code: # 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 math
  
  import torch
  import torch.utils.checkpoint
  from packaging import version
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  
  from ...activations import ACT2FN, gelu
  from ...file_utils import (
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import (
      BaseModelOutputWithPastAndCrossAttentions,
      BaseModelOutputWithPoolingAndCrossAttentions,
      CausalLMOutputWithCrossAttentions,
      MaskedLMOutput,
      MultipleChoiceModelOutput,
      QuestionAnsweringModelOutput,
      SequenceClassifierOutput,
      TokenClassifierOutput,
  )
  from ...modeling_utils import (
      PreTrainedModel,
      apply_chunking_to_forward,
      find_pruneable_heads_and_indices,
      prune_linear_layer,
  )
  from ...utils import logging
  from .configuration_roberta import RobertaConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "roberta-base"
  _CONFIG_FOR_DOC = "RobertaConfig"
  _TOKENIZER_FOR_DOC = "RobertaTokenizer"
  
  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(nn.Module):
      """
      Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
      """
  
      # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
      def __init__(self, config):
          super().__init__()
          self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
          self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
          self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
  
          # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
          # any TensorFlow checkpoint file
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          # position_ids (1, len position emb) is contiguous in memory and exported when serialized
          self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
          self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
          if version.parse(torch.__version__) > version.parse("1.6.0"):
              self.register_buffer(
                  "token_type_ids",
                  torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
                  persistent=False,
              )
  
          # End copy
          self.padding_idx = config.pad_token_id
          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, past_key_values_length=0
      ):
          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, past_key_values_length)
              else:
                  position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
  
          if input_ids is not None:
              input_shape = input_ids.size()
          else:
              input_shape = inputs_embeds.size()[:-1]
  
          seq_length = input_shape[1]
  
          # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
          # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
          # issue #5664
          if token_type_ids is None:
              if hasattr(self, "token_type_ids"):
                  buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                  buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                  token_type_ids = buffered_token_type_ids_expanded
              else:
                  token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
  
          if inputs_embeds is None:
              inputs_embeds = self.word_embeddings(input_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = inputs_embeds + token_type_embeddings
          if self.position_embedding_type == "absolute":
              position_embeddings = self.position_embeddings(position_ids)
              embeddings += position_embeddings
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
      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.
  
          Args:
              inputs_embeds: torch.Tensor
  
          Returns: 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)
  
  
  # Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta
  class RobertaSelfAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
              raise ValueError(
                  f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                  f"heads ({config.num_attention_heads})"
              )
  
          self.num_attention_heads = config.num_attention_heads
          self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
  
          self.query = nn.Linear(config.hidden_size, self.all_head_size)
          self.key = nn.Linear(config.hidden_size, self.all_head_size)
          self.value = nn.Linear(config.hidden_size, self.all_head_size)
  
          self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
          self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
          if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
              self.max_position_embeddings = config.max_position_embeddings
              self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
  
          self.is_decoder = config.is_decoder
  
      def transpose_for_scores(self, x):
          new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
          x = x.view(*new_x_shape)
          return x.permute(0, 2, 1, 3)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
      ):
          mixed_query_layer = self.query(hidden_states)
  
          # If this is instantiated as a cross-attention module, the keys
          # and values come from an encoder; the attention mask needs to be
          # such that the encoder's padding tokens are not attended to.
          is_cross_attention = encoder_hidden_states is not None
  
          if is_cross_attention and past_key_value is not None:
              # reuse k,v, cross_attentions
              key_layer = past_key_value[0]
              value_layer = past_key_value[1]
              attention_mask = encoder_attention_mask
          elif is_cross_attention:
              key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
              value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
              attention_mask = encoder_attention_mask
          elif past_key_value is not None:
              key_layer = self.transpose_for_scores(self.key(hidden_states))
              value_layer = self.transpose_for_scores(self.value(hidden_states))
              key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
              value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
          else:
              key_layer = self.transpose_for_scores(self.key(hidden_states))
              value_layer = self.transpose_for_scores(self.value(hidden_states))
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
  
          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_layer, value_layer)
  
          # Take the dot product between "query" and "key" to get the raw attention scores.
          attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
  
          if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
              seq_length = hidden_states.size()[1]
              position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
              position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
              distance = position_ids_l - position_ids_r
              positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
              positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
  
              if self.position_embedding_type == "relative_key":
                  relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                  attention_scores = attention_scores + relative_position_scores
              elif self.position_embedding_type == "relative_key_query":
                  relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                  relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                  attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
  
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
          if attention_mask is not None:
              # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
              attention_scores = attention_scores + attention_mask
  
          # Normalize the attention scores to probabilities.
          attention_probs = nn.Softmax(dim=-1)(attention_scores)
  
          # This is actually dropping out entire tokens to attend to, which might
          # seem a bit unusual, but is taken from the original Transformer paper.
          attention_probs = self.dropout(attention_probs)
  
          # Mask heads if we want to
          if head_mask is not None:
              attention_probs = attention_probs * head_mask
  
          context_layer = torch.matmul(attention_probs, value_layer)
  
          context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
          new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
          context_layer = context_layer.view(*new_context_layer_shape)
  
          outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
  
          if self.is_decoder:
              outputs = outputs + (past_key_value,)
          return outputs
  
  
  # Copied from transformers.models.bert.modeling_bert.BertSelfOutput
  class RobertaSelfOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta
  class RobertaAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.self = RobertaSelfAttention(config)
          self.output = RobertaSelfOutput(config)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          if len(heads) == 0:
              return
          heads, index = find_pruneable_heads_and_indices(
              heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
          )
  
          # Prune linear layers
          self.self.query = prune_linear_layer(self.self.query, index)
          self.self.key = prune_linear_layer(self.self.key, index)
          self.self.value = prune_linear_layer(self.self.value, index)
          self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
  
          # Update hyper params and store pruned heads
          self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
          self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
      ):
          self_outputs = self.self(
              hidden_states,
              attention_mask,
              head_mask,
              encoder_hidden_states,
              encoder_attention_mask,
              past_key_value,
              output_attentions,
          )
          attention_output = self.output(self_outputs[0], hidden_states)
          outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
          return outputs
  
  
  # Copied from transformers.models.bert.modeling_bert.BertIntermediate
  class RobertaIntermediate(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
          if isinstance(config.hidden_act, str):
              self.intermediate_act_fn = ACT2FN[config.hidden_act]
          else:
              self.intermediate_act_fn = config.hidden_act
  
      def forward(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.intermediate_act_fn(hidden_states)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertOutput
  class RobertaOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  # Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta
  class RobertaLayer(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.chunk_size_feed_forward = config.chunk_size_feed_forward
          self.seq_len_dim = 1
          self.attention = RobertaAttention(config)
          self.is_decoder = config.is_decoder
          self.add_cross_attention = config.add_cross_attention
          if self.add_cross_attention:
              assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
              self.crossattention = RobertaAttention(config)
          self.intermediate = RobertaIntermediate(config)
          self.output = RobertaOutput(config)
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_value=None,
          output_attentions=False,
      ):
          # 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
          self_attention_outputs = self.attention(
              hidden_states,
              attention_mask,
              head_mask,
              output_attentions=output_attentions,
              past_key_value=self_attn_past_key_value,
          )
          attention_output = self_attention_outputs[0]
  
          # if decoder, the last output is tuple of self-attn cache
          if self.is_decoder:
              outputs = self_attention_outputs[1:-1]
              present_key_value = self_attention_outputs[-1]
          else:
              outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
  
          cross_attn_present_key_value = None
          if self.is_decoder and encoder_hidden_states is not None:
              assert hasattr(
                  self, "crossattention"
              ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
  
              # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
              cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
              cross_attention_outputs = self.crossattention(
                  attention_output,
                  attention_mask,
                  head_mask,
                  encoder_hidden_states,
                  encoder_attention_mask,
                  cross_attn_past_key_value,
                  output_attentions,
              )
              attention_output = cross_attention_outputs[0]
              outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
  
              # add cross-attn cache to positions 3,4 of present_key_value tuple
              cross_attn_present_key_value = cross_attention_outputs[-1]
              present_key_value = present_key_value + cross_attn_present_key_value
  
          layer_output = apply_chunking_to_forward(
              self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
          )
          outputs = (layer_output,) + outputs
  
          # if decoder, return the attn key/values as the last output
          if self.is_decoder:
              outputs = outputs + (present_key_value,)
  
          return outputs
  
      def feed_forward_chunk(self, attention_output):
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.output(intermediate_output, attention_output)
          return layer_output
  
  
  # Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta
  class RobertaEncoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.layer = nn.ModuleList([RobertaLayer(config) for _ in range(config.num_hidden_layers)])
          self.gradient_checkpointing = False
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=False,
          output_hidden_states=False,
          return_dict=True,
      ):
          all_hidden_states = () if output_hidden_states else None
          all_self_attentions = () if output_attentions else None
          all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
  
          next_decoder_cache = () if use_cache else None
          for i, layer_module in enumerate(self.layer):
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              layer_head_mask = head_mask[i] if head_mask is not None else None
              past_key_value = past_key_values[i] if past_key_values is not None else None
  
              if self.gradient_checkpointing and self.training:
  
                  if use_cache:
                      logger.warning(
                          "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                      )
                      use_cache = False
  
                  def create_custom_forward(module):
                      def custom_forward(*inputs):
                          return module(*inputs, past_key_value, output_attentions)
  
                      return custom_forward
  
                  layer_outputs = torch.utils.checkpoint.checkpoint(
                      create_custom_forward(layer_module),
                      hidden_states,
                      attention_mask,
                      layer_head_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                  )
              else:
                  layer_outputs = layer_module(
                      hidden_states,
                      attention_mask,
                      layer_head_mask,
                      encoder_hidden_states,
                      encoder_attention_mask,
                      past_key_value,
                      output_attentions,
                  )
  
              hidden_states = layer_outputs[0]
              if use_cache:
                  next_decoder_cache += (layer_outputs[-1],)
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
                  if self.config.add_cross_attention:
                      all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
  
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(
                  v
                  for v in [
                      hidden_states,
                      next_decoder_cache,
                      all_hidden_states,
                      all_self_attentions,
                      all_cross_attentions,
                  ]
                  if v is not None
              )
          return BaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=hidden_states,
              past_key_values=next_decoder_cache,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
              cross_attentions=all_cross_attentions,
          )
  
  
  # Copied from transformers.models.bert.modeling_bert.BertPooler
  class RobertaPooler(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.activation = nn.Tanh()
  
      def forward(self, hidden_states):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
          first_token_tensor = hidden_states[:, 0]
          pooled_output = self.dense(first_token_tensor)
          pooled_output = self.activation(pooled_output)
          return pooled_output
  
  
  class RobertaPreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = RobertaConfig
      base_model_prefix = "roberta"
      supports_gradient_checkpointing = True
  
      # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
      def _init_weights(self, module):
          """Initialize the weights"""
          if isinstance(module, nn.Linear):
              # Slightly different from the TF version which uses truncated_normal for initialization
              # cf https://github.com/pytorch/pytorch/pull/5617
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.Embedding):
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
              if module.padding_idx is not None:
                  module.weight.data[module.padding_idx].zero_()
          elif isinstance(module, nn.LayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
  
      def _set_gradient_checkpointing(self, module, value=False):
          if isinstance(module, RobertaEncoder):
              module.gradient_checkpointing = value
  
      def update_keys_to_ignore(self, config, del_keys_to_ignore):
          """Remove some keys from ignore list"""
          if not config.tie_word_embeddings:
              # must make a new list, or the class variable gets modified!
              self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore]
              self._keys_to_ignore_on_load_missing = [
                  k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore
              ]
  
  
  ROBERTA_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.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
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
              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`):
              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`):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, 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.
  """
  
  
  @add_start_docstrings(
      "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
      ROBERTA_START_DOCSTRING,
  )
  class RobertaModel(RobertaPreTrainedModel):
      """
  
      The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
      cross-attention is added between the self-attention layers, following the architecture described in `Attention is
      all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
      Kaiser and Illia Polosukhin.
  
      To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration
      set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
      argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
      input to the forward pass.
  
      .. _`Attention is all you need`: https://arxiv.org/abs/1706.03762
  
      """
  
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
      def __init__(self, config, add_pooling_layer=True):
          super().__init__(config)
          self.config = config
  
          self.embeddings = RobertaEmbeddings(config)
          self.encoder = RobertaEncoder(config)
  
          self.pooler = RobertaPooler(config) if add_pooling_layer else None
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
  
      def _prune_heads(self, heads_to_prune):
          """
          Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
          class PreTrainedModel
          """
          for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(heads)
  
      @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=BaseModelOutputWithPoolingAndCrossAttentions,
          config_class=_CONFIG_FOR_DOC,
      )
      # Copied from transformers.models.bert.modeling_bert.BertModel.forward
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
              the model is configured as a decoder.
          encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
              the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 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)`.
          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 = 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
  
          if self.config.is_decoder:
              use_cache = use_cache if use_cache is not None else self.config.use_cache
          else:
              use_cache = False
  
          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()
          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")
  
          batch_size, seq_length = input_shape
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          # 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 attention_mask is None:
              attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
  
          if token_type_ids is None:
              if hasattr(self.embeddings, "token_type_ids"):
                  buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                  buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
                  token_type_ids = buffered_token_type_ids_expanded
              else:
                  token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
          # ourselves in which case we just need to make it broadcastable to all heads.
          extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
  
          # If a 2D or 3D attention mask is provided for the cross-attention
          # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
          if self.config.is_decoder and encoder_hidden_states is not None:
              encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
              encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
              if encoder_attention_mask is None:
                  encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
              encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
          else:
              encoder_extended_attention_mask = None
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
          head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
  
          embedding_output = self.embeddings(
              input_ids=input_ids,
              position_ids=position_ids,
              token_type_ids=token_type_ids,
              inputs_embeds=inputs_embeds,
              past_key_values_length=past_key_values_length,
          )
          encoder_outputs = self.encoder(
              embedding_output,
              attention_mask=extended_attention_mask,
              head_mask=head_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_extended_attention_mask,
              past_key_values=past_key_values,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          sequence_output = encoder_outputs[0]
          pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
  
          if not return_dict:
              return (sequence_output, pooled_output) + encoder_outputs[1:]
  
          return BaseModelOutputWithPoolingAndCrossAttentions(
              last_hidden_state=sequence_output,
              pooler_output=pooled_output,
              past_key_values=encoder_outputs.past_key_values,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
              cross_attentions=encoder_outputs.cross_attentions,
          )
  
  
  @add_start_docstrings(
      """RoBERTa Model with a `language modeling` head on top for CLM fine-tuning. """, ROBERTA_START_DOCSTRING
  )
  class RobertaForCausalLM(RobertaPreTrainedModel):
      _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
      _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
  
      def __init__(self, config):
          super().__init__(config)
  
          if not config.is_decoder:
              logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`")
  
          self.roberta = RobertaModel(config, add_pooling_layer=False)
          self.lm_head = RobertaLMHead(config)
  
          # The LM head weights require special treatment only when they are tied with the word embeddings
          self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.lm_head.decoder
  
      def set_output_embeddings(self, new_embeddings):
          self.lm_head.decoder = new_embeddings
  
      @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          labels=None,
          past_key_values=None,
          use_cache=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
              the model is configured as a decoder.
          encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
              the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the left-to-right language modeling loss (next word prediction). 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]``
          past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 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)`.
          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`).
  
          Returns:
  
          Example::
  
              >>> from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig
              >>> import torch
  
              >>> tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
              >>> config = RobertaConfig.from_pretrained("roberta-base")
              >>> config.is_decoder = True
              >>> model = RobertaForCausalLM.from_pretrained('roberta-base', config=config)
  
              >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
              >>> outputs = model(**inputs)
  
              >>> prediction_logits = outputs.logits
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          if labels is not None:
              use_cache = False
  
          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,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              past_key_values=past_key_values,
              use_cache=use_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
          prediction_scores = self.lm_head(sequence_output)
  
          lm_loss = None
          if labels is not None:
              # we are doing next-token prediction; shift prediction scores and input ids by one
              shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
              labels = labels[:, 1:].contiguous()
              loss_fct = CrossEntropyLoss()
              lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (prediction_scores,) + outputs[2:]
              return ((lm_loss,) + output) if lm_loss is not None else output
  
          return CausalLMOutputWithCrossAttentions(
              loss=lm_loss,
              logits=prediction_scores,
              past_key_values=outputs.past_key_values,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
              cross_attentions=outputs.cross_attentions,
          )
  
      def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
          input_shape = input_ids.shape
          # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
          if attention_mask is None:
              attention_mask = input_ids.new_ones(input_shape)
  
          # cut decoder_input_ids if past is used
          if past is not None:
              input_ids = input_ids[:, -1:]
  
          return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
  
      def _reorder_cache(self, 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("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
  class RobertaForMaskedLM(RobertaPreTrainedModel):
      _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
      _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
  
      def __init__(self, config):
          super().__init__(config)
  
          if config.is_decoder:
              logger.warning(
                  "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
                  "bi-directional self-attention."
              )
  
          self.roberta = RobertaModel(config, add_pooling_layer=False)
          self.lm_head = RobertaLMHead(config)
  
          # The LM head weights require special treatment only when they are tied with the word embeddings
          self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.lm_head.decoder
  
      def set_output_embeddings(self, new_embeddings):
          self.lm_head.decoder = new_embeddings
  
      @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=MaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
          mask="<mask>",
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          labels=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 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.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          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,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          sequence_output = outputs[0]
          prediction_scores = self.lm_head(sequence_output)
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (prediction_scores,) + outputs[2:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return MaskedLMOutput(
              loss=masked_lm_loss,
              logits=prediction_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.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 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
          self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
          self.bias = nn.Parameter(torch.zeros(config.vocab_size))
          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
  
      def _tie_weights(self):
          # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
          self.bias = self.decoder.bias
  
  
  @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(RobertaPreTrainedModel):
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
          self.config = config
  
          self.roberta = RobertaModel(config, add_pooling_layer=False)
          self.classifier = RobertaClassificationHead(config)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=SequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      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,
          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 regression loss is computed (Mean-Square loss),
              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
  
          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,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          sequence_output = outputs[0]
          logits = self.classifier(sequence_output)
  
          loss = None
          if labels is not None:
              if self.config.problem_type is None:
                  if self.num_labels == 1:
                      self.config.problem_type = "regression"
                  elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                      self.config.problem_type = "single_label_classification"
                  else:
                      self.config.problem_type = "multi_label_classification"
  
              if self.config.problem_type == "regression":
                  loss_fct = MSELoss()
                  if self.num_labels == 1:
                      loss = loss_fct(logits.squeeze(), labels.squeeze())
                  else:
                      loss = loss_fct(logits, labels)
              elif self.config.problem_type == "single_label_classification":
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              elif self.config.problem_type == "multi_label_classification":
                  loss_fct = BCEWithLogitsLoss()
                  loss = loss_fct(logits, labels)
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.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(RobertaPreTrainedModel):
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      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_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=MultipleChoiceModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      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,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
              num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
              :obj:`input_ids` above)
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
          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,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
          reshaped_logits = logits.view(-1, num_choices)
  
          loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels)
  
          if not return_dict:
              output = (reshaped_logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return MultipleChoiceModelOutput(
              loss=loss,
              logits=reshaped_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.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(RobertaPreTrainedModel):
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.roberta = RobertaModel(config, add_pooling_layer=False)
          classifier_dropout = (
              config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
          )
          self.dropout = nn.Dropout(classifier_dropout)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      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,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
              1]``.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          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,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          loss = None
          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))
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return TokenClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.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)
          classifier_dropout = (
              config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
          )
          self.dropout = nn.Dropout(classifier_dropout)
          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(RobertaPreTrainedModel):
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.roberta = RobertaModel(config, add_pooling_layer=False)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=QuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      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,
          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 (:obj:`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 (:obj:`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
  
          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,
              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).contiguous()
          end_logits = end_logits.squeeze(-1).contiguous()
  
          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 = start_positions.clamp(0, ignored_index)
              end_positions = 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[2:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return QuestionAnsweringModelOutput(
              loss=total_loss,
              start_logits=start_logits,
              end_logits=end_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
      """
      Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
      are ignored. This is modified from fairseq's `utils.make_positions`.
  
      Args:
          x: torch.Tensor x:
  
      Returns: torch.Tensor
      """
      # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
      mask = input_ids.ne(padding_idx).int()
      incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
      return incremental_indices.long() + padding_idx
   # coding=utf-8
  # Copyright 2020 Microsoft and the Hugging Face 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 DeBERTa model. """
  
  import math
  from collections.abc import Sequence
  
  import torch
  from torch import _softmax_backward_data, nn
  from torch.nn import CrossEntropyLoss
  
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_outputs import (
      BaseModelOutput,
      MaskedLMOutput,
      QuestionAnsweringModelOutput,
      SequenceClassifierOutput,
      TokenClassifierOutput,
  )
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_deberta import DebertaConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "DebertaConfig"
  _TOKENIZER_FOR_DOC = "DebertaTokenizer"
  _CHECKPOINT_FOR_DOC = "microsoft/deberta-base"
  
  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "microsoft/deberta-base",
      "microsoft/deberta-large",
      "microsoft/deberta-xlarge",
      "microsoft/deberta-base-mnli",
      "microsoft/deberta-large-mnli",
      "microsoft/deberta-xlarge-mnli",
  ]
  
  
  class ContextPooler(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
          self.dropout = StableDropout(config.pooler_dropout)
          self.config = config
  
      def forward(self, hidden_states):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
  
          context_token = hidden_states[:, 0]
          context_token = self.dropout(context_token)
          pooled_output = self.dense(context_token)
          pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
          return pooled_output
  
      @property
      def output_dim(self):
          return self.config.hidden_size
  
  
  class XSoftmax(torch.autograd.Function):
      """
      Masked Softmax which is optimized for saving memory
  
      Args:
          input (:obj:`torch.tensor`): The input tensor that will apply softmax.
          mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
          dim (int): The dimension that will apply softmax
  
      Example::
  
            >>> import torch
            >>> from transformers.models.deberta.modeling_deberta import XSoftmax
  
            >>> # Make a tensor
            >>> x = torch.randn([4,20,100])
  
            >>> # Create a mask
            >>> mask = (x>0).int()
  
            >>> y = XSoftmax.apply(x, mask, dim=-1)
      """
  
      @staticmethod
      def forward(self, input, mask, dim):
          self.dim = dim
          rmask = ~(mask.bool())
  
          output = input.masked_fill(rmask, float("-inf"))
          output = torch.softmax(output, self.dim)
          output.masked_fill_(rmask, 0)
          self.save_for_backward(output)
          return output
  
      @staticmethod
      def backward(self, grad_output):
          (output,) = self.saved_tensors
          inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)
          return inputGrad, None, None
  
  
  class DropoutContext(object):
      def __init__(self):
          self.dropout = 0
          self.mask = None
          self.scale = 1
          self.reuse_mask = True
  
  
  def get_mask(input, local_context):
      if not isinstance(local_context, DropoutContext):
          dropout = local_context
          mask = None
      else:
          dropout = local_context.dropout
          dropout *= local_context.scale
          mask = local_context.mask if local_context.reuse_mask else None
  
      if dropout > 0 and mask is None:
          mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()
  
      if isinstance(local_context, DropoutContext):
          if local_context.mask is None:
              local_context.mask = mask
  
      return mask, dropout
  
  
  class XDropout(torch.autograd.Function):
      """Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
  
      @staticmethod
      def forward(ctx, input, local_ctx):
          mask, dropout = get_mask(input, local_ctx)
          ctx.scale = 1.0 / (1 - dropout)
          if dropout > 0:
              ctx.save_for_backward(mask)
              return input.masked_fill(mask, 0) * ctx.scale
          else:
              return input
  
      @staticmethod
      def backward(ctx, grad_output):
          if ctx.scale > 1:
              (mask,) = ctx.saved_tensors
              return grad_output.masked_fill(mask, 0) * ctx.scale, None
          else:
              return grad_output, None
  
  
  class StableDropout(nn.Module):
      """
      Optimized dropout module for stabilizing the training
  
      Args:
          drop_prob (float): the dropout probabilities
      """
  
      def __init__(self, drop_prob):
          super().__init__()
          self.drop_prob = drop_prob
          self.count = 0
          self.context_stack = None
  
      def forward(self, x):
          """
          Call the module
  
          Args:
              x (:obj:`torch.tensor`): The input tensor to apply dropout
          """
          if self.training and self.drop_prob > 0:
              return XDropout.apply(x, self.get_context())
          return x
  
      def clear_context(self):
          self.count = 0
          self.context_stack = None
  
      def init_context(self, reuse_mask=True, scale=1):
          if self.context_stack is None:
              self.context_stack = []
          self.count = 0
          for c in self.context_stack:
              c.reuse_mask = reuse_mask
              c.scale = scale
  
      def get_context(self):
          if self.context_stack is not None:
              if self.count >= len(self.context_stack):
                  self.context_stack.append(DropoutContext())
              ctx = self.context_stack[self.count]
              ctx.dropout = self.drop_prob
              self.count += 1
              return ctx
          else:
              return self.drop_prob
  
  
  class DebertaLayerNorm(nn.Module):
      """LayerNorm module in the TF style (epsilon inside the square root)."""
  
      def __init__(self, size, eps=1e-12):
          super().__init__()
          self.weight = nn.Parameter(torch.ones(size))
          self.bias = nn.Parameter(torch.zeros(size))
          self.variance_epsilon = eps
  
      def forward(self, hidden_states):
          input_type = hidden_states.dtype
          hidden_states = hidden_states.float()
          mean = hidden_states.mean(-1, keepdim=True)
          variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
          hidden_states = (hidden_states - mean) / torch.sqrt(variance + self.variance_epsilon)
          hidden_states = hidden_states.to(input_type)
          y = self.weight * hidden_states + self.bias
          return y
  
  
  class DebertaSelfOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
          self.dropout = StableDropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  class DebertaAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.self = DisentangledSelfAttention(config)
          self.output = DebertaSelfOutput(config)
          self.config = config
  
      def forward(
          self,
          hidden_states,
          attention_mask,
          return_att=False,
          query_states=None,
          relative_pos=None,
          rel_embeddings=None,
      ):
          self_output = self.self(
              hidden_states,
              attention_mask,
              return_att,
              query_states=query_states,
              relative_pos=relative_pos,
              rel_embeddings=rel_embeddings,
          )
          if return_att:
              self_output, att_matrix = self_output
          if query_states is None:
              query_states = hidden_states
          attention_output = self.output(self_output, query_states)
  
          if return_att:
              return (attention_output, att_matrix)
          else:
              return attention_output
  
  
  # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Deberta
  class DebertaIntermediate(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
          if isinstance(config.hidden_act, str):
              self.intermediate_act_fn = ACT2FN[config.hidden_act]
          else:
              self.intermediate_act_fn = config.hidden_act
  
      def forward(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.intermediate_act_fn(hidden_states)
          return hidden_states
  
  
  class DebertaOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
          self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
          self.dropout = StableDropout(config.hidden_dropout_prob)
          self.config = config
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  class DebertaLayer(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.attention = DebertaAttention(config)
          self.intermediate = DebertaIntermediate(config)
          self.output = DebertaOutput(config)
  
      def forward(
          self,
          hidden_states,
          attention_mask,
          return_att=False,
          query_states=None,
          relative_pos=None,
          rel_embeddings=None,
      ):
          attention_output = self.attention(
              hidden_states,
              attention_mask,
              return_att=return_att,
              query_states=query_states,
              relative_pos=relative_pos,
              rel_embeddings=rel_embeddings,
          )
          if return_att:
              attention_output, att_matrix = attention_output
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.output(intermediate_output, attention_output)
          if return_att:
              return (layer_output, att_matrix)
          else:
              return layer_output
  
  
  class DebertaEncoder(nn.Module):
      """Modified BertEncoder with relative position bias support"""
  
      def __init__(self, config):
          super().__init__()
          self.layer = nn.ModuleList([DebertaLayer(config) for _ in range(config.num_hidden_layers)])
          self.relative_attention = getattr(config, "relative_attention", False)
          if self.relative_attention:
              self.max_relative_positions = getattr(config, "max_relative_positions", -1)
              if self.max_relative_positions < 1:
                  self.max_relative_positions = config.max_position_embeddings
              self.rel_embeddings = nn.Embedding(self.max_relative_positions * 2, config.hidden_size)
  
      def get_rel_embedding(self):
          rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
          return rel_embeddings
  
      def get_attention_mask(self, attention_mask):
          if attention_mask.dim() <= 2:
              extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
              attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
              attention_mask = attention_mask.byte()
          elif attention_mask.dim() == 3:
              attention_mask = attention_mask.unsqueeze(1)
  
          return attention_mask
  
      def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
          if self.relative_attention and relative_pos is None:
              q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)
              relative_pos = build_relative_position(q, hidden_states.size(-2), hidden_states.device)
          return relative_pos
  
      def forward(
          self,
          hidden_states,
          attention_mask,
          output_hidden_states=True,
          output_attentions=False,
          query_states=None,
          relative_pos=None,
          return_dict=True,
      ):
          attention_mask = self.get_attention_mask(attention_mask)
          relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)
  
          all_hidden_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
  
          if isinstance(hidden_states, Sequence):
              next_kv = hidden_states[0]
          else:
              next_kv = hidden_states
          rel_embeddings = self.get_rel_embedding()
          for i, layer_module in enumerate(self.layer):
  
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              hidden_states = layer_module(
                  next_kv,
                  attention_mask,
                  output_attentions,
                  query_states=query_states,
                  relative_pos=relative_pos,
                  rel_embeddings=rel_embeddings,
              )
              if output_attentions:
                  hidden_states, att_m = hidden_states
  
              if query_states is not None:
                  query_states = hidden_states
                  if isinstance(hidden_states, Sequence):
                      next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
              else:
                  next_kv = hidden_states
  
              if output_attentions:
                  all_attentions = all_attentions + (att_m,)
  
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
          return BaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
          )
  
  
  def build_relative_position(query_size, key_size, device):
      """
      Build relative position according to the query and key
  
      We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key
      :math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`R_{q \\rightarrow k} =
      P_q - P_k`
  
      Args:
          query_size (int): the length of query
          key_size (int): the length of key
  
      Return:
          :obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size]
  
      """
  
      q_ids = torch.arange(query_size, dtype=torch.long, device=device)
      k_ids = torch.arange(key_size, dtype=torch.long, device=device)
      rel_pos_ids = q_ids[:, None] - k_ids.view(1, -1).repeat(query_size, 1)
      rel_pos_ids = rel_pos_ids[:query_size, :]
      rel_pos_ids = rel_pos_ids.unsqueeze(0)
      return rel_pos_ids
  
  
  @torch.jit.script
  def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
      return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])
  
  
  @torch.jit.script
  def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
      return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])
  
  
  @torch.jit.script
  def pos_dynamic_expand(pos_index, p2c_att, key_layer):
      return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))
  
  
  class DisentangledSelfAttention(nn.Module):
      """
      Disentangled self-attention module
  
      Parameters:
          config (:obj:`str`):
              A model config class instance with the configuration to build a new model. The schema is similar to
              `BertConfig`, for more details, please refer :class:`~transformers.DebertaConfig`
  
      """
  
      def __init__(self, config):
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0:
              raise ValueError(
                  f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                  f"heads ({config.num_attention_heads})"
              )
          self.num_attention_heads = config.num_attention_heads
          self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
          self.in_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=False)
          self.q_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float))
          self.v_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float))
          self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
  
          self.relative_attention = getattr(config, "relative_attention", False)
          self.talking_head = getattr(config, "talking_head", False)
  
          if self.talking_head:
              self.head_logits_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias=False)
              self.head_weights_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias=False)
  
          if self.relative_attention:
              self.max_relative_positions = getattr(config, "max_relative_positions", -1)
              if self.max_relative_positions < 1:
                  self.max_relative_positions = config.max_position_embeddings
              self.pos_dropout = StableDropout(config.hidden_dropout_prob)
  
              if "c2p" in self.pos_att_type or "p2p" in self.pos_att_type:
                  self.pos_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
              if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
                  self.pos_q_proj = nn.Linear(config.hidden_size, self.all_head_size)
  
          self.dropout = StableDropout(config.attention_probs_dropout_prob)
  
      def transpose_for_scores(self, x):
          new_x_shape = x.size()[:-1] + (self.num_attention_heads, -1)
          x = x.view(*new_x_shape)
          return x.permute(0, 2, 1, 3)
  
      def forward(
          self,
          hidden_states,
          attention_mask,
          return_att=False,
          query_states=None,
          relative_pos=None,
          rel_embeddings=None,
      ):
          """
          Call the module
  
          Args:
              hidden_states (:obj:`torch.FloatTensor`):
                  Input states to the module usually the output from previous layer, it will be the Q,K and V in
                  `Attention(Q,K,V)`
  
              attention_mask (:obj:`torch.ByteTensor`):
                  An attention mask matrix of shape [`B`, `N`, `N`] where `B` is the batch size, `N` is the maximum
                  sequence length in which element [i,j] = `1` means the `i` th token in the input can attend to the `j`
                  th token.
  
              return_att (:obj:`bool`, optional):
                  Whether return the attention matrix.
  
              query_states (:obj:`torch.FloatTensor`, optional):
                  The `Q` state in `Attention(Q,K,V)`.
  
              relative_pos (:obj:`torch.LongTensor`):
                  The relative position encoding between the tokens in the sequence. It's of shape [`B`, `N`, `N`] with
                  values ranging in [`-max_relative_positions`, `max_relative_positions`].
  
              rel_embeddings (:obj:`torch.FloatTensor`):
                  The embedding of relative distances. It's a tensor of shape [:math:`2 \\times
                  \\text{max_relative_positions}`, `hidden_size`].
  
  
          """
          if query_states is None:
              qp = self.in_proj(hidden_states)  # .split(self.all_head_size, dim=-1)
              query_layer, key_layer, value_layer = self.transpose_for_scores(qp).chunk(3, dim=-1)
          else:
  
              def linear(w, b, x):
                  if b is not None:
                      return torch.matmul(x, w.t()) + b.t()
                  else:
                      return torch.matmul(x, w.t())  # + b.t()
  
              ws = self.in_proj.weight.chunk(self.num_attention_heads * 3, dim=0)
              qkvw = [torch.cat([ws[i * 3 + k] for i in range(self.num_attention_heads)], dim=0) for k in range(3)]
              qkvb = [None] * 3
  
              q = linear(qkvw[0], qkvb[0], query_states)
              k, v = [linear(qkvw[i], qkvb[i], hidden_states) for i in range(1, 3)]
              query_layer, key_layer, value_layer = [self.transpose_for_scores(x) for x in [q, k, v]]
  
          query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])
          value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :])
  
          rel_att = None
          # Take the dot product between "query" and "key" to get the raw attention scores.
          scale_factor = 1 + len(self.pos_att_type)
          scale = math.sqrt(query_layer.size(-1) * scale_factor)
          query_layer = query_layer / scale
          attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
          if self.relative_attention:
              rel_embeddings = self.pos_dropout(rel_embeddings)
              rel_att = self.disentangled_att_bias(query_layer, key_layer, relative_pos, rel_embeddings, scale_factor)
  
          if rel_att is not None:
              attention_scores = attention_scores + rel_att
  
          # bxhxlxd
          if self.talking_head:
              attention_scores = self.head_logits_proj(attention_scores.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
  
          attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
          attention_probs = self.dropout(attention_probs)
          if self.talking_head:
              attention_probs = self.head_weights_proj(attention_probs.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
  
          context_layer = torch.matmul(attention_probs, value_layer)
          context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
          new_context_layer_shape = context_layer.size()[:-2] + (-1,)
          context_layer = context_layer.view(*new_context_layer_shape)
          if return_att:
              return (context_layer, attention_probs)
          else:
              return context_layer
  
      def disentangled_att_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
          if relative_pos is None:
              q = query_layer.size(-2)
              relative_pos = build_relative_position(q, key_layer.size(-2), query_layer.device)
          if relative_pos.dim() == 2:
              relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
          elif relative_pos.dim() == 3:
              relative_pos = relative_pos.unsqueeze(1)
          # bxhxqxk
          elif relative_pos.dim() != 4:
              raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}")
  
          att_span = min(max(query_layer.size(-2), key_layer.size(-2)), self.max_relative_positions)
          relative_pos = relative_pos.long().to(query_layer.device)
          rel_embeddings = rel_embeddings[
              self.max_relative_positions - att_span : self.max_relative_positions + att_span, :
          ].unsqueeze(0)
          if "c2p" in self.pos_att_type or "p2p" in self.pos_att_type:
              pos_key_layer = self.pos_proj(rel_embeddings)
              pos_key_layer = self.transpose_for_scores(pos_key_layer)
  
          if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
              pos_query_layer = self.pos_q_proj(rel_embeddings)
              pos_query_layer = self.transpose_for_scores(pos_query_layer)
  
          score = 0
          # content->position
          if "c2p" in self.pos_att_type:
              c2p_att = torch.matmul(query_layer, pos_key_layer.transpose(-1, -2))
              c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
              c2p_att = torch.gather(c2p_att, dim=-1, index=c2p_dynamic_expand(c2p_pos, query_layer, relative_pos))
              score += c2p_att
  
          # position->content
          if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type:
              pos_query_layer /= math.sqrt(pos_query_layer.size(-1) * scale_factor)
              if query_layer.size(-2) != key_layer.size(-2):
                  r_pos = build_relative_position(key_layer.size(-2), key_layer.size(-2), query_layer.device)
              else:
                  r_pos = relative_pos
              p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
              if query_layer.size(-2) != key_layer.size(-2):
                  pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)
  
          if "p2c" in self.pos_att_type:
              p2c_att = torch.matmul(key_layer, pos_query_layer.transpose(-1, -2))
              p2c_att = torch.gather(
                  p2c_att, dim=-1, index=p2c_dynamic_expand(p2c_pos, query_layer, key_layer)
              ).transpose(-1, -2)
              if query_layer.size(-2) != key_layer.size(-2):
                  p2c_att = torch.gather(p2c_att, dim=-2, index=pos_dynamic_expand(pos_index, p2c_att, key_layer))
              score += p2c_att
  
          return score
  
  
  class DebertaEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      def __init__(self, config):
          super().__init__()
          pad_token_id = getattr(config, "pad_token_id", 0)
          self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
          self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)
  
          self.position_biased_input = getattr(config, "position_biased_input", True)
          if not self.position_biased_input:
              self.position_embeddings = None
          else:
              self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)
  
          if config.type_vocab_size > 0:
              self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)
  
          if self.embedding_size != config.hidden_size:
              self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)
          self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
          self.dropout = StableDropout(config.hidden_dropout_prob)
          self.config = config
  
          # position_ids (1, len position emb) is contiguous in memory and exported when serialized
          self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
  
      def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
          if input_ids is not None:
              input_shape = input_ids.size()
          else:
              input_shape = inputs_embeds.size()[:-1]
  
          seq_length = input_shape[1]
  
          if position_ids is None:
              position_ids = self.position_ids[:, :seq_length]
  
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
  
          if inputs_embeds is None:
              inputs_embeds = self.word_embeddings(input_ids)
  
          if self.position_embeddings is not None:
              position_embeddings = self.position_embeddings(position_ids.long())
          else:
              position_embeddings = torch.zeros_like(inputs_embeds)
  
          embeddings = inputs_embeds
          if self.position_biased_input:
              embeddings += position_embeddings
          if self.config.type_vocab_size > 0:
              token_type_embeddings = self.token_type_embeddings(token_type_ids)
              embeddings += token_type_embeddings
  
          if self.embedding_size != self.config.hidden_size:
              embeddings = self.embed_proj(embeddings)
  
          embeddings = self.LayerNorm(embeddings)
  
          if mask is not None:
              if mask.dim() != embeddings.dim():
                  if mask.dim() == 4:
                      mask = mask.squeeze(1).squeeze(1)
                  mask = mask.unsqueeze(2)
              mask = mask.to(embeddings.dtype)
  
              embeddings = embeddings * mask
  
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  class DebertaPreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = DebertaConfig
      base_model_prefix = "deberta"
      _keys_to_ignore_on_load_missing = ["position_ids"]
      _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
  
      def _init_weights(self, module):
          """Initialize the weights."""
          if isinstance(module, nn.Linear):
              # Slightly different from the TF version which uses truncated_normal for initialization
              # cf https://github.com/pytorch/pytorch/pull/5617
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.Embedding):
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
              if module.padding_idx is not None:
                  module.weight.data[module.padding_idx].zero_()
  
  
  DEBERTA_START_DOCSTRING = r"""
      The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention
      <https://arxiv.org/abs/2006.03654>`_ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build on top of
      BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
      improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.
  
      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.DebertaConfig`): 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.
  """
  
  DEBERTA_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.DebertaTokenizer`. See
              :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
              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`):
              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>`_
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, 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 `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.
  """
  
  
  @add_start_docstrings(
      "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
      DEBERTA_START_DOCSTRING,
  )
  class DebertaModel(DebertaPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.embeddings = DebertaEmbeddings(config)
          self.encoder = DebertaEncoder(config)
          self.z_steps = 0
          self.config = config
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, new_embeddings):
          self.embeddings.word_embeddings = new_embeddings
  
      def _prune_heads(self, heads_to_prune):
          """
          Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
          class PreTrainedModel
          """
          raise NotImplementedError("The prune function is not implemented in DeBERTa model.")
  
      @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=SequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          inputs_embeds=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          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
  
          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()
          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")
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          if attention_mask is None:
              attention_mask = torch.ones(input_shape, device=device)
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          embedding_output = self.embeddings(
              input_ids=input_ids,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              mask=attention_mask,
              inputs_embeds=inputs_embeds,
          )
  
          encoder_outputs = self.encoder(
              embedding_output,
              attention_mask,
              output_hidden_states=True,
              output_attentions=output_attentions,
              return_dict=return_dict,
          )
          encoded_layers = encoder_outputs[1]
  
          if self.z_steps > 1:
              hidden_states = encoded_layers[-2]
              layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
              query_states = encoded_layers[-1]
              rel_embeddings = self.encoder.get_rel_embedding()
              attention_mask = self.encoder.get_attention_mask(attention_mask)
              rel_pos = self.encoder.get_rel_pos(embedding_output)
              for layer in layers[1:]:
                  query_states = layer(
                      hidden_states,
                      attention_mask,
                      return_att=False,
                      query_states=query_states,
                      relative_pos=rel_pos,
                      rel_embeddings=rel_embeddings,
                  )
                  encoded_layers.append(query_states)
  
          sequence_output = encoded_layers[-1]
  
          if not return_dict:
              return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]
  
          return BaseModelOutput(
              last_hidden_state=sequence_output,
              hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
              attentions=encoder_outputs.attentions,
          )
  
  
  @add_start_docstrings("""DeBERTa Model with a `language modeling` head on top. """, DEBERTA_START_DOCSTRING)
  class DebertaForMaskedLM(DebertaPreTrainedModel):
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
      _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
  
      def __init__(self, config):
          super().__init__(config)
  
          self.deberta = DebertaModel(config)
          self.cls = DebertaOnlyMLMHead(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.cls.predictions.decoder
  
      def set_output_embeddings(self, new_embeddings):
          self.cls.predictions.decoder = new_embeddings
  
      @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=MaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          inputs_embeds=None,
          labels=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 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]``
          """
  
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.deberta(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
          prediction_scores = self.cls(sequence_output)
  
          masked_lm_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()  # -100 index = padding token
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
  
          if not return_dict:
              output = (prediction_scores,) + outputs[1:]
              return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
  
          return MaskedLMOutput(
              loss=masked_lm_loss,
              logits=prediction_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  # copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta
  class DebertaPredictionHeadTransform(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          if isinstance(config.hidden_act, str):
              self.transform_act_fn = ACT2FN[config.hidden_act]
          else:
              self.transform_act_fn = config.hidden_act
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
      def forward(self, hidden_states):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.transform_act_fn(hidden_states)
          hidden_states = self.LayerNorm(hidden_states)
          return hidden_states
  
  
  # copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta
  class DebertaLMPredictionHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.transform = DebertaPredictionHeadTransform(config)
  
          # The output weights are the same as the input embeddings, but there is
          # an output-only bias for each token.
          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, hidden_states):
          hidden_states = self.transform(hidden_states)
          hidden_states = self.decoder(hidden_states)
          return hidden_states
  
  
  # copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta
  class DebertaOnlyMLMHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.predictions = DebertaLMPredictionHead(config)
  
      def forward(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
          return prediction_scores
  
  
  @add_start_docstrings(
      """
      DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
      pooled output) e.g. for GLUE tasks.
      """,
      DEBERTA_START_DOCSTRING,
  )
  class DebertaForSequenceClassification(DebertaPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          num_labels = getattr(config, "num_labels", 2)
          self.num_labels = num_labels
  
          self.deberta = DebertaModel(config)
          self.pooler = ContextPooler(config)
          output_dim = self.pooler.output_dim
  
          self.classifier = nn.Linear(output_dim, num_labels)
          drop_out = getattr(config, "cls_dropout", None)
          drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
          self.dropout = StableDropout(drop_out)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.deberta.get_input_embeddings()
  
      def set_input_embeddings(self, new_embeddings):
          self.deberta.set_input_embeddings(new_embeddings)
  
      @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=SequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          inputs_embeds=None,
          labels=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 regression loss is computed (Mean-Square loss),
              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
  
          outputs = self.deberta(
              input_ids,
              token_type_ids=token_type_ids,
              attention_mask=attention_mask,
              position_ids=position_ids,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          encoder_layer = outputs[0]
          pooled_output = self.pooler(encoder_layer)
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
  
          loss = None
          if labels is not None:
              if self.num_labels == 1:
                  # regression task
                  loss_fn = nn.MSELoss()
                  logits = logits.view(-1).to(labels.dtype)
                  loss = loss_fn(logits, labels.view(-1))
              elif labels.dim() == 1 or labels.size(-1) == 1:
                  label_index = (labels >= 0).nonzero()
                  labels = labels.long()
                  if label_index.size(0) > 0:
                      labeled_logits = torch.gather(logits, 0, label_index.expand(label_index.size(0), logits.size(1)))
                      labels = torch.gather(labels, 0, label_index.view(-1))
                      loss_fct = CrossEntropyLoss()
                      loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
                  else:
                      loss = torch.tensor(0).to(logits)
              else:
                  log_softmax = nn.LogSoftmax(-1)
                  loss = -((log_softmax(logits) * labels).sum(-1)).mean()
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
          else:
              return SequenceClassifierOutput(
                  loss=loss,
                  logits=logits,
                  hidden_states=outputs.hidden_states,
                  attentions=outputs.attentions,
              )
  
  
  @add_start_docstrings(
      """
      DeBERTa 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.
      """,
      DEBERTA_START_DOCSTRING,
  )
  class DebertaForTokenClassification(DebertaPreTrainedModel):
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.deberta = DebertaModel(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_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          inputs_embeds=None,
          labels=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 token classification loss. Indices should be in ``[0, ..., config.num_labels -
              1]``.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.deberta(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          sequence_output = outputs[0]
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          loss = None
          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))
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return TokenClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      DeBERTa 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`).
      """,
      DEBERTA_START_DOCSTRING,
  )
  class DebertaForQuestionAnswering(DebertaPreTrainedModel):
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.deberta = DebertaModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          tokenizer_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=QuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          inputs_embeds=None,
          start_positions=None,
          end_positions=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 (:obj:`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 (:obj:`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
  
          outputs = self.deberta(
              input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              inputs_embeds=inputs_embeds,
              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).contiguous()
          end_logits = end_logits.squeeze(-1).contiguous()
  
          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 = start_positions.clamp(0, ignored_index)
              end_positions = 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 QuestionAnsweringModelOutput(
              loss=total_loss,
              start_logits=start_logits,
              end_logits=end_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
apis: torch.ones torch.nn.Linear torch.nn.MSELoss torch.nn.Softmax torch.tensor torch.nn.Embedding torch.nn.Tanh torch.nn.CrossEntropyLoss torch.cumsum torch.nn.LayerNorm torch.arange torch.tanh torch.nn.BCEWithLogitsLoss torch.zeros torch.einsum torch.cat torch.nn.Dropout torch.matmul
apis: torch.ones torch.nn.Linear torch.nn.MSELoss torch.nn.Dropout torch.clamp torch.empty_like torch.zeros_like torch.sqrt torch.nn.Embedding torch.nn.CrossEntropyLoss torch.nn.LogSoftmax torch.tensor torch.arange torch._softmax_backward_data torch.nn.LayerNorm torch.zeros torch.softmax torch.matmul