tokens = embed_tokens
          else:
              self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
  
          if config.static_position_embeddings:
              self.embed_positions = BartSinusoidalPositionalEmbedding(
                  config.max_position_embeddings, config.d_model, config.pad_token_id
              )
          else:
              self.embed_positions = BartLearnedPositionalEmbedding(
                  config.max_position_embeddings,
                  config.d_model,
                  self.padding_idx,
                  config.extra_pos_embeddings,
              )
          self.layers = nn.ModuleList([BartDecoderLayer(config) for _ in range(config.decoder_layers)])
          self.layernorm_embedding = BartLayerNorm(config.d_model) if config.normalize_embedding else nn.Identity()
          self.layer_norm = BartLayerNorm(config.d_model) if config.add_final_layer_norm else None
  
          self.init_weights()
  
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_values=None,
          inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          Args:
              input_ids (:obj:`torch.LongTensor` of shape :obj:`(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 :class:`~transformers.BartTokenizer`. See
                  :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
                  for details.
  
                  `What are input IDs? <../glossary.html#input-ids>`__
              attention_mask (:obj:`torch.Tensor` of shape :obj:`(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.html#attention-mask>`__
              encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.LongTensor` of shape :obj:`(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.html#attention-mask>`__
              past_key_values (:obj:`Tuple[Tuple[torch.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(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 :obj:`past_key_values` are used, the user can optionally input only the last
                  :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of
                  shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size,
                  sequence_length)`.
              inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
                  Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded
                  representation. This is useful if you want more control over how to convert :obj:`input_ids` indices
                  into associated vectors than the model's internal embedding lookup matrix.
              output_attentions (:obj:`bool`, `optional`):
                  Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                  returned tensors for more detail.
              output_hidden_states (:obj:`bool`, `optional`):
                  Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
                  for more detail.
              return_dict (:obj:`bool`, `optional`):
                  Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          # retrieve input_ids and inputs_embeds
          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 = input_ids.size()
              input_ids = input_ids.view(-1, input_shape[-1])
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
  
          # past_key_values_length
          past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
  
          if inputs_embeds is None:
              inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  
          # create causal mask
          # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
          combined_attention_mask = None
          if input_shape[-1] > 1:
              combined_attention_mask = _make_causal_mask(
                  input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
              ).to(self.device)
  
          # create decoder_padding_mask if not provided and needed
          # 4.12.20 (PVP): Not a fan of this "magical" function that
          # automatically creates attention_mask for padded tokens
          # => this is inconsistent with other models
          # => Pegasus uses the pad_token as decoder_start_token_id, so that this could
          # pose some problems.
          if (
              attention_mask is None
              and input_ids is not None
              and input_shape[-1] > 1
              and self.config.pad_token_id in input_ids
          ):
              # should be kept for backwards compatibility
              attention_mask = input_ids.ne(self.config.pad_token_id).to(torch.long)
              # never mask leading token, even if it is pad
              attention_mask[:, 0] = attention_mask[:, 1]
              if past_key_values_length > 0:
                  attention_mask = torch.cat(
                      [
                          torch.ones(
                              (input_shape[0], past_key_values_length), dtype=torch.long, device=input_ids.device
                          ),
                          attention_mask,
                      ],
                      dim=-1,
                  )
  
          if attention_mask is not None and combined_attention_mask is not None:
              # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
              combined_attention_mask = combined_attention_mask + _expand_mask(
                  attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
              )
  
          # expand encoder attention mask
          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, inputs_embeds.dtype, tgt_len=input_shape[-1])
  
          # embed positions
          positions = self.embed_positions(input_shape, past_key_values_length)
  
          if self.do_blenderbot_90_layernorm:
              hidden_states = self.layernorm_embedding(inputs_embeds)
              hidden_states += positions
          else:
              hidden_states = inputs_embeds + positions
              hidden_states = self.layernorm_embedding(hidden_states)
  
          hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
  
          # decoder layers
          all_hidden_states = () if output_hidden_states else None
          all_self_attns = () if output_attentions else None
          all_cross_attentions = () if output_attentions else None
          next_decoder_cache = () if use_cache else None
          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 self.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, present_key_value, layer_cross_attn = decoder_layer(
                  hidden_states,
                  attention_mask=combined_attention_mask,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_attention_mask,
                  past_key_value=past_key_value,
                  output_attentions=output_attentions,
              )
  
              if use_cache:
                  next_decoder_cache += (present_key_value,)
  
              if output_attentions:
                  all_self_attns += (layer_self_attn,)
                  all_cross_attentions += (layer_cross_attn,)
  
          # add hidden states from the last decoder layer
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          # if config.add_final_layer_norm (mBART)
          if self.layer_norm:
              hidden_states = self.layer_norm(hidden_states)
  
          next_cache = next_decoder_cache if use_cache else None
          if not return_dict:
              return tuple(
                  v
                  for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                  if v is not None
              )
          return BaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=hidden_states,
              past_key_values=next_cache,
              hidden_states=all_hidden_states,
              attentions=all_self_attns,
              cross_attentions=all_cross_attentions,
          )
  
  
  @add_start_docstrings(
      "The bare BART Model outputting raw hidden-states without any specific head on top.",
      BART_START_DOCSTRING,
  )
  class BartModel(BartPretrainedModel):
      def __init__(self, config: BartConfig):
          super().__init__(config)
  
          padding_idx, vocab_size = config.pad_token_id, config.vocab_size
          self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
  
          self.encoder = BartEncoder(config, self.shared)
          self.decoder = BartDecoder(config, self.shared)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.shared
  
      def set_input_embeddings(self, value):
          self.shared = value
          self.encoder.embed_tokens = self.shared
          self.decoder.embed_tokens = self.shared
  
      def get_encoder(self):
          return self.encoder
  
      def get_decoder(self):
          return self.decoder
  
      @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="facebook/bart-large",
          output_type=Seq2SeqModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=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,
      ):
  
          # 4.12.20 (PVP): Not a fan of this "magical" function and
          # also wonder how often it's actually used ... keep now
          # for backward compatibility
          # -> is this used for backward compatibility
          if decoder_input_ids is None and decoder_inputs_embeds is None:
              decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)
  
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          use_cache = use_cache if use_cache is not None else self.config.use_cache
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if encoder_outputs is None:
              encoder_outputs = self.encoder(
                  input_ids=input_ids,
                  attention_mask=attention_mask,
                  inputs_embeds=inputs_embeds,
                  output_attentions=output_attentions,
                  output_hidden_states=output_hidden_states,
                  return_dict=return_dict,
              )
          # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=False
          elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
              encoder_outputs = BaseModelOutput(
                  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,
              )
  
          # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
          decoder_outputs = self.decoder(
              input_ids=decoder_input_ids,
              attention_mask=decoder_attention_mask,
              encoder_hidden_states=encoder_outputs[0],
              encoder_attention_mask=attention_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,
          )
  
          if not return_dict:
              return decoder_outputs + encoder_outputs
  
          return Seq2SeqModelOutput(
              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 BART Model with a language modeling head. Can be used for summarization.", BART_START_DOCSTRING
  )
  class BartForConditionalGeneration(BartPretrainedModel):
      base_model_prefix = "model"
      _keys_to_ignore_on_load_missing = [
          r"final_logits_bias",
          r"encoder\.version",
          r"decoder\.version",
          r"lm_head\.weight",
      ]
  
      def __init__(self, config: BartConfig):
          super().__init__(config)
          self.model = BartModel(config)
          self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
          self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
  
          self.init_weights()
  
      def get_encoder(self):
          return self.model.get_encoder()
  
      def get_decoder(self):
          return self.model.get_decoder()
  
      def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
          new_embeddings = super().resize_token_embeddings(new_num_tokens)
          self._resize_final_logits_bias(new_num_tokens)
          return new_embeddings
  
      def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
          old_num_tokens = self.final_logits_bias.shape[-1]
          if new_num_tokens <= old_num_tokens:
              new_bias = self.final_logits_bias[:, :new_num_tokens]
          else:
              extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
              new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
          self.register_buffer("final_logits_bias", new_bias)
  
      def get_output_embeddings(self):
          return self.lm_head
  
      def set_output_embeddings(self, new_embeddings):
          self.lm_head = new_embeddings
  
      @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
      @add_end_docstrings(BART_GENERATION_EXAMPLE)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          past_key_values=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          labels=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(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:
  
          Conditional generation example::
  
              >>> # Mask filling only works for bart-large
              >>> from transformers import BartTokenizer, BartForConditionalGeneration
              >>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
              >>> TXT = "My friends are <mask> but they eat too many carbs."
  
              >>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
              >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
              >>> logits = model(input_ids).logits
  
              >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
              >>> probs = logits[0, masked_index].softmax(dim=0)
              >>> values, predictions = probs.topk(5)
  
              >>> tokenizer.decode(predictions).split()
              >>> # ['good', 'great', 'all', 'really', 'very']
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          if labels is not None:
              use_cache = False
              if decoder_input_ids is None:
                  decoder_input_ids = shift_tokens_right(labels, self.config.pad_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,
              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,
          )
          lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              # TODO(SS): do we need to ignore pad tokens in labels?
              masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (lm_logits,) + outputs[1:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return Seq2SeqLMOutput(
              loss=masked_lm_loss,
              logits=lm_logits,
              past_key_values=outputs.past_key_values,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  
      def prepare_inputs_for_generation(
          self, decoder_input_ids, past=None, attention_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:]
  
          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,
              "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
          }
  
      def adjust_logits_during_generation(self, logits, cur_len, max_length):
          if cur_len == 1 and self.config.force_bos_token_to_be_generated:
              self._force_token_id_to_be_generated(logits, self.config.bos_token_id)
          elif cur_len == max_length - 1 and self.config.eos_token_id is not None:
              self._force_token_id_to_be_generated(logits, self.config.eos_token_id)
          return logits
  
      @staticmethod
      def _force_token_id_to_be_generated(scores, token_id) -> None:
          """force one of token_ids to be generated by setting prob of all other tokens to 0 (logprob=-float("inf"))"""
          scores[:, [x for x in range(scores.shape[1]) if x != token_id]] = -float("inf")
  
      @staticmethod
      def _reorder_cache(past, beam_idx):
          reordered_past = ()
          for layer_past in past:
              reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
          return reordered_past
  
  
  @add_start_docstrings(
      """
      Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
      tasks.
      """,
      BART_START_DOCSTRING,
  )
  class BartForSequenceClassification(BartPretrainedModel):
      def __init__(self, config: BartConfig, **kwargs):
          super().__init__(config, **kwargs)
          self.model = BartModel(config)
          self.classification_head = BartClassificationHead(
              config.d_model,
              config.d_model,
              config.num_labels,
              config.classifier_dropout,
          )
          self.model._init_weights(self.classification_head.dense)
          self.model._init_weights(self.classification_head.out_proj)
  
      @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="facebook/bart-large",
          output_type=Seq2SeqSequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          labels=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
              config.num_labels - 1]`. If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          if labels is not None:
              use_cache = False
  
          if input_ids is None and inputs_embeds is not None:
              raise NotImplementedError(
                  f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
              )
  
          outputs = self.model(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              encoder_outputs=encoder_outputs,
              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,
          )
          hidden_states = outputs[0]  # last hidden state
  
          eos_mask = input_ids.eq(self.config.eos_token_id)
  
          if len(torch.unique(eos_mask.sum(1))) > 1:
              raise ValueError("All examples must have the same number of <eos> tokens.")
          sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
              :, -1, :
          ]
          logits = self.classification_head(sentence_representation)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return Seq2SeqSequenceClassifierOutput(
              loss=loss,
              logits=logits,
              past_key_values=outputs.past_key_values,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  
  
  @add_start_docstrings(
      """
      BART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
      layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
      """,
      BART_START_DOCSTRING,
  )
  class BartForQuestionAnswering(BartPretrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          config.num_labels = 2
          self.num_labels = config.num_labels
  
          self.model = BartModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.model._init_weights(self.qa_outputs)
  
      @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint="facebook/bart-large",
          output_type=Seq2SeqQuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          decoder_input_ids=None,
          decoder_attention_mask=None,
          encoder_outputs=None,
          start_positions=None,
          end_positions=None,
          inputs_embeds=None,
          decoder_inputs_embeds=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the start of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for position (index) of the end of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          if start_positions is not None and end_positions is not None:
              use_cache = False
  
          outputs = self.model(
              input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              encoder_outputs=encoder_outputs,
              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,
          )
  
          sequence_output = outputs[0]
  
          logits = self.qa_outputs(sequence_output)
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          total_loss = None
          if start_positions is not None and end_positions is not None:
              # If we are on multi-GPU, split add a dimension
              if len(start_positions.size()) > 1:
                  start_positions = start_positions.squeeze(-1)
              if len(end_positions.size()) > 1:
                  end_positions = end_positions.squeeze(-1)
              # sometimes the start/end positions are outside our model inputs, we ignore these terms
              ignored_index = start_logits.size(1)
              start_positions.clamp_(0, ignored_index)
              end_positions.clamp_(0, ignored_index)
  
              loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
              start_loss = loss_fct(start_logits, start_positions)
              end_loss = loss_fct(end_logits, end_positions)
              total_loss = (start_loss + end_loss) / 2
  
          if not return_dict:
              output = (
                  start_logits,
                  end_logits,
              ) + outputs[1:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return Seq2SeqQuestionAnsweringModelOutput(
              loss=total_loss,
              start_logits=start_logits,
              end_logits=end_logits,
              past_key_values=outputs.past_key_values,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  