text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """PyTorch BERT model. """
  
  
  import math
  import os
  import warnings
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import copy
  
  import torch
  import torch.utils.checkpoint
  from packaging import version
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  
  from transformers.activations import ACT2FN
  from transformers.file_utils import (
      ModelOutput,
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from transformers.modeling_outputs import (
      BaseModelOutputWithPastAndCrossAttentions,
      BaseModelOutputWithPoolingAndCrossAttentions,
      CausalLMOutputWithCrossAttentions,
      MaskedLMOutput,
      MultipleChoiceModelOutput,
      NextSentencePredictorOutput,
      QuestionAnsweringModelOutput,
      SequenceClassifierOutput,
      TokenClassifierOutput,
  )
  from transformers.modeling_utils import (
      PreTrainedModel,
      apply_chunking_to_forward,
      find_pruneable_heads_and_indices,
      prune_linear_layer,
  )
  from transformers.utils import logging
  from transformers.models.bert.configuration_bert import BertConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "bert-base-uncased"
  _CONFIG_FOR_DOC = "BertConfig"
  _TOKENIZER_FOR_DOC = "BertTokenizer"
  
  BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "bert-base-uncased",
      "bert-large-uncased",
      "bert-base-cased",
      "bert-large-cased",
      "bert-base-multilingual-uncased",
      "bert-base-multilingual-cased",
      "bert-base-chinese",
      "bert-base-german-cased",
      "bert-large-uncased-whole-word-masking",
      "bert-large-cased-whole-word-masking",
      "bert-large-uncased-whole-word-masking-finetuned-squad",
      "bert-large-cased-whole-word-masking-finetuned-squad",
      "bert-base-cased-finetuned-mrpc",
      "bert-base-german-dbmdz-cased",
      "bert-base-german-dbmdz-uncased",
      "cl-tohoku/bert-base-japanese",
      "cl-tohoku/bert-base-japanese-whole-word-masking",
      "cl-tohoku/bert-base-japanese-char",
      "cl-tohoku/bert-base-japanese-char-whole-word-masking",
      "TurkuNLP/bert-base-finnish-cased-v1",
      "TurkuNLP/bert-base-finnish-uncased-v1",
      "wietsedv/bert-base-dutch-cased",
      # See all BERT models at https://huggingface.co/models?filter=bert
  ]
  
  
  def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
      """Load tf checkpoints in a pytorch model."""
      try:
          import re
  
          import numpy as np
          import tensorflow as tf
      except ImportError:
          logger.error(
              "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
              "https://www.tensorflow.org/install/ for installation instructions."
          )
          raise
      tf_path = os.path.abspath(tf_checkpoint_path)
      logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
      # Load weights from TF model
      init_vars = tf.train.list_variables(tf_path)
      names = []
      arrays = []
      for name, shape in init_vars:
          logger.info(f"Loading TF weight {name} with shape {shape}")
          array = tf.train.load_variable(tf_path, name)
          names.append(name)
          arrays.append(array)
  
      for name, array in zip(names, arrays):
          name = name.split("/")
          # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
          # which are not required for using pretrained model
          if any(
              n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
              for n in name
          ):
              logger.info(f"Skipping {'/'.join(name)}")
              continue
          pointer = model
          for m_name in name:
              if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                  scope_names = re.split(r"_(\d+)", m_name)
              else:
                  scope_names = [m_name]
              if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                  pointer = getattr(pointer, "weight")
              elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                  pointer = getattr(pointer, "bias")
              elif scope_names[0] == "output_weights":
                  pointer = getattr(pointer, "weight")
              elif scope_names[0] == "squad":
                  pointer = getattr(pointer, "classifier")
              else:
                  try:
                      pointer = getattr(pointer, scope_names[0])
                  except AttributeError:
                      logger.info(f"Skipping {'/'.join(name)}")
                      continue
              if len(scope_names) >= 2:
                  num = int(scope_names[1])
                  pointer = pointer[num]
          if m_name[-11:] == "_embeddings":
              pointer = getattr(pointer, "weight")
          elif m_name == "kernel":
              array = np.transpose(array)
          try:
              assert (
                  pointer.shape == array.shape
              ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
          except AssertionError as e:
              e.args += (pointer.shape, array.shape)
              raise
          logger.info(f"Initialize PyTorch weight {name}")
          pointer.data = torch.from_numpy(array)
      return model
  
  
  class BertEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings."""
  
      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,
              )
  
      def forward(
          self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
      ):
          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[:, past_key_values_length : seq_length + past_key_values_length]
  
          # 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
  
  
  class BertSelfAttention(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 BertModel 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
  
  
  class BertSelfOutput(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
  
  
  class BertAttention(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.self = BertSelfAttention(config)
          self.output = BertSelfOutput(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
  
  
  class BertIntermediate(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 BertOutput(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
  
  
  class BertLayer(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 = BertAttention(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 = BertAttention(config)
          self.intermediate = BertIntermediate(config)
          self.output = BertOutput(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
  
  
  class BertEncoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.layer = nn.ModuleList([BertLayer(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,
          )
  
  class BertPooler(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 BertPredictionHeadTransform(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
  
  
  class BertLMPredictionHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.transform = BertPredictionHeadTransform(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
  
  
  class BertOnlyMLMHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.predictions = BertLMPredictionHead(config)
  
      def forward(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
          return prediction_scores
  
  
  class BertOnlyNSPHead(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.seq_relationship = nn.Linear(config.hidden_size, 2)
  
      def forward(self, pooled_output):
          seq_relationship_score = self.seq_relationship(pooled_output)
          return seq_relationship_score
  
  
  class BertPreTrainingHeads(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.predictions = BertLMPredictionHead(config)
          self.seq_relationship = nn.Linear(config.hidden_size, 2)
  
      def forward(self, sequence_output, pooled_output):
          prediction_scores = self.predictions(sequence_output)
          seq_relationship_score = self.seq_relationship(pooled_output)
          return prediction_scores, seq_relationship_score
  
  
  class BertPreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = BertConfig
      load_tf_weights = load_tf_weights_in_bert
      base_model_prefix = "bert"
      supports_gradient_checkpointing = True
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      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, BertEncoder):
              module.gradient_checkpointing = value
  
  
  
  
  @dataclass
  class BertForPreTrainingOutput(ModelOutput):
      """
      Output type of :class:`~transformers.BertForPreTraining`.
  
      Args:
          loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
              Total loss as the sum of the masked language modeling loss and the next sequence prediction
              (classification) loss.
          prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
              Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
              before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[torch.FloatTensor] = None
      prediction_logits: torch.FloatTensor = None
      seq_relationship_logits: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
  
  
  
  BERT_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.BertConfig`): 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.
  """
  
  BERT_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.BertTokenizer`. 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 Bert Model transformer outputting raw hidden-states without any specific head on top.",
      BERT_START_DOCSTRING,
  )
  class BertModel(BertPreTrainedModel):
      """
  
      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 <https://arxiv.org/abs/1706.03762>`__ 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.
      """
  
      def __init__(self, config, add_pooling_layer=True):
          super().__init__(config)
          self.config = config
  
          self.embeddings = BertEmbeddings(config)
          self.encoder = BertEncoder(config)
  
          self.pooler = BertPooler(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(BERT_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,
      )
      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(
      """
      Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
      sentence prediction (classification)` head.
      """,
      BERT_START_DOCSTRING,
  )
  class BertForPreTraining(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.bert = BertModel(config)
          self.cls = BertPreTrainingHeads(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(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=BertForPreTrainingOutput, 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,
          next_sentence_label=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          labels (:obj:`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]``
          next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
              Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
              (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
  
              - 0 indicates sequence B is a continuation of sequence A,
              - 1 indicates sequence B is a random sequence.
          kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
              Used to hide legacy arguments that have been deprecated.
  
          Returns:
  
          Example::
  
              >>> from transformers import BertTokenizer, BertForPreTraining
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = BertForPreTraining.from_pretrained('bert-base-uncased')
  
              >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
              >>> outputs = model(**inputs)
  
              >>> prediction_logits = outputs.prediction_logits
              >>> seq_relationship_logits = outputs.seq_relationship_logits
          """
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.bert(
              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, pooled_output = outputs[:2]
          prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
  
          total_loss = None
          if labels is not None and next_sentence_label is not None:
              loss_fct = CrossEntropyLoss()
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
              next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
              total_loss = masked_lm_loss + next_sentence_loss
  
          if not return_dict:
              output = (prediction_scores, seq_relationship_score) + outputs[2:]
              return ((total_loss,) + output) if total_loss is not None else output
  
          return BertForPreTrainingOutput(
              loss=total_loss,
              prediction_logits=prediction_scores,
              seq_relationship_logits=seq_relationship_score,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  
  
  
  @add_start_docstrings(
      """Bert Model with a `language modeling` head on top for CLM fine-tuning. """, BERT_START_DOCSTRING
  )
  class BertLMHeadModel(BertPreTrainedModel):
  
      _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)
  
          if not config.is_decoder:
              logger.warning("If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`")
  
          self.bert = BertModel(config, add_pooling_layer=False)
          self.cls = BertOnlyMLMHead(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(BERT_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 n ``[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 BertTokenizer, BertLMHeadModel, BertConfig
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
              >>> config = BertConfig.from_pretrained("bert-base-cased")
              >>> config.is_decoder = True
              >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', 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.bert(
              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.cls(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("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
  class BertForMaskedLM(BertPreTrainedModel):
  
      _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)
  
          if config.is_decoder:
              logger.warning(
                  "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
                  "bi-directional self-attention."
              )
  
          self.bert = BertModel(config, add_pooling_layer=False)
          self.cls = BertOnlyMLMHead(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(BERT_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,
          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]``
          """
  
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.bert(
              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.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[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,
          )
  
  
      def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
          input_shape = input_ids.shape
          effective_batch_size = input_shape[0]
  
          #  add a dummy token
          assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
          attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
          dummy_token = torch.full(
              (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
          )
          input_ids = torch.cat([input_ids, dummy_token], dim=1)
  
          return {"input_ids": input_ids, "attention_mask": attention_mask}
  
  
  
  
  
  @add_start_docstrings(
      """Bert Model with a `next sentence prediction (classification)` head on top. """,
      BERT_START_DOCSTRING,
  )
  class BertForNextSentencePrediction(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.bert = BertModel(config)
          self.cls = BertOnlyNSPHead(config)
  
          self.init_weights()
  
  
  
      @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
      @replace_return_docstrings(output_type=NextSentencePredictorOutput, 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,
          **kwargs,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
              Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
              (see ``input_ids`` docstring). Indices should be in ``[0, 1]``:
  
              - 0 indicates sequence B is a continuation of sequence A,
              - 1 indicates sequence B is a random sequence.
  
          Returns:
  
          Example::
  
              >>> from transformers import BertTokenizer, BertForNextSentencePrediction
              >>> import torch
  
              >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              >>> model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
  
              >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
              >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
              >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
  
              >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
              >>> logits = outputs.logits
              >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
          """
  
          if "next_sentence_label" in kwargs:
              warnings.warn(
                  "The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.",
                  FutureWarning,
              )
              labels = kwargs.pop("next_sentence_label")
  
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.bert(
              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,
          )
  
          pooled_output = outputs[1]
  
          seq_relationship_scores = self.cls(pooled_output)
  
          next_sentence_loss = None
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
  
          if not return_dict:
              output = (seq_relationship_scores,) + outputs[2:]
              return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
  
          return NextSentencePredictorOutput(
              loss=next_sentence_loss,
              logits=seq_relationship_scores,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  
  
  
  @add_start_docstrings(
      """
      Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
      output) e.g. for GLUE tasks.
      """,
      BERT_START_DOCSTRING,
  )
  class BertForSequenceClassification(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
          self.config = config
  
          self.bert = BertModel(config)
          classifier_dropout = config.hidden_dropout_prob
          # 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(BERT_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.bert(
              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,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_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,
          )
  
  
  
  class BertWithExit_s1(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.num_labels = config.num_labels
          self.configuration_s1 = copy.deepcopy(config)
          self.configuration_s2 = copy.deepcopy(config)
  
          self.bert = BertModel(config)
          classifier_dropout = config.hidden_dropout_prob
          self.s1_dropout = nn.Dropout(classifier_dropout)
          self.s1_classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      def add_exit(self, start_point, end_point):
          self.configuration_s1.num_hidden_layers = start_point
          self.configuration_s2.num_hidden_layers = end_point - start_point
          self.bert_s1 = BertModel(self.configuration_s1)
          self.bert_s2 = BertEncoder(self.configuration_s2)
  
          dict_pre_trained = self.bert.state_dict().copy()
          dict_s1 = self.bert_s1.state_dict().copy()
          dict_s2 = self.bert_s2.state_dict().copy()
  
          for k,v in self.bert.state_dict().items():
  
              for k_s1,v_s1 in self.bert_s1.state_dict().items():
                  if k == k_s1:
                      dict_s1[k_s1] = dict_pre_trained[k]
  
              for k_s2,v_s2 in self.bert_s2.state_dict().items():
                  if k.split(".")[3:] == k_s2.split(".")[2:] and int(k.split(".")[2]) == int(k_s2.split(".")[1])+start_point:
                      dict_s2[k_s2] = dict_pre_trained[k]
  
          self.bert_s1.load_state_dict(dict_s1)
          self.bert_s2.load_state_dict(dict_s2)
  
      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).
          """
          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
  
          s1_outputs = self.bert_s1(
              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,
          )
  
          s1_pooled_output = s1_outputs[1]
  
          input_shape = input_ids.size()
          device = input_ids.device if input_ids is not None else inputs_embeds.device
          extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
  
          s2_outputs = self.bert_s2(
              s1_outputs[0],
              attention_mask=extended_attention_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          s1_pooled_output = self.s1_dropout(s1_pooled_output)
          s1_logits = self.s1_classifier(s1_pooled_output)
  
          return s2_outputs, s1_logits
  
  
  
  
  class BertWithExit_s2(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.num_labels = config.num_labels
          self.configuration_s2 = copy.deepcopy(config)
  
          self.bert = BertModel(config)
          classifier_dropout = config.hidden_dropout_prob
          self.s2_pooler = BertPooler(config)
          self.s2_dropout = nn.Dropout(classifier_dropout)
          self.s2_classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      def add_exit(self, start_point, end_point):
          self.configuration_s2.num_hidden_layers -= end_point
          self.bert_s2 = BertEncoder(self.configuration_s2)
  
          dict_pre_trained = self.bert.state_dict().copy()
          dict_s2 = self.bert_s2.state_dict().copy()
  
          for k,v in self.bert.state_dict().items():
              for k_s2,v_s2 in self.bert_s2.state_dict().items():
                  if k.split(".")[3:] == k_s2.split(".")[2:] and int(k.split(".")[2]) == int(k_s2.split(".")[1])+end_point:
                      dict_s2[k_s2] = dict_pre_trained[k]
  
          self.bert_s2.load_state_dict(dict_s2)
  
      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).
          """
          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
  
          input_shape = torch.Size([1, 7])
          device = input_ids.device if input_ids is not None else inputs_embeds.device
          extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
          s2_outputs = self.bert_s2(
              input_ids,
              attention_mask=extended_attention_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          s2_pooled_output = self.s2_pooler(s2_outputs[0])
          s2_pooled_output = self.s2_dropout(s2_pooled_output)
          s2_logits = self.s2_classifier(s2_pooled_output)
          return s2_logits
  
  
  
  
  class BertWithExit(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.num_labels = config.num_labels
  
          self.configuration_s1 = copy.deepcopy(config)
          self.configuration_s2 = copy.deepcopy(config)
  
          self.bert = BertModel(config)
          classifier_dropout = config.hidden_dropout_prob
          # classifier_dropout = (
          #     config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
          # )
          self.s2_pooler = BertPooler(config)
          self.s1_dropout = nn.Dropout(classifier_dropout)
          self.s2_dropout = nn.Dropout(classifier_dropout)
          self.s1_classifier = nn.Linear(config.hidden_size, config.num_labels)
          self.s2_classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      def add_exit(self, placement):
          self.configuration_s1.num_hidden_layers = placement
          self.configuration_s2.num_hidden_layers -= placement
          self.bert_s1 = BertModel(self.configuration_s1)
          self.bert_s2 = BertEncoder(self.configuration_s2)
  
          dict_pre_trained = self.bert.state_dict().copy()
          dict_s1 = self.bert_s1.state_dict().copy()
          dict_s2 = self.bert_s2.state_dict().copy()
  
          for k,v in self.bert.state_dict().items():
  
              for k_s1,v_s1 in self.bert_s1.state_dict().items():
                  if k == k_s1:
                      dict_s1[k_s1] = dict_pre_trained[k]
  
              for k_s2,v_s2 in self.bert_s2.state_dict().items():
                  if k.split(".")[3:] == k_s2.split(".")[2:] and int(k.split(".")[2]) == int(k_s2.split(".")[1])+placement:
                      dict_s2[k_s2] = dict_pre_trained[k]
  
          self.bert_s1.load_state_dict(dict_s1)
          self.bert_s2.load_state_dict(dict_s2)
          # self.init_weights()
  
      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).
          """
          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
  
          s1_outputs = self.bert_s1(
              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,
          )
  
          s1_pooled_output = s1_outputs[1]
  
          input_shape = input_ids.size()
          device = input_ids.device if input_ids is not None else inputs_embeds.device
          extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
          s2_outputs = self.bert_s2(
              s1_outputs[0],
              attention_mask=extended_attention_mask,
              head_mask=head_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          s2_pooled_output = self.s2_pooler(s2_outputs[0])
          s1_pooled_output = self.s1_dropout(s1_pooled_output)
          s1_logits = self.s1_classifier(s1_pooled_output)
          s2_pooled_output = self.s2_dropout(s2_pooled_output)
          s2_logits = self.s2_classifier(s2_pooled_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:
                      s1_loss = loss_fct(s1_logits.squeeze(), labels.squeeze())
                      s2_loss = loss_fct(s2_logits.squeeze(), labels.squeeze())
                  else:
                      s1_loss = loss_fct(s1_logits, labels)
                      s2_loss = loss_fct(s2_logits, labels)
              elif self.config.problem_type == "single_label_classification":
                  loss_fct = CrossEntropyLoss()
                  s1_loss = loss_fct(s1_logits.view(-1, self.num_labels), labels.view(-1))
                  s2_loss = loss_fct(s2_logits.view(-1, self.num_labels), labels.view(-1))
              elif self.config.problem_type == "multi_label_classification":
                  loss_fct = BCEWithLogitsLoss()
                  s1_loss = loss_fct(s1_logits, labels)
                  s2_loss = loss_fct(s2_logits, labels)
              loss = 0.5 * s1_loss + 0.5 * s2_loss
          if not return_dict:
              output = (s1_logits,) + s1_outputs[2:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutput(
              loss=loss,
              logits=s1_logits,
              hidden_states=s1_outputs.hidden_states,
              attentions=s1_outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      Bert 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.
      """,
      BERT_START_DOCSTRING,
  )
  class BertForMultipleChoice(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.bert = BertModel(config)
          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, 1)
  
          self.init_weights()
  
  
  
      @add_start_docstrings_to_model_forward(BERT_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,
          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 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]
  
          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
          token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
          position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids 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.bert(
              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,
          )
  
          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(
      """
      Bert 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.
      """,
      BERT_START_DOCSTRING,
  )
  class BertForTokenClassification(BertPreTrainedModel):
  
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.bert = BertModel(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(BERT_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.bert(
              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,
          )
  
  
  
  
  
  @add_start_docstrings(
      """
      Bert 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`).
      """,
      BERT_START_DOCSTRING,
  )
  class BertForQuestionAnswering(BertPreTrainedModel):
  
      _keys_to_ignore_on_load_unexpected = [r"pooler"]
  
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.bert = BertModel(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(BERT_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.bert(
              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,
          )
  