text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  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.
  """PyTorch RoBERTa model. """
  
  
  import logging
  import warnings
  
  import torch
  import torch.nn as nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from .configuration_roberta import RobertaConfig
  from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
  from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
  from .modeling_utils import create_position_ids_from_input_ids
  
  
  logger = logging.getLogger(__name__)
  
  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "roberta-base",
      "roberta-large",
      "roberta-large-mnli",
      "distilroberta-base",
      "roberta-base-openai-detector",
      "roberta-large-openai-detector",
      # See all RoBERTa models at https://huggingface.co/models?filter=roberta
  ]
  
  
  class RobertaEmbeddings(BertEmbeddings):
      """
      Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
      """
  
      def __init__(self, config):
          super().__init__(config)
          self.padding_idx = config.pad_token_id
          self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
          self.position_embeddings = nn.Embedding(
              config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
          )
  
      def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
          if position_ids is None:
              if input_ids is not None:
                  # Create the position ids from the input token ids. Any padded tokens remain padded.
                  position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
              else:
                  position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
  
          return super().forward(
              input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds
          )
  
      def create_position_ids_from_inputs_embeds(self, inputs_embeds):
          """ We are provided embeddings directly. We cannot infer which are padded so just generate
          sequential position ids.
  
          :param torch.Tensor inputs_embeds:
          :return torch.Tensor:
          """
          input_shape = inputs_embeds.size()[:-1]
          sequence_length = input_shape[1]
  
          position_ids = torch.arange(
              self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
          )
          return position_ids.unsqueeze(0).expand(input_shape)
  
  
  ROBERTA_START_DOCSTRING = r"""
  
      This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
      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.RobertaConfig`): 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.
  """
  
  ROBERTA_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`transformers.RobertaTokenizer`.
              See :func:`transformers.PreTrainedTokenizer.encode` and
              :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
              Mask to avoid performing attention on padding token indices.
              Mask values selected in ``[0, 1]``:
              ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
              Segment token indices to indicate first and second portions of the inputs.
              Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
              corresponds to a `sentence B` token
  
              `What are token type IDs? <../glossary.html#token-type-ids>`_
          position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
              Indices of positions of each input sequence tokens in the position embeddings.
              Selected in the range ``[0, config.max_position_embeddings - 1]``.
  
              `What are position IDs? <../glossary.html#position-ids>`_
          head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
              Mask to nullify selected heads of the self-attention modules.
              Mask values selected in ``[0, 1]``:
              :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
              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 `input_ids` indices into associated vectors
              than the model's internal embedding lookup matrix.
          output_attentions (:obj:`bool`, `optional`, defaults to `:obj:`None`):
              If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
  """
  
  
  @add_start_docstrings(
      "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
      ROBERTA_START_DOCSTRING,
  )
  class RobertaModel(BertModel):
      """
      This class overrides :class:`~transformers.BertModel`. Please check the
      superclass for the appropriate documentation alongside usage examples.
      """
  
      config_class = RobertaConfig
      base_model_prefix = "roberta"
  
      def __init__(self, config):
          super().__init__(config)
  
          self.embeddings = RobertaEmbeddings(config)
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
  
  
  @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
  class RobertaForMaskedLM(BertPreTrainedModel):
      config_class = RobertaConfig
      base_model_prefix = "roberta"
  
      def __init__(self, config):
          super().__init__(config)
  
          self.roberta = RobertaModel(config)
          self.lm_head = RobertaLMHead(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.lm_head.decoder
  
      @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
          **kwargs
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Labels for computing the masked language modeling loss.
              Indices should be in ``[-100, 0, ..., config.vocab_size]`` (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]``
          kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
              Used to hide legacy arguments that have been deprecated.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
          masked_lm_loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Masked language modeling loss.
          prediction_scores (: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 ``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 ``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.
  
      Examples::
  
          from transformers import RobertaTokenizer, RobertaForMaskedLM
          import torch
  
          tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
          model = RobertaForMaskedLM.from_pretrained('roberta-base')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=input_ids)
          loss, prediction_scores = outputs[:2]
  
          """
          if "masked_lm_labels" in kwargs:
              warnings.warn(
                  "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
                  DeprecationWarning,
              )
              labels = kwargs.pop("masked_lm_labels")
          assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
  
          outputs = self.roberta(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
          )
          sequence_output = outputs[0]
          prediction_scores = self.lm_head(sequence_output)
  
          outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
  
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
              outputs = (masked_lm_loss,) + outputs
  
          return outputs  # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
  
  
  class RobertaLMHead(nn.Module):
      """Roberta Head for masked language modeling."""
  
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.layer_norm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
          self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
          self.bias = nn.Parameter(torch.zeros(config.vocab_size))
  
          # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
          self.decoder.bias = self.bias
  
      def forward(self, features, **kwargs):
          x = self.dense(features)
          x = gelu(x)
          x = self.layer_norm(x)
  
          # project back to size of vocabulary with bias
          x = self.decoder(x)
  
          return x
  
  
  @add_start_docstrings(
      """RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
      on top of the pooled output) e.g. for GLUE tasks. """,
      ROBERTA_START_DOCSTRING,
  )
  class RobertaForSequenceClassification(BertPreTrainedModel):
      config_class = RobertaConfig
      base_model_prefix = "roberta"
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.roberta = RobertaModel(config)
          self.classifier = RobertaClassificationHead(config)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              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 regression loss is computed (Mean-Square loss),
              If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
          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).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned 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 ``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.
  
      Examples::
  
          from transformers import RobertaTokenizer, RobertaForSequenceClassification
          import torch
  
          tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
          model = RobertaForSequenceClassification.from_pretrained('roberta-base')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
          loss, logits = outputs[:2]
  
          """
          outputs = self.roberta(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
          )
          sequence_output = outputs[0]
          logits = self.classifier(sequence_output)
  
          outputs = (logits,) + outputs[2:]
          if labels is not None:
              if self.num_labels == 1:
                  #  We are doing regression
                  loss_fct = MSELoss()
                  loss = loss_fct(logits.view(-1), labels.view(-1))
              else:
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), logits, (hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """Roberta Model with a multiple choice classification head on top (a linear layer on top of
      the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
      ROBERTA_START_DOCSTRING,
  )
  class RobertaForMultipleChoice(BertPreTrainedModel):
      config_class = RobertaConfig
      base_model_prefix = "roberta"
  
      def __init__(self, config):
          super().__init__(config)
  
          self.roberta = RobertaModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, 1)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
      def forward(
          self,
          input_ids=None,
          token_type_ids=None,
          attention_mask=None,
          labels=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          output_attentions=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              Labels for computing the multiple choice classification loss.
              Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
              of the input tensors. (see `input_ids` above)
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
          loss (:obj:`torch.FloatTensor`` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification loss.
          classification_scores (: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 ``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 ``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.
  
      Examples::
  
          from transformers import RobertaTokenizer, RobertaForMultipleChoice
          import torch
  
          tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
          model = RobertaForMultipleChoice.from_pretrained('roberta-base')
          choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
          input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
          labels = torch.tensor(1).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
          loss, classification_scores = outputs[:2]
  
          """
          num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
  
          flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
          flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
          flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
          flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
          flat_inputs_embeds = (
              inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
              if inputs_embeds is not None
              else None
          )
  
          outputs = self.roberta(
              flat_input_ids,
              position_ids=flat_position_ids,
              token_type_ids=flat_token_type_ids,
              attention_mask=flat_attention_mask,
              head_mask=head_mask,
              inputs_embeds=flat_inputs_embeds,
              output_attentions=output_attentions,
          )
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
          reshaped_logits = logits.view(-1, num_choices)
  
          outputs = (reshaped_logits,) + outputs[2:]  # add hidden states and attention if they are here
  
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels)
              outputs = (loss,) + outputs
  
          return outputs  # (loss), reshaped_logits, (hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """Roberta Model with a token classification head on top (a linear layer on top of
      the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
      ROBERTA_START_DOCSTRING,
  )
  class RobertaForTokenClassification(BertPreTrainedModel):
      config_class = RobertaConfig
      base_model_prefix = "roberta"
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.roberta = RobertaModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
          output_attentions=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Labels for computing the token classification loss.
              Indices should be in ``[0, ..., config.num_labels - 1]``.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
              Classification loss.
          scores (: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 ``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 ``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.
  
      Examples::
  
          from transformers import RobertaTokenizer, RobertaForTokenClassification
          import torch
  
          tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
          model = RobertaForTokenClassification.from_pretrained('roberta-base')
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
          loss, scores = outputs[:2]
  
          """
  
          outputs = self.roberta(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
          )
  
          sequence_output = outputs[0]
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
  
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              # Only keep active parts of the loss
              if attention_mask is not None:
                  active_loss = attention_mask.view(-1) == 1
                  active_logits = logits.view(-1, self.num_labels)
                  active_labels = torch.where(
                      active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
                  )
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), scores, (hidden_states), (attentions)
  
  
  class RobertaClassificationHead(nn.Module):
      """Head for sentence-level classification tasks."""
  
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
  
      def forward(self, features, **kwargs):
          x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
          x = self.dropout(x)
          x = self.dense(x)
          x = torch.tanh(x)
          x = self.dropout(x)
          x = self.out_proj(x)
          return x
  
  
  @add_start_docstrings(
      """Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
      the hidden-states output to compute `span start logits` and `span end logits`). """,
      ROBERTA_START_DOCSTRING,
  )
  class RobertaForQuestionAnswering(BertPreTrainedModel):
      config_class = RobertaConfig
      base_model_prefix = "roberta"
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.roberta = RobertaModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          start_positions=None,
          end_positions=None,
          output_attentions=None,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              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`, defaults to :obj:`None`):
              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.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
          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_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-start scores (before SoftMax).
          end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-end scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned 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 ``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.
  
      Examples::
  
          # The checkpoint roberta-large is not fine-tuned for question answering. Please see the
          # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
  
          from transformers import RobertaTokenizer, RobertaForQuestionAnswering
          import torch
  
          tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
          model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
  
          question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
          input_ids = tokenizer.encode(question, text)
          start_scores, end_scores = model(torch.tensor([input_ids]))
  
          all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
          answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
  
          """
  
          outputs = self.roberta(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
          )
  
          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)
  
          outputs = (start_logits, end_logits,) + outputs[2:]
          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
              outputs = (total_loss,) + outputs
  
          return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)
  