text: # coding=utf-8
  # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
  #
  # 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 DistilBERT model adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM) and in
   part from HuggingFace PyTorch version of Google AI Bert model (https://github.com/google-research/bert)
  """
  
  
  import math
  from typing import Dict, List, Optional, Set, Tuple, Union
  
  import numpy as np
  import torch
  from packaging import version
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  
  from transformers.configuration_utils import PretrainedConfig
  
  from ...activations import get_activation
  from ...adapters.composition import adjust_tensors_for_parallel
  from ...adapters.context import ForwardContext
  from ...adapters.lora import Linear as LoRALinear
  from ...adapters.mixins.distilbert import DistilBertModelAdaptersMixin, DistilBertTransfomerBlockAdaptersMixin
  from ...adapters.model_mixin import ModelWithHeadsAdaptersMixin
  from ...adapters.prefix_tuning import PrefixTuningShim
  from ...deepspeed import is_deepspeed_zero3_enabled
  from ...modeling_outputs import (
      BaseModelOutput,
      MaskedLMOutput,
      MultipleChoiceModelOutput,
      QuestionAnsweringModelOutput,
      SequenceClassifierOutput,
      TokenClassifierOutput,
  )
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import (
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      logging,
      replace_return_docstrings,
  )
  from .configuration_distilbert import DistilBertConfig
  
  
  logger = logging.get_logger(__name__)
  _CHECKPOINT_FOR_DOC = "distilbert-base-uncased"
  _CONFIG_FOR_DOC = "DistilBertConfig"
  _TOKENIZER_FOR_DOC = "DistilBertTokenizer"
  
  DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "distilbert-base-uncased",
      "distilbert-base-uncased-distilled-squad",
      "distilbert-base-cased",
      "distilbert-base-cased-distilled-squad",
      "distilbert-base-german-cased",
      "distilbert-base-multilingual-cased",
      "distilbert-base-uncased-finetuned-sst-2-english",
      # See all DistilBERT models at https://huggingface.co/models?filter=distilbert
  ]
  
  
  # UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE #
  
  
  def create_sinusoidal_embeddings(n_pos: int, dim: int, out: torch.Tensor):
      if is_deepspeed_zero3_enabled():
          import deepspeed
  
          with deepspeed.zero.GatheredParameters(out, modifier_rank=0):
              if torch.distributed.get_rank() == 0:
                  _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out)
      else:
          _create_sinusoidal_embeddings(n_pos=n_pos, dim=dim, out=out)
  
  
  def _create_sinusoidal_embeddings(n_pos: int, dim: int, out: torch.Tensor):
      position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
      out.requires_grad = False
      out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
      out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
      out.detach_()
  
  
  class Embeddings(nn.Module):
      def __init__(self, config: PretrainedConfig):
          super().__init__()
          self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=config.pad_token_id)
          self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
          if config.sinusoidal_pos_embds:
              create_sinusoidal_embeddings(
                  n_pos=config.max_position_embeddings, dim=config.dim, out=self.position_embeddings.weight
              )
  
          self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12)
          self.dropout = nn.Dropout(config.dropout)
          if version.parse(torch.__version__) > version.parse("1.6.0"):
              self.register_buffer(
                  "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
              )
  
      def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
          """
          Parameters:
              input_ids: torch.tensor(bs, max_seq_length) The token ids to embed.
  
          Returns: torch.tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type
          embeddings)
          """
          seq_length = input_ids.size(1)
  
          # Setting the position-ids to the registered buffer in constructor, it helps
          # when tracing the model without passing position-ids, solves
          # isues similar to issue #5664
          if hasattr(self, "position_ids"):
              position_ids = self.position_ids[:, :seq_length]
          else:
              position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)  # (max_seq_length)
              position_ids = position_ids.unsqueeze(0).expand_as(input_ids)  # (bs, max_seq_length)
  
          word_embeddings = self.word_embeddings(input_ids)  # (bs, max_seq_length, dim)
          position_embeddings = self.position_embeddings(position_ids)  # (bs, max_seq_length, dim)
  
          embeddings = word_embeddings + position_embeddings  # (bs, max_seq_length, dim)
          embeddings = self.LayerNorm(embeddings)  # (bs, max_seq_length, dim)
          embeddings = self.dropout(embeddings)  # (bs, max_seq_length, dim)
          return embeddings
  
  
  class MultiHeadSelfAttention(nn.Module):
      def __init__(self, config: PretrainedConfig):
          super().__init__()
  
          self.n_heads = config.n_heads
          self.dim = config.dim
          self.dropout = nn.Dropout(p=config.attention_dropout)
  
          assert self.dim % self.n_heads == 0
  
          self.q_lin = LoRALinear(config.dim, config.dim, "selfattn", config)
          self.k_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
          self.v_lin = LoRALinear(config.dim, config.dim, "selfattn", config)
          self.out_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
  
          self.pruned_heads: Set[int] = set()
  
          self.prefix_tuning = PrefixTuningShim("self", config)
  
      def prune_heads(self, heads: List[int]):
          attention_head_size = self.dim // self.n_heads
          if len(heads) == 0:
              return
          heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, attention_head_size, self.pruned_heads)
          # Prune linear layers
          self.q_lin = prune_linear_layer(self.q_lin, index)
          self.k_lin = prune_linear_layer(self.k_lin, index)
          self.v_lin = prune_linear_layer(self.v_lin, index)
          self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
          # Update hyper params
          self.n_heads = self.n_heads - len(heads)
          self.dim = attention_head_size * self.n_heads
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def forward(
          self,
          query: torch.Tensor,
          key: torch.Tensor,
          value: torch.Tensor,
          mask: torch.Tensor,
          head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Tuple[torch.Tensor, ...]:
          """
          Parameters:
              query: torch.tensor(bs, seq_length, dim)
              key: torch.tensor(bs, seq_length, dim)
              value: torch.tensor(bs, seq_length, dim)
              mask: torch.tensor(bs, seq_length)
  
          Returns:
              weights: torch.tensor(bs, n_heads, seq_length, seq_length) Attention weights context: torch.tensor(bs,
              seq_length, dim) Contextualized layer. Optional: only if `output_attentions=True`
          """
          bs, q_length, dim = query.size()
          # assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured'
          # assert key.size() == value.size()
  
          dim_per_head = self.dim // self.n_heads
  
          def shape(x: torch.Tensor) -> torch.Tensor:
              """separate heads"""
              return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
  
          def unshape(x: torch.Tensor) -> torch.Tensor:
              """group heads"""
              return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
  
          q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)
          k = shape(self.k_lin(key))  # (bs, n_heads, k_length, dim_per_head)
          v = shape(self.v_lin(value))  # (bs, n_heads, k_length, dim_per_head)
  
          k, v, mask = self.prefix_tuning(k, v, mask, invert_mask=False)
  
          mask_reshp = (bs, 1, 1, k.size(2))
  
          q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
          scores = torch.matmul(q, k.transpose(2, 3))  # (bs, n_heads, q_length, k_length)
          mask = (mask == 0).view(mask_reshp).expand_as(scores)  # (bs, n_heads, q_length, k_length)
          scores = scores.masked_fill(mask, -float("inf"))  # (bs, n_heads, q_length, k_length)
  
          weights = nn.functional.softmax(scores, dim=-1)  # (bs, n_heads, q_length, k_length)
          weights = self.dropout(weights)  # (bs, n_heads, q_length, k_length)
  
          # Mask heads if we want to
          if head_mask is not None:
              weights = weights * head_mask
  
          context = torch.matmul(weights, v)  # (bs, n_heads, q_length, dim_per_head)
          context = unshape(context)  # (bs, q_length, dim)
          context = self.out_lin(context)  # (bs, q_length, dim)
  
          if output_attentions:
              return (context, weights)
          else:
              return (context,)
  
  
  class FFN(nn.Module):
      def __init__(self, config: PretrainedConfig):
          super().__init__()
          self.dropout = nn.Dropout(p=config.dropout)
          self.chunk_size_feed_forward = config.chunk_size_feed_forward
          self.seq_len_dim = 1
          self.lin1 = LoRALinear(config.dim, config.hidden_dim, "intermediate", config)
          self.lin2 = LoRALinear(config.hidden_dim, config.dim, "output", config)
          self.activation = get_activation(config.activation)
  
      def forward(self, input: torch.Tensor) -> torch.Tensor:
          return apply_chunking_to_forward(self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, input)
  
      def ff_chunk(self, input: torch.Tensor) -> torch.Tensor:
          x = self.lin1(input)
          x = self.activation(x)
          x = self.lin2(x)
          x = self.dropout(x)
          return x
  
  
  class TransformerBlock(DistilBertTransfomerBlockAdaptersMixin, nn.Module):
      def __init__(self, config: PretrainedConfig):
          super().__init__()
          self.config = config
  
          assert config.dim % config.n_heads == 0
  
          self.attention = MultiHeadSelfAttention(config)
          self.sa_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
  
          self.ffn = FFN(config)
          self.output_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
  
          self._init_adapter_modules()
  
      def forward(
          self,
          x: torch.Tensor,
          attn_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Tuple[torch.Tensor, ...]:
          """
          Parameters:
              x: torch.tensor(bs, seq_length, dim)
              attn_mask: torch.tensor(bs, seq_length)
  
          Returns:
              sa_weights: torch.tensor(bs, n_heads, seq_length, seq_length) The attention weights ffn_output:
              torch.tensor(bs, seq_length, dim) The output of the transformer block contextualization.
          """
          # Self-Attention
          sa_output = self.attention(
              query=x,
              key=x,
              value=x,
              mask=attn_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
          )
          if output_attentions:
              sa_output, sa_weights = sa_output  # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
          else:  # To handle these `output_attentions` or `output_hidden_states` cases returning tuples
              assert type(sa_output) == tuple
              sa_output = sa_output[0]
          sa_output = self.attention_adapters(sa_output, x, self.sa_layer_norm)  # (bs, seq_length, dim)
  
          # Feed Forward Network
          ffn_output = self.ffn(sa_output)  # (bs, seq_length, dim)
          ffn_output: torch.Tensor = self.output_adapters(
              ffn_output, sa_output, self.output_layer_norm
          )  # (bs, seq_length, dim)
  
          output = (ffn_output,)
          if output_attentions:
              output = (sa_weights,) + output
          return output
  
  
  class Transformer(nn.Module):
      def __init__(self, config: PretrainedConfig):
          super().__init__()
          self.n_layers = config.n_layers
          self.layer = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layers)])
  
      def forward(
          self,
          x: torch.Tensor,
          attn_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: Optional[bool] = None,
      ) -> Union[BaseModelOutput, Tuple[torch.Tensor, ...]]:  # docstyle-ignore
          """
          Parameters:
              x: torch.tensor(bs, seq_length, dim) Input sequence embedded.
              attn_mask: torch.tensor(bs, seq_length) Attention mask on the sequence.
  
          Returns:
              hidden_state: torch.tensor(bs, seq_length, dim) Sequence of hidden states in the last (top)
              layer all_hidden_states: Tuple[torch.tensor(bs, seq_length, dim)]
                  Tuple of length n_layers with the hidden states from each layer.
                  Optional: only if output_hidden_states=True
              all_attentions: Tuple[torch.tensor(bs, n_heads, seq_length, seq_length)]
                  Tuple of length n_layers with the attention weights from each layer
                  Optional: only if output_attentions=True
          """
          all_hidden_states = () if output_hidden_states else None
          all_attentions = () if output_attentions else None
  
          hidden_state = x
          for i, layer_module in enumerate(self.layer):
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_state,)
  
              layer_outputs = layer_module(
                  x=hidden_state, attn_mask=attn_mask, head_mask=head_mask[i], output_attentions=output_attentions
              )
              hidden_state = layer_outputs[-1]
              (attn_mask,) = adjust_tensors_for_parallel(hidden_state, attn_mask)
  
              if output_attentions:
                  assert len(layer_outputs) == 2
                  attentions = layer_outputs[0]
                  all_attentions = all_attentions + (attentions,)
              else:
                  assert len(layer_outputs) == 1
  
          # Add last layer
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_state,)
  
          if not return_dict:
              return tuple(v for v in [hidden_state, all_hidden_states, all_attentions] if v is not None)
          return BaseModelOutput(
              last_hidden_state=hidden_state, hidden_states=all_hidden_states, attentions=all_attentions
          )
  
  
  # INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
  class DistilBertPreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = DistilBertConfig
      load_tf_weights = None
      base_model_prefix = "distilbert"
  
      def _init_weights(self, module: nn.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)
  
  
  DISTILBERT_START_DOCSTRING = r"""
  
      This model inherits from [`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 ([`DistilBertConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
  """
  
  DISTILBERT_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (`torch.LongTensor` of shape `({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using [`DistilBertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`torch.FloatTensor` of shape `({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#attention-mask)
          head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(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 (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
              Optionally, instead of passing `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 (`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 (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
  """
  
  
  @add_start_docstrings(
      "The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",
      DISTILBERT_START_DOCSTRING,
  )
  class DistilBertModel(DistilBertModelAdaptersMixin, DistilBertPreTrainedModel):
      def __init__(self, config: PretrainedConfig):
          super().__init__(config)
  
          self.embeddings = Embeddings(config)  # Embeddings
          self.transformer = Transformer(config)  # Encoder
  
          self._init_adapter_modules()
  
          self.post_init()
  
      def get_position_embeddings(self) -> nn.Embedding:
          """
          Returns the position embeddings
          """
          return self.embeddings.position_embeddings
  
      def resize_position_embeddings(self, new_num_position_embeddings: int):
          """
          Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.
  
          Arguments:
              new_num_position_embeddings (`int`):
                  The number of new position embedding matrix. If position embeddings are learned, increasing the size
                  will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                  end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                  size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                  the size will remove vectors from the end.
          """
          num_position_embeds_diff = new_num_position_embeddings - self.config.max_position_embeddings
  
          # no resizing needs to be done if the length stays the same
          if num_position_embeds_diff == 0:
              return
  
          logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...")
          self.config.max_position_embeddings = new_num_position_embeddings
  
          old_position_embeddings_weight = self.embeddings.position_embeddings.weight.clone()
  
          self.embeddings.position_embeddings = nn.Embedding(self.config.max_position_embeddings, self.config.dim)
  
          if self.config.sinusoidal_pos_embds:
              create_sinusoidal_embeddings(
                  n_pos=self.config.max_position_embeddings, dim=self.config.dim, out=self.position_embeddings.weight
              )
          else:
              with torch.no_grad():
                  if num_position_embeds_diff > 0:
                      self.embeddings.position_embeddings.weight[:-num_position_embeds_diff] = nn.Parameter(
                          old_position_embeddings_weight
                      )
                  else:
                      self.embeddings.position_embeddings.weight = nn.Parameter(
                          old_position_embeddings_weight[:num_position_embeds_diff]
                      )
          # move position_embeddings to correct device
          self.embeddings.position_embeddings.to(self.device)
  
      def get_input_embeddings(self) -> nn.Embedding:
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, new_embeddings: nn.Embedding):
          self.embeddings.word_embeddings = new_embeddings
  
      def _prune_heads(self, heads_to_prune: Dict[int, List[List[int]]]):
          """
          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.transformer.layer[layer].attention.prune_heads(heads)
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices"))
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=BaseModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      @ForwardContext.wrap
      def forward(
          self,
          input_ids: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          inputs_embeds: Optional[torch.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[BaseModelOutput, Tuple[torch.Tensor, ...]]:
          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)  # (bs, seq_length)
  
          # Prepare head mask if needed
          head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
  
          if inputs_embeds is None:
              inputs_embeds = self.embeddings(input_ids)  # (bs, seq_length, dim)
          inputs_embeds = self.invertible_adapters_forward(inputs_embeds)
  
          return self.transformer(
              x=inputs_embeds,
              attn_mask=attention_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
  
  @add_start_docstrings(
      """DistilBert Model with a `masked language modeling` head on top.""",
      DISTILBERT_START_DOCSTRING,
  )
  class DistilBertForMaskedLM(ModelWithHeadsAdaptersMixin, DistilBertPreTrainedModel):
      def __init__(self, config: PretrainedConfig):
          super().__init__(config)
  
          self.activation = get_activation(config.activation)
  
          self.distilbert = DistilBertModel(config)
          self.vocab_transform = nn.Linear(config.dim, config.dim)
          self.vocab_layer_norm = nn.LayerNorm(config.dim, eps=1e-12)
          self.vocab_projector = nn.Linear(config.dim, config.vocab_size)
  
          # Initialize weights and apply final processing
          self.post_init()
  
          self.mlm_loss_fct = nn.CrossEntropyLoss()
  
      def get_position_embeddings(self) -> nn.Embedding:
          """
          Returns the position embeddings
          """
          return self.distilbert.get_position_embeddings()
  
      def resize_position_embeddings(self, new_num_position_embeddings: int):
          """
          Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.
  
          Arguments:
              new_num_position_embeddings (`int`):
                  The number of new position embedding matrix. If position embeddings are learned, increasing the size
                  will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                  end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                  size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                  the size will remove vectors from the end.
          """
          self.distilbert.resize_position_embeddings(new_num_position_embeddings)
  
      def get_output_embeddings(self) -> nn.Module:
          return self.vocab_projector
  
      def set_output_embeddings(self, new_embeddings: nn.Module):
          self.vocab_projector = new_embeddings
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices"))
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=MaskedLMOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          inputs_embeds: Optional[torch.Tensor] = None,
          labels: Optional[torch.LongTensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[MaskedLMOutput, Tuple[torch.Tensor, ...]]:
          r"""
          labels (`torch.LongTensor` of shape `(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
  
          dlbrt_output = self.distilbert(
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          hidden_states = dlbrt_output[0]  # (bs, seq_length, dim)
          prediction_logits = self.vocab_transform(hidden_states)  # (bs, seq_length, dim)
          prediction_logits = self.activation(prediction_logits)  # (bs, seq_length, dim)
          prediction_logits = self.vocab_layer_norm(prediction_logits)  # (bs, seq_length, dim)
          prediction_logits = self.distilbert.invertible_adapters_forward(prediction_logits, rev=True)
          prediction_logits = self.vocab_projector(prediction_logits)  # (bs, seq_length, vocab_size)
  
          mlm_loss = None
          if labels is not None:
              mlm_loss = self.mlm_loss_fct(prediction_logits.view(-1, prediction_logits.size(-1)), labels.view(-1))
  
          if not return_dict:
              output = (prediction_logits,) + dlbrt_output[1:]
              return ((mlm_loss,) + output) if mlm_loss is not None else output
  
          return MaskedLMOutput(
              loss=mlm_loss,
              logits=prediction_logits,
              hidden_states=dlbrt_output.hidden_states,
              attentions=dlbrt_output.attentions,
          )
  
  
  @add_start_docstrings(
      """
      DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
      pooled output) e.g. for GLUE tasks.
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class DistilBertForSequenceClassification(ModelWithHeadsAdaptersMixin, DistilBertPreTrainedModel):
      def __init__(self, config: PretrainedConfig):
          super().__init__(config)
          self.num_labels = config.num_labels
          self.config = config
  
          self.distilbert = DistilBertModel(config)
          self.pre_classifier = nn.Linear(config.dim, config.dim)
          self.classifier = nn.Linear(config.dim, config.num_labels)
          self.dropout = nn.Dropout(config.seq_classif_dropout)
  
          # Initialize weights and apply final processing
          self.post_init()
  
      def get_position_embeddings(self) -> nn.Embedding:
          """
          Returns the position embeddings
          """
          return self.distilbert.get_position_embeddings()
  
      def resize_position_embeddings(self, new_num_position_embeddings: int):
          """
          Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.
  
          Arguments:
              new_num_position_embeddings (`int`):
                  The number of new position embedding matrix. If position embeddings are learned, increasing the size
                  will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                  end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                  size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                  the size will remove vectors from the end.
          """
          self.distilbert.resize_position_embeddings(new_num_position_embeddings)
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=SequenceClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          inputs_embeds: Optional[torch.Tensor] = None,
          labels: Optional[torch.LongTensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[SequenceClassifierOutput, Tuple[torch.Tensor, ...]]:
          r"""
          labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
              config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
              `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
  
          distilbert_output = self.distilbert(
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
          pooled_output = hidden_state[:, 0]  # (bs, dim)
          pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
          pooled_output = nn.ReLU()(pooled_output)  # (bs, dim)
          pooled_output = self.dropout(pooled_output)  # (bs, dim)
          logits = self.classifier(pooled_output)  # (bs, num_labels)
  
          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,) + distilbert_output[1:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=logits,
              hidden_states=distilbert_output.hidden_states,
              attentions=distilbert_output.attentions,
          )
  
  
  @add_start_docstrings(
      """
      DistilBert 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`).
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class DistilBertForQuestionAnswering(ModelWithHeadsAdaptersMixin, DistilBertPreTrainedModel):
      def __init__(self, config: PretrainedConfig):
          super().__init__(config)
  
          self.distilbert = DistilBertModel(config)
          self.qa_outputs = nn.Linear(config.dim, config.num_labels)
          assert config.num_labels == 2
          self.dropout = nn.Dropout(config.qa_dropout)
  
          # Initialize weights and apply final processing
          self.post_init()
  
      def get_position_embeddings(self) -> nn.Embedding:
          """
          Returns the position embeddings
          """
          return self.distilbert.get_position_embeddings()
  
      def resize_position_embeddings(self, new_num_position_embeddings: int):
          """
          Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.
  
          Arguments:
              new_num_position_embeddings (`int`):
                  The number of new position embedding matrix. If position embeddings are learned, increasing the size
                  will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                  end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                  size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                  the size will remove vectors from the end.
          """
          self.distilbert.resize_position_embeddings(new_num_position_embeddings)
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices"))
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=QuestionAnsweringModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          inputs_embeds: Optional[torch.Tensor] = None,
          start_positions: Optional[torch.Tensor] = None,
          end_positions: Optional[torch.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[QuestionAnsweringModelOutput, Tuple[torch.Tensor, ...]]:
          r"""
          start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Labels for position (index) of the start of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Labels for position (index) of the end of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
              are not taken into account for computing the loss.
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          distilbert_output = self.distilbert(
              input_ids=input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          hidden_states = distilbert_output[0]  # (bs, max_query_len, dim)
  
          hidden_states = self.dropout(hidden_states)  # (bs, max_query_len, dim)
          logits = self.qa_outputs(hidden_states)  # (bs, max_query_len, 2)
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1).contiguous()  # (bs, max_query_len)
          end_logits = end_logits.squeeze(-1).contiguous()  # (bs, max_query_len)
  
          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 = nn.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) + distilbert_output[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=distilbert_output.hidden_states,
              attentions=distilbert_output.attentions,
          )
  
  
  @add_start_docstrings(
      """
      DistilBert 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.
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class DistilBertForTokenClassification(ModelWithHeadsAdaptersMixin, DistilBertPreTrainedModel):
      def __init__(self, config: PretrainedConfig):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.distilbert = DistilBertModel(config)
          self.dropout = nn.Dropout(config.dropout)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          # Initialize weights and apply final processing
          self.post_init()
  
      def get_position_embeddings(self) -> nn.Embedding:
          """
          Returns the position embeddings
          """
          return self.distilbert.get_position_embeddings()
  
      def resize_position_embeddings(self, new_num_position_embeddings: int):
          """
          Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.
  
          Arguments:
              new_num_position_embeddings (`int`):
                  The number of new position embedding matrix. If position embeddings are learned, increasing the size
                  will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                  end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                  size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                  the size will remove vectors from the end.
          """
          self.distilbert.resize_position_embeddings(new_num_position_embeddings)
  
      @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TokenClassifierOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def forward(
          self,
          input_ids: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          inputs_embeds: Optional[torch.Tensor] = None,
          labels: Optional[torch.LongTensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[TokenClassifierOutput, Tuple[torch.Tensor, ...]]:
          r"""
          labels (`torch.LongTensor` of shape `(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.distilbert(
              input_ids,
              attention_mask=attention_mask,
              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()
              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(
      """
      DistilBert 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.
      """,
      DISTILBERT_START_DOCSTRING,
  )
  class DistilBertForMultipleChoice(ModelWithHeadsAdaptersMixin, DistilBertPreTrainedModel):
      def __init__(self, config: PretrainedConfig):
          super().__init__(config)
  
          self.distilbert = DistilBertModel(config)
          self.pre_classifier = nn.Linear(config.dim, config.dim)
          self.classifier = nn.Linear(config.dim, 1)
          self.dropout = nn.Dropout(config.seq_classif_dropout)
  
          # Initialize weights and apply final processing
          self.post_init()
  
      def get_position_embeddings(self) -> nn.Embedding:
          """
          Returns the position embeddings
          """
          return self.distilbert.get_position_embeddings()
  
      def resize_position_embeddings(self, new_num_position_embeddings: int):
          """
          Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.
  
          Arguments:
              new_num_position_embeddings (`int`)
                  The number of new position embeddings. If position embeddings are learned, increasing the size will add
                  newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If
                  position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will
                  add correct vectors at the end following the position encoding algorithm, whereas reducing the size
                  will remove vectors from the end.
          """
          self.distilbert.resize_position_embeddings(new_num_position_embeddings)
  
      @add_start_docstrings_to_model_forward(
          DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
      )
      @replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_ids: Optional[torch.Tensor] = None,
          attention_mask: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          inputs_embeds: Optional[torch.Tensor] = None,
          labels: Optional[torch.LongTensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[MultipleChoiceModelOutput, Tuple[torch.Tensor, ...]]:
          r"""
          labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
              num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
              `input_ids` above)
  
          Returns:
  
          Examples:
  
          ```python
          >>> from transformers import DistilBertTokenizer, DistilBertForMultipleChoice
          >>> import torch
  
          >>> tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-cased")
          >>> model = DistilBertForMultipleChoice.from_pretrained("distilbert-base-cased")
  
          >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
          >>> choice0 = "It is eaten with a fork and a knife."
          >>> choice1 = "It is eaten while held in the hand."
          >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1
  
          >>> encoding = tokenizer([[prompt, choice0], [prompt, choice1]], return_tensors="pt", padding=True)
          >>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1
  
          >>> # the linear classifier still needs to be trained
          >>> loss = outputs.loss
          >>> logits = outputs.logits
          ```"""
          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]
  
          input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
          attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
          inputs_embeds = (
              inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
              if inputs_embeds is not None
              else None
          )
  
          outputs = self.distilbert(
              input_ids,
              attention_mask=attention_mask,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          hidden_state = outputs[0]  # (bs * num_choices, seq_len, dim)
          pooled_output = hidden_state[:, 0]  # (bs * num_choices, dim)
          pooled_output = self.pre_classifier(pooled_output)  # (bs * num_choices, dim)
          pooled_output = nn.ReLU()(pooled_output)  # (bs * num_choices, dim)
          pooled_output = self.dropout(pooled_output)  # (bs * num_choices, dim)
          logits = self.classifier(pooled_output)  # (bs * num_choices, 1)
  
          reshaped_logits = logits.view(-1, num_choices)  # (bs, 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[1:]
              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,
          )
  