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 logging
  import math
  import os
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from .activations import gelu, gelu_new, swish
  from .configuration_bert import BertConfig
  from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
  from .modeling_utils import PreTrainedModel, prune_linear_layer
  
  
  logger = logging.getLogger(__name__)
  
  BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
      "bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin",
      "bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-pytorch_model.bin",
      "bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-pytorch_model.bin",
      "bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-pytorch_model.bin",
      "bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-pytorch_model.bin",
      "bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-pytorch_model.bin",
      "bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-pytorch_model.bin",
      "bert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-pytorch_model.bin",
      "bert-large-uncased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-pytorch_model.bin",
      "bert-large-cased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-pytorch_model.bin",
      "bert-large-uncased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-pytorch_model.bin",
      "bert-large-cased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-pytorch_model.bin",
      "bert-base-cased-finetuned-mrpc": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-pytorch_model.bin",
      "bert-base-german-dbmdz-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-pytorch_model.bin",
      "bert-base-german-dbmdz-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-pytorch_model.bin",
      "bert-base-japanese": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-pytorch_model.bin",
      "bert-base-japanese-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking-pytorch_model.bin",
      "bert-base-japanese-char": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-pytorch_model.bin",
      "bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-pytorch_model.bin",
      "bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/pytorch_model.bin",
      "bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/pytorch_model.bin",
      "bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/pytorch_model.bin",
  }
  
  
  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("Converting TensorFlow checkpoint from {}".format(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("Loading TF weight {} with shape {}".format(name, 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("Skipping {}".format("/".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("Skipping {}".format("/".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
          except AssertionError as e:
              e.args += (pointer.shape, array.shape)
              raise
          logger.info("Initialize PyTorch weight {}".format(name))
          pointer.data = torch.from_numpy(array)
      return model
  
  
  def mish(x):
      return x * torch.tanh(nn.functional.softplus(x))
  
  
  ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}
  
  
  BertLayerNorm = torch.nn.LayerNorm
  
  
  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=0)
          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 = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
          if input_ids is not None:
              input_shape = input_ids.size()
          else:
              input_shape = inputs_embeds.size()[:-1]
  
          seq_length = input_shape[1]
          device = input_ids.device if input_ids is not None else inputs_embeds.device
          if position_ids is None:
              position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
              position_ids = position_ids.unsqueeze(0).expand(input_shape)
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          if inputs_embeds is None:
              inputs_embeds = self.word_embeddings(input_ids)
          position_embeddings = self.position_embeddings(position_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = inputs_embeds + position_embeddings + token_type_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:
              raise ValueError(
                  "The hidden size (%d) is not a multiple of the number of attention "
                  "heads (%d)" % (config.hidden_size, config.num_attention_heads)
              )
          self.output_attentions = config.output_attentions
  
          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)
  
      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,
      ):
          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.
          if encoder_hidden_states is not None:
              mixed_key_layer = self.key(encoder_hidden_states)
              mixed_value_layer = self.value(encoder_hidden_states)
              attention_mask = encoder_attention_mask
          else:
              mixed_key_layer = self.key(hidden_states)
              mixed_value_layer = self.value(hidden_states)
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
          key_layer = self.transpose_for_scores(mixed_key_layer)
          value_layer = self.transpose_for_scores(mixed_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))
          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 self.output_attentions else (context_layer,)
          return outputs
  
  
  class BertSelfOutput(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = BertLayerNorm(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
          mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)
          heads = set(heads) - self.pruned_heads  # Convert to set and remove already pruned heads
          for head in heads:
              # Compute how many pruned heads are before the head and move the index accordingly
              head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
              mask[head] = 0
          mask = mask.view(-1).contiguous().eq(1)
          index = torch.arange(len(mask))[mask].long()
  
          # 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,
      ):
          self_outputs = self.self(
              hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask
          )
          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 = BertLayerNorm(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.attention = BertAttention(config)
          self.is_decoder = config.is_decoder
          if self.is_decoder:
              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,
      ):
          self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
          attention_output = self_attention_outputs[0]
          outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
  
          if self.is_decoder and encoder_hidden_states is not None:
              cross_attention_outputs = self.crossattention(
                  attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask
              )
              attention_output = cross_attention_outputs[0]
              outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights
  
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.output(intermediate_output, attention_output)
          outputs = (layer_output,) + outputs
          return outputs
  
  
  class BertEncoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
          self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          head_mask=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
      ):
          all_hidden_states = ()
          all_attentions = ()
          for i, layer_module in enumerate(self.layer):
              if self.output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              layer_outputs = layer_module(
                  hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask
              )
              hidden_states = layer_outputs[0]
  
              if self.output_attentions:
                  all_attentions = all_attentions + (layer_outputs[1],)
  
          # Add last layer
          if self.output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          outputs = (hidden_states,)
          if self.output_hidden_states:
              outputs = outputs + (all_hidden_states,)
          if self.output_attentions:
              outputs = outputs + (all_attentions,)
          return outputs  # last-layer hidden state, (all hidden states), (all 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 = BertLayerNorm(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
      pretrained_model_archive_map = BERT_PRETRAINED_MODEL_ARCHIVE_MAP
      load_tf_weights = load_tf_weights_in_bert
      base_model_prefix = "bert"
  
      def _init_weights(self, module):
          """ Initialize the weights """
          if isinstance(module, (nn.Linear, nn.Embedding)):
              # 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)
          elif isinstance(module, BertLayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
          if isinstance(module, nn.Linear) and module.bias is not None:
              module.bias.data.zero_()
  
  
  BERT_START_DOCSTRING = r"""
      This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
      Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
      usage and behavior.
  
      Parameters:
          config (:class:`~transformers.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:`(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`transformers.BertTokenizer`.
              See :func:`transformers.PreTrainedTokenizer.encode` and
              :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Mask to avoid performing attention on padding token indices.
              Mask values selected in ``[0, 1]``:
              ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Segment token indices to indicate first and second portions of the inputs.
              Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
              corresponds to a `sentence B` token
  
              `What are token type IDs? <../glossary.html#token-type-ids>`_
          position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Indices of positions of each input sequence tokens in the position embeddings.
              Selected in the range ``[0, config.max_position_embeddings - 1]``.
  
              `What are position IDs? <../glossary.html#position-ids>`_
          head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
              Mask to nullify selected heads of the self-attention modules.
              Mask values selected in ``[0, 1]``:
              :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
          inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
              Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert `input_ids` indices into associated vectors
              than the model's internal embedding lookup matrix.
          encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
              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`, defaults to :obj:`None`):
              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 MASKED tokens.
  """
  
  
  @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`_ 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`; an
      :obj:`encoder_hidden_states` is expected as an input to the forward pass.
  
      .. _`Attention is all you need`:
          https://arxiv.org/abs/1706.03762
  
      """
  
      def __init__(self, config):
          super().__init__(config)
          self.config = config
  
          self.embeddings = BertEmbeddings(config)
          self.encoder = BertEncoder(config)
          self.pooler = BertPooler(config)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
  
      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_callable(BERT_INPUTS_DOCSTRING)
      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,
      ):
          r"""
      Return:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
              Last layer hidden-state of the first token of the sequence (classification token)
              further processed by a Linear layer and a Tanh activation function. The Linear
              layer weights are trained from the next sentence prediction (classification)
              objective during pre-training.
  
              This output is usually *not* a good summary
              of the semantic content of the input, you're often better with averaging or pooling
              the sequence of hidden-states for the whole input sequence.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import BertModel, BertTokenizer
          import torch
  
          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
          model = BertModel.from_pretrained('bert-base-uncased')
  
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids)
  
          last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
  
          """
  
          if input_ids is not None and inputs_embeds is not None:
              raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
          elif input_ids is not None:
              input_shape = input_ids.size()
          elif inputs_embeds is not None:
              input_shape = inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          device = input_ids.device if input_ids is not None else inputs_embeds.device
  
          if attention_mask is None:
              attention_mask = torch.ones(input_shape, device=device)
          if token_type_ids is None:
              token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
  
          # 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.
          if attention_mask.dim() == 3:
              extended_attention_mask = attention_mask[:, None, :, :]
          elif attention_mask.dim() == 2:
              # Provided a padding mask of dimensions [batch_size, seq_length]
              # - if the model is a decoder, apply a causal mask in addition to the padding mask
              # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
              if self.config.is_decoder:
                  batch_size, seq_length = input_shape
                  seq_ids = torch.arange(seq_length, device=device)
                  causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
                  causal_mask = causal_mask.to(
                      attention_mask.dtype
                  )  # causal and attention masks must have same type with pytorch version < 1.3
                  extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
              else:
                  extended_attention_mask = attention_mask[:, None, None, :]
          else:
              raise ValueError(
                  "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
                      input_shape, attention_mask.shape
                  )
              )
  
          # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
          # masked positions, this operation will create a tensor which is 0.0 for
          # positions we want to attend and -10000.0 for masked positions.
          # Since we are adding it to the raw scores before the softmax, this is
          # effectively the same as removing these entirely.
          extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
  
          # If a 2D ou 3D attention mask is provided for the cross-attention
          # we need to make broadcastabe 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)
  
              if encoder_attention_mask.dim() == 3:
                  encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
              elif encoder_attention_mask.dim() == 2:
                  encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
              else:
                  raise ValueError(
                      "Wrong shape for encoder_hidden_shape (shape {}) or encoder_attention_mask (shape {})".format(
                          encoder_hidden_shape, encoder_attention_mask.shape
                      )
                  )
  
              encoder_extended_attention_mask = encoder_extended_attention_mask.to(
                  dtype=next(self.parameters()).dtype
              )  # fp16 compatibility
              encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
          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]
          if head_mask is not None:
              if head_mask.dim() == 1:
                  head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                  head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
              elif head_mask.dim() == 2:
                  head_mask = (
                      head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
                  )  # We can specify head_mask for each layer
              head_mask = head_mask.to(
                  dtype=next(self.parameters()).dtype
              )  # switch to fload if need + fp16 compatibility
          else:
              head_mask = [None] * 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
          )
          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,
          )
          sequence_output = encoder_outputs[0]
          pooled_output = self.pooler(sequence_output)
  
          outputs = (sequence_output, pooled_output,) + encoder_outputs[
              1:
          ]  # add hidden_states and attentions if they are here
          return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """Bert Model with two heads on top as done during the pre-training: 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
  
      @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          masked_lm_labels=None,
          next_sentence_label=None,
      ):
          r"""
          masked_lm_labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
              Labels for computing the masked language modeling loss.
              Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
              Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
              in ``[0, ..., config.vocab_size]``
          next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`, defaults to :obj:`None`):
              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.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
          prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, 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 :obj:`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 ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
  
      Examples::
  
          from transformers import BertTokenizer, BertForPreTraining
          import torch
  
          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
          model = BertForPreTraining.from_pretrained('bert-base-uncased')
  
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids)
  
          prediction_scores, seq_relationship_scores = outputs[:2]
  
          """
  
          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,
          )
  
          sequence_output, pooled_output = outputs[:2]
          prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
  
          outputs = (prediction_scores, seq_relationship_score,) + outputs[
              2:
          ]  # add hidden states and attention if they are here
  
          if masked_lm_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), masked_lm_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
              outputs = (total_loss,) + outputs
  
          return outputs  # (loss), prediction_scores, seq_relationship_score, (hidden_states), (attentions)
  
  
  @add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
  class BertForMaskedLM(BertPreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.bert = BertModel(config)
          self.cls = BertOnlyMLMHead(config)
  
          self.init_weights()
  
      def get_output_embeddings(self):
          return self.cls.predictions.decoder
  
      @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          masked_lm_labels=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
          lm_labels=None,
      ):
          r"""
          masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Labels for computing the masked language modeling loss.
              Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
              Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
              in ``[0, ..., config.vocab_size]``
          lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Labels for computing the left-to-right language modeling loss (next word prediction).
              Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
              Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
              in ``[0, ..., config.vocab_size]``
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          masked_lm_loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Masked language modeling loss.
          ltr_lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`lm_labels` is provided):
                  Next token prediction loss.
          prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
          Examples::
  
              from transformers import BertTokenizer, BertForMaskedLM
              import torch
  
              tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
              model = BertForMaskedLM.from_pretrained('bert-base-uncased')
  
              input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
              outputs = model(input_ids, masked_lm_labels=input_ids)
  
              loss, prediction_scores = outputs[:2]
  
          """
  
          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,
          )
  
          sequence_output = outputs[0]
          prediction_scores = self.cls(sequence_output)
  
          outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
  
          # Although this may seem awkward, BertForMaskedLM supports two scenarios:
          # 1. If a tensor that contains the indices of masked labels is provided,
          #    the cross-entropy is the MLM cross-entropy that measures the likelihood
          #    of predictions for masked words.
          # 2. If `lm_labels` is provided we are in a causal scenario where we
          #    try to predict the next token for each input in the decoder.
          if masked_lm_labels is not None:
              loss_fct = CrossEntropyLoss()  # -100 index = padding token
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
              outputs = (masked_lm_loss,) + outputs
  
          if lm_labels is not None:
              # we are doing next-token prediction; shift prediction scores and input ids by one
              prediction_scores = prediction_scores[:, :-1, :].contiguous()
              lm_labels = lm_labels[:, 1:].contiguous()
              loss_fct = CrossEntropyLoss()
              ltr_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), lm_labels.view(-1))
              outputs = (ltr_lm_loss,) + outputs
  
          return outputs  # (masked_lm_loss), (ltr_lm_loss), prediction_scores, (hidden_states), (attentions)
  
  
  @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_callable(BERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          next_sentence_label=None,
      ):
          r"""
          next_sentence_label (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              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:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):
              Next sequence prediction (classification) loss.
          seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, 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 ``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 ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import BertTokenizer, BertForNextSentencePrediction
          import torch
  
          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
          model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
  
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids)
  
          seq_relationship_scores = outputs[0]
  
          """
  
          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,
          )
  
          pooled_output = outputs[1]
  
          seq_relationship_score = self.cls(pooled_output)
  
          outputs = (seq_relationship_score,) + outputs[2:]  # add hidden states and attention if they are here
          if next_sentence_label is not None:
              loss_fct = CrossEntropyLoss()
              next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
              outputs = (next_sentence_loss,) + outputs
  
          return outputs  # (next_sentence_loss), seq_relationship_score, (hidden_states), (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.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              Labels for computing the sequence classification/regression loss.
              Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
              If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
              If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
              Classification (or regression if config.num_labels==1) loss.
          logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
              Classification (or regression if config.num_labels==1) scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import BertTokenizer, BertForSequenceClassification
          import torch
  
          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
          model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
  
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
  
          loss, logits = outputs[:2]
  
          """
  
          outputs = self.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,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
  
          outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
  
          if labels is not None:
              if self.num_labels == 1:
                  #  We are doing regression
                  loss_fct = MSELoss()
                  loss = loss_fct(logits.view(-1), labels.view(-1))
              else:
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), logits, (hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """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)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, 1)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              Labels for computing the multiple choice classification loss.
              Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
              of the input tensors. (see `input_ids` above)
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided):
              Classification loss.
          classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
              `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
  
              Classification scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import BertTokenizer, BertForMultipleChoice
          import torch
  
          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
          model = BertForMultipleChoice.from_pretrained('bert-base-uncased')
          choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
  
          input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
          labels = torch.tensor(1).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
  
          loss, classification_scores = outputs[:2]
  
          """
          num_choices = input_ids.shape[1]
  
          input_ids = input_ids.view(-1, input_ids.size(-1))
          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
  
          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,
          )
  
          pooled_output = outputs[1]
  
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
          reshaped_logits = logits.view(-1, num_choices)
  
          outputs = (reshaped_logits,) + outputs[2:]  # add hidden states and attention if they are here
  
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels)
              outputs = (loss,) + outputs
  
          return outputs  # (loss), reshaped_logits, (hidden_states), (attentions)
  
  
  @add_start_docstrings(
      """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):
      def __init__(self, config):
          super().__init__(config)
          self.num_labels = config.num_labels
  
          self.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
      def forward(
          self,
          input_ids=None,
          attention_mask=None,
          token_type_ids=None,
          position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          labels=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
              Labels for computing the token classification loss.
              Indices should be in ``[0, ..., config.num_labels - 1]``.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
              Classification loss.
          scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
              Classification scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import BertTokenizer, BertForTokenClassification
          import torch
  
          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
          model = BertForTokenClassification.from_pretrained('bert-base-uncased')
  
          input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
          labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
          outputs = model(input_ids, labels=labels)
  
          loss, scores = outputs[:2]
  
          """
  
          outputs = self.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,
          )
  
          sequence_output = outputs[0]
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              # Only keep active parts of the loss
              if attention_mask is not None:
                  active_loss = attention_mask.view(-1) == 1
                  active_logits = logits.view(-1, self.num_labels)
                  active_labels = torch.where(
                      active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
                  )
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              outputs = (loss,) + outputs
  
          return outputs  # (loss), scores, (hidden_states), (attentions)
  
  
  @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):
      def __init__(self, config):
          super(BertForQuestionAnswering, self).__init__(config)
          self.num_labels = config.num_labels
  
          self.bert = BertModel(config)
          self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
  
          self.init_weights()
  
      @add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
      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,
      ):
          r"""
          start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              Labels for position (index) of the start of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`).
              Position outside of the sequence are not taken into account for computing the loss.
          end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
              Labels for position (index) of the end of the labelled span for computing the token classification loss.
              Positions are clamped to the length of the sequence (`sequence_length`).
              Position outside of the sequence are not taken into account for computing the loss.
  
      Returns:
          :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
          loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
              Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
          start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-start scores (before SoftMax).
          end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
              Span-end scores (before SoftMax).
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
              :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
  
      Examples::
  
          from transformers import BertTokenizer, BertForQuestionAnswering
          import torch
  
          tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
          model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
  
          question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
          input_ids = tokenizer.encode(question, text)
          token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))]
          start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids]))
  
          all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
          answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
  
          assert answer == "a nice puppet"
  
          """
  
          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,
          )
  
          sequence_output = outputs[0]
  
          logits = self.qa_outputs(sequence_output)
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          outputs = (start_logits, end_logits,) + outputs[2:]
          if start_positions is not None and end_positions is not None:
              # If we are on multi-GPU, split add a dimension
              if len(start_positions.size()) > 1:
                  start_positions = start_positions.squeeze(-1)
              if len(end_positions.size()) > 1:
                  end_positions = end_positions.squeeze(-1)
              # sometimes the start/end positions are outside our model inputs, we ignore these terms
              ignored_index = start_logits.size(1)
              start_positions.clamp_(0, ignored_index)
              end_positions.clamp_(0, ignored_index)
  
              loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
              start_loss = loss_fct(start_logits, start_positions)
              end_loss = loss_fct(end_logits, end_positions)
              total_loss = (start_loss + end_loss) / 2
              outputs = (total_loss,) + outputs
  
          return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)
  