text: """PyTorch BERT model."""
  
  from __future__ import (absolute_import, division, print_function,
                          unicode_literals)
  
  import logging
  import os
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from torch.nn import functional as F
  
  from model.configuration_utils import PretrainedConfig
  from model.file_utils import cached_path, WEIGHTS_NAME, TF_WEIGHTS_NAME
  
  logger = logging.getLogger(__name__)
  
  
  try:
      from torch.nn import Identity
  except ImportError:
      # Older PyTorch compatibility
      class Identity(nn.Module):
          r"""A placeholder identity operator that is argument-insensitive.
          """
          def __init__(self, *args, **kwargs):
              super(Identity, self).__init__()
  
          def forward(self, input):
              return input
  
  class PreTrainedModel(nn.Module):
      r""" Base class for all models.
  
          :class:`~pytorch_transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
          as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
  
          Class attributes (overridden by derived classes):
              - ``config_class``: a class derived from :class:`~pytorch_transformers.PretrainedConfig` to use as configuration class for this model architecture.
              - ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
              - ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
  
                  - ``model``: an instance of the relevant subclass of :class:`~pytorch_transformers.PreTrainedModel`,
                  - ``config``: an instance of the relevant subclass of :class:`~pytorch_transformers.PretrainedConfig`,
                  - ``path``: a path (string) to the TensorFlow checkpoint.
  
              - ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
      """
      config_class = None
      pretrained_model_archive_map = {}
      load_tf_weights = lambda model, config, path: None
      base_model_prefix = ""
  
      def __init__(self, config, *inputs, **kwargs):
          super(PreTrainedModel, self).__init__()
          if not isinstance(config, PretrainedConfig):
              raise ValueError(
                  "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
                  "To create a model from a pretrained model use "
                  "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
                      self.__class__.__name__, self.__class__.__name__
                  ))
          # Save config in model
          self.config = config
  
      def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
          """ Build a resized Embedding Module from a provided token Embedding Module.
              Increasing the size will add newly initialized vectors at the end
              Reducing the size will remove vectors from the end
  
          Args:
              new_num_tokens: (`optional`) int
                  New number of tokens in the embedding matrix.
                  Increasing the size will add newly initialized vectors at the end
                  Reducing the size will remove vectors from the end
                  If not provided or None: return the provided token Embedding Module.
          Return: ``torch.nn.Embeddings``
              Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
          """
          if new_num_tokens is None:
              return old_embeddings
  
          old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
          if old_num_tokens == new_num_tokens:
              return old_embeddings
  
          # Build new embeddings
          new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
          new_embeddings.to(old_embeddings.weight.device)
  
          # initialize all new embeddings (in particular added tokens)
          self._init_weights(new_embeddings)
  
          # Copy word embeddings from the previous weights
          num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
          new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]
  
          return new_embeddings
  
      def _tie_or_clone_weights(self, first_module, second_module):
          """ Tie or clone module weights depending of weither we are using TorchScript or not
          """
  
          if self.config.torchscript:
              first_module.weight = nn.Parameter(second_module.weight.clone())
          else:
              first_module.weight = second_module.weight
  
  
          if hasattr(first_module, 'bias') and first_module.bias is not None:
              first_module.bias.data = torch.nn.functional.pad(
                  first_module.bias.data,
                  (0, first_module.weight.shape[0] - first_module.bias.shape[0]),
                  'constant',
                  0
              )
  
      def resize_token_embeddings(self, new_num_tokens=None):
          """ Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
          Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
  
          Arguments:
  
              new_num_tokens: (`optional`) int:
                  New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. 
                  If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.
  
          Return: ``torch.nn.Embeddings``
              Pointer to the input tokens Embeddings Module of the model
          """
          base_model = getattr(self, self.base_model_prefix, self)  # get the base model if needed
          model_embeds = base_model._resize_token_embeddings(new_num_tokens)
          if new_num_tokens is None:
              return model_embeds
  
          # Update base model and current model config
          self.config.vocab_size = new_num_tokens
          base_model.vocab_size = new_num_tokens
  
          # Tie weights again if needed
          if hasattr(self, 'tie_weights'):
              self.tie_weights()
  
          return model_embeds
  
      def init_weights(self):
          """ Initialize and prunes weights if needed. """
          # Initialize weights
          self.apply(self._init_weights)
  
          # Prune heads if needed
          if self.config.pruned_heads:
              self.prune_heads(self.config.pruned_heads)
  
      def prune_heads(self, heads_to_prune):
          """ Prunes heads of the base model.
  
              Arguments:
  
                  heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
                  E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
          """
          base_model = getattr(self, self.base_model_prefix, self)  # get the base model if needed
  
          # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
          for layer, heads in heads_to_prune.items():
              union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
              self.config.pruned_heads[layer] = list(union_heads)  # Unfortunately we have to store it as list for JSON
  
          base_model._prune_heads(heads_to_prune)
  
      def save_pretrained(self, save_directory):
          """ Save a model and its configuration file to a directory, so that it
              can be re-loaded using the `:func:`~pytorch_transformers.PreTrainedModel.from_pretrained`` class method.
          """
          assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
  
          # Only save the model it-self if we are using distributed training
          model_to_save = self.module if hasattr(self, 'module') else self
  
          # Save configuration file
          model_to_save.config.save_pretrained(save_directory)
  
          # If we save using the predefined names, we can load using `from_pretrained`
          output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
  
          torch.save(model_to_save.state_dict(), output_model_file)
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
          r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.
  
          The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)
          To train the model, you should first set it back in training mode with ``model.train()``
  
          The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
          It is up to you to train those weights with a downstream fine-tuning task.
  
          The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.
  
          Parameters:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  
              model_args: (`optional`) Sequence of positional arguments:
                  All remaning positional arguments will be passed to the underlying model's ``__init__`` method
  
              config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
                  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
  
                  - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                  - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                  - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
  
              state_dict: (`optional`) dict:
                  an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                  This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                  In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
  
              cache_dir: (`optional`) string:
                  Path to a directory in which a downloaded pre-trained model
                  configuration should be cached if the standard cache should not be used.
  
              force_download: (`optional`) boolean, default False:
                  Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
  
              proxies: (`optional`) dict, default None:
                  A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                  The proxies are used on each request.
  
              output_loading_info: (`optional`) boolean:
                  Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
  
              kwargs: (`optional`) Remaining dictionary of keyword arguments:
                  Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
  
                  - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                  - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
  
          Examples::
  
              model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
              model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
              assert model.config.output_attention == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
              model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
          config = kwargs.pop('config', None)
          state_dict = kwargs.pop('state_dict', None)
          cache_dir = kwargs.pop('cache_dir', None)
          from_tf = kwargs.pop('from_tf', False)
          force_download = kwargs.pop('force_download', False)
          proxies = kwargs.pop('proxies', None)
          output_loading_info = kwargs.pop('output_loading_info', False)
  
          # Load config
          if config is None:
              config, model_kwargs = cls.config_class.from_pretrained(
                  pretrained_model_name_or_path, *model_args,
                  cache_dir=cache_dir, return_unused_kwargs=True,
                  force_download=force_download,
                  **kwargs
              )
          else:
              model_kwargs = kwargs
  
          # Load model
          if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
              archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path]
          elif os.path.isdir(pretrained_model_name_or_path):
              if from_tf:
                  # Directly load from a TensorFlow checkpoint
                  archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
              else:
                  archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
          else:
              if from_tf:
                  # Directly load from a TensorFlow checkpoint
                  archive_file = pretrained_model_name_or_path + ".index"
              else:
                  archive_file = pretrained_model_name_or_path
          # redirect to the cache, if necessary
          try:
              resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
          except EnvironmentError as e:
              if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                  logger.error(
                      "Couldn't reach server at '{}' to download pretrained weights.".format(
                          archive_file))
              else:
                  logger.error(
                      "Model name '{}' was not found in model name list ({}). "
                      "We assumed '{}' was a path or url but couldn't find any file "
                      "associated to this path or url.".format(
                          pretrained_model_name_or_path,
                          ', '.join(cls.pretrained_model_archive_map.keys()),
                          archive_file))
              raise e
          if resolved_archive_file == archive_file:
              logger.info("loading weights file {}".format(archive_file))
          else:
              logger.info("loading weights file {} from cache at {}".format(
                  archive_file, resolved_archive_file))
  
          # Instantiate model.
          model = cls(config, *model_args, **model_kwargs)
  
          if state_dict is None and not from_tf:
              state_dict = torch.load(resolved_archive_file, map_location='cpu')
          if from_tf:
              # Directly load from a TensorFlow checkpoint
              return cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
  
          # Convert old format to new format if needed from a PyTorch state_dict
          old_keys = []
          new_keys = []
          for key in state_dict.keys():
              new_key = None
              if 'gamma' in key:
                  new_key = key.replace('gamma', 'weight')
              if 'beta' in key:
                  new_key = key.replace('beta', 'bias')
              if new_key:
                  old_keys.append(key)
                  new_keys.append(new_key)
          for old_key, new_key in zip(old_keys, new_keys):
              state_dict[new_key] = state_dict.pop(old_key)
  
          # Load from a PyTorch state_dict
          missing_keys = []
          unexpected_keys = []
          error_msgs = []
          # copy state_dict so _load_from_state_dict can modify it
          metadata = getattr(state_dict, '_metadata', None)
          state_dict = state_dict.copy()
          if metadata is not None:
              state_dict._metadata = metadata
  
          def load(module, prefix=''):
              local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
              module._load_from_state_dict(
                  state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
              for name, child in module._modules.items():
                  if child is not None:
                      load(child, prefix + name + '.')
  
          # Make sure we are able to load base models as well as derived models (with heads)
          start_prefix = ''
          model_to_load = model
          if not hasattr(model, cls.base_model_prefix) and any(s.startswith(cls.base_model_prefix) for s in state_dict.keys()):
              start_prefix = cls.base_model_prefix + '.'
          if hasattr(model, cls.base_model_prefix) and not any(s.startswith(cls.base_model_prefix) for s in state_dict.keys()):
              model_to_load = getattr(model, cls.base_model_prefix)
  
          load(model_to_load, prefix=start_prefix)
          if len(missing_keys) > 0:
              logger.info("Weights of {} not initialized from pretrained model: {}".format(
                  model.__class__.__name__, missing_keys))
          if len(unexpected_keys) > 0:
              logger.info("Weights from pretrained model not used in {}: {}".format(
                  model.__class__.__name__, unexpected_keys))
          if len(error_msgs) > 0:
              raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
                                 model.__class__.__name__, "\n\t".join(error_msgs)))
  
          if hasattr(model, 'tie_weights'):
              model.tie_weights()  # make sure word embedding weights are still tied
  
          # Set model in evaluation mode to desactivate DropOut modules by default
          model.eval()
  
          if output_loading_info:
              loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs}
              return model, loading_info
  
          return model
  
  
  class Conv1D(nn.Module):
      def __init__(self, nf, nx):
          """ Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
              Basically works like a Linear layer but the weights are transposed
          """
          super(Conv1D, self).__init__()
          self.nf = nf
          w = torch.empty(nx, nf)
          nn.init.normal_(w, std=0.02)
          self.weight = nn.Parameter(w)
          self.bias = nn.Parameter(torch.zeros(nf))
  
      def forward(self, x):
          size_out = x.size()[:-1] + (self.nf,)
          x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
          x = x.view(*size_out)
          return x
  
  
  class PoolerStartLogits(nn.Module):
      """ Compute SQuAD start_logits from sequence hidden states. """
      def __init__(self, config):
          super(PoolerStartLogits, self).__init__()
          self.dense = nn.Linear(config.hidden_size, 1)
  
      def forward(self, hidden_states, p_mask=None):
          """ Args:
              **p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`
                  invalid position mask such as query and special symbols (PAD, SEP, CLS)
                  1.0 means token should be masked.
          """
          x = self.dense(hidden_states).squeeze(-1)
  
          if p_mask is not None:
              x = x * (1 - p_mask) - 1e30 * p_mask
  
          return x
  
  
  class PoolerEndLogits(nn.Module):
      """ Compute SQuAD end_logits from sequence hidden states and start token hidden state.
      """
      def __init__(self, config):
          super(PoolerEndLogits, self).__init__()
          self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
          self.activation = nn.Tanh()
          self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.dense_1 = nn.Linear(config.hidden_size, 1)
  
      def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):
          """ Args:
              One of ``start_states``, ``start_positions`` should be not None.
              If both are set, ``start_positions`` overrides ``start_states``.
  
              **start_states**: ``torch.LongTensor`` of shape identical to hidden_states
                  hidden states of the first tokens for the labeled span.
              **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
                  position of the first token for the labeled span:
              **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
                  Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
                  1.0 means token should be masked.
          """
          assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
          if start_positions is not None:
              slen, hsz = hidden_states.shape[-2:]
              start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
              start_states = hidden_states.gather(-2, start_positions) # shape (bsz, 1, hsz)
              start_states = start_states.expand(-1, slen, -1) # shape (bsz, slen, hsz)
  
          x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
          x = self.activation(x)
          x = self.LayerNorm(x)
          x = self.dense_1(x).squeeze(-1)
  
          if p_mask is not None:
              x = x * (1 - p_mask) - 1e30 * p_mask
  
          return x
  
  
  class PoolerAnswerClass(nn.Module):
      """ Compute SQuAD 2.0 answer class from classification and start tokens hidden states. """
      def __init__(self, config):
          super(PoolerAnswerClass, self).__init__()
          self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
          self.activation = nn.Tanh()
          self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)
  
      def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None):
          """
          Args:
              One of ``start_states``, ``start_positions`` should be not None.
              If both are set, ``start_positions`` overrides ``start_states``.
  
              **start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``.
                  hidden states of the first tokens for the labeled span.
              **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
                  position of the first token for the labeled span.
              **cls_index**: torch.LongTensor of shape ``(batch_size,)``
                  position of the CLS token. If None, take the last token.
  
              note(Original repo):
                  no dependency on end_feature so that we can obtain one single `cls_logits`
                  for each sample
          """
          hsz = hidden_states.shape[-1]
          assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
          if start_positions is not None:
              start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
              start_states = hidden_states.gather(-2, start_positions).squeeze(-2) # shape (bsz, hsz)
  
          if cls_index is not None:
              cls_index = cls_index[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
              cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, hsz)
          else:
              cls_token_state = hidden_states[:, -1, :] # shape (bsz, hsz)
  
          x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1))
          x = self.activation(x)
          x = self.dense_1(x).squeeze(-1)
  
          return x
  
  
  class SQuADHead(nn.Module):
      r""" A SQuAD head inspired by XLNet.
  
      Parameters:
          config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.
  
      Inputs:
          **hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``
              hidden states of sequence tokens
          **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
              position of the first token for the labeled span.
          **end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
              position of the last token for the labeled span.
          **cls_index**: torch.LongTensor of shape ``(batch_size,)``
              position of the CLS token. If None, take the last token.
          **is_impossible**: ``torch.LongTensor`` of shape ``(batch_size,)``
              Whether the question has a possible answer in the paragraph or not.
          **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
              Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
              1.0 means token should be masked.
  
      Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
          **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
              Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
          **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
              ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
              Log probabilities for the top config.start_n_top start token possibilities (beam-search).
          **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
              ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
              Indices for the top config.start_n_top start token possibilities (beam-search).
          **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
              ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
              Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
          **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
              ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
              Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
          **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
              ``torch.FloatTensor`` of shape ``(batch_size,)``
              Log probabilities for the ``is_impossible`` label of the answers.
      """
      def __init__(self, config):
          super(SQuADHead, self).__init__()
          self.start_n_top = config.start_n_top
          self.end_n_top = config.end_n_top
  
          self.start_logits = PoolerStartLogits(config)
          self.end_logits = PoolerEndLogits(config)
          self.answer_class = PoolerAnswerClass(config)
  
      def forward(self, hidden_states, start_positions=None, end_positions=None,
                  cls_index=None, is_impossible=None, p_mask=None):
          outputs = ()
  
          start_logits = self.start_logits(hidden_states, p_mask=p_mask)
  
          if start_positions is not None and end_positions is not None:
              # If we are on multi-GPU, let's remove the dimension added by batch splitting
              for x in (start_positions, end_positions, cls_index, is_impossible):
                  if x is not None and x.dim() > 1:
                      x.squeeze_(-1)
  
              # during training, compute the end logits based on the ground truth of the start position
              end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)
  
              loss_fct = CrossEntropyLoss()
              start_loss = loss_fct(start_logits, start_positions)
              end_loss = loss_fct(end_logits, end_positions)
              total_loss = (start_loss + end_loss) / 2
  
              if cls_index is not None and is_impossible is not None:
                  # Predict answerability from the representation of CLS and START
                  cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
                  loss_fct_cls = nn.BCEWithLogitsLoss()
                  cls_loss = loss_fct_cls(cls_logits, is_impossible)
  
                  # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
                  total_loss += cls_loss * 0.5
  
              outputs = (total_loss,) + outputs
  
          else:
              # during inference, compute the end logits based on beam search
              bsz, slen, hsz = hidden_states.size()
              start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)
  
              start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
              start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
              start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
              start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)
  
              hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)
              p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
              end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
              end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)
  
              end_top_log_probs, end_top_index = torch.topk(end_log_probs, self.end_n_top, dim=1) # shape (bsz, end_n_top, start_n_top)
              end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
              end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)
  
              start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
              cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)
  
              outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
  
          # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
          # or (if labels are provided) (total_loss,)
          return outputs
  
  
  class SequenceSummary(nn.Module):
      r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
          Args of the config class:
              summary_type:
                  - 'last' => [default] take the last token hidden state (like XLNet)
                  - 'first' => take the first token hidden state (like Bert)
                  - 'mean' => take the mean of all tokens hidden states
                  - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
                  - 'attn' => Not implemented now, use multi-head attention
              summary_use_proj: Add a projection after the vector extraction
              summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
              summary_activation: 'tanh' => add a tanh activation to the output, Other => no activation. Default
              summary_first_dropout: Add a dropout before the projection and activation
              summary_last_dropout: Add a dropout after the projection and activation
      """
      def __init__(self, config):
          super(SequenceSummary, self).__init__()
  
          self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
          if self.summary_type == 'attn':
              # We should use a standard multi-head attention module with absolute positional embedding for that.
              # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
              # We can probably just use the multi-head attention module of PyTorch >=1.1.0
              raise NotImplementedError
  
          self.summary = Identity()
          if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
              if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                  num_classes = config.num_labels
              else:
                  num_classes = config.hidden_size
              self.summary = nn.Linear(config.hidden_size, num_classes)
  
          self.activation = Identity()
          if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
              self.activation = nn.Tanh()
  
          self.first_dropout = Identity()
          if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
              self.first_dropout = nn.Dropout(config.summary_first_dropout)
  
          self.last_dropout = Identity()
          if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
              self.last_dropout = nn.Dropout(config.summary_last_dropout)
  
      def forward(self, hidden_states, cls_index=None):
          """ hidden_states: float Tensor in shape [bsz, seq_len, hidden_size], the hidden-states of the last layer.
              cls_index: [optional] position of the classification token if summary_type == 'cls_index',
                  shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.
                  if summary_type == 'cls_index' and cls_index is None:
                      we take the last token of the sequence as classification token
          """
          if self.summary_type == 'last':
              output = hidden_states[:, -1]
          elif self.summary_type == 'first':
              output = hidden_states[:, 0]
          elif self.summary_type == 'mean':
              output = hidden_states.mean(dim=1)
          elif self.summary_type == 'cls_index':
              if cls_index is None:
                  cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2]-1, dtype=torch.long)
              else:
                  cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
                  cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),))
              # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
              output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
          elif self.summary_type == 'attn':
              raise NotImplementedError
  
          output = self.first_dropout(output)
          output = self.summary(output)
          output = self.activation(output)
          output = self.last_dropout(output)
  
          return output
  
  
  def prune_linear_layer(layer, index, dim=0):
      """ Prune a linear layer (a model parameters) to keep only entries in index.
          Return the pruned layer as a new layer with requires_grad=True.
          Used to remove heads.
      """
      index = index.to(layer.weight.device)
      W = layer.weight.index_select(dim, index).clone().detach()
      if layer.bias is not None:
          if dim == 1:
              b = layer.bias.clone().detach()
          else:
              b = layer.bias[index].clone().detach()
      new_size = list(layer.weight.size())
      new_size[dim] = len(index)
      new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
      new_layer.weight.requires_grad = False
      new_layer.weight.copy_(W.contiguous())
      new_layer.weight.requires_grad = True
      if layer.bias is not None:
          new_layer.bias.requires_grad = False
          new_layer.bias.copy_(b.contiguous())
          new_layer.bias.requires_grad = True
      return new_layer
  
  
  def prune_conv1d_layer(layer, index, dim=1):
      """ Prune a Conv1D layer (a model parameters) to keep only entries in index.
          A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
          Return the pruned layer as a new layer with requires_grad=True.
          Used to remove heads.
      """
      index = index.to(layer.weight.device)
      W = layer.weight.index_select(dim, index).clone().detach()
      if dim == 0:
          b = layer.bias.clone().detach()
      else:
          b = layer.bias[index].clone().detach()
      new_size = list(layer.weight.size())
      new_size[dim] = len(index)
      new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
      new_layer.weight.requires_grad = False
      new_layer.weight.copy_(W.contiguous())
      new_layer.weight.requires_grad = True
      new_layer.bias.requires_grad = False
      new_layer.bias.copy_(b.contiguous())
      new_layer.bias.requires_grad = True
      return new_layer
  
  
  def prune_layer(layer, index, dim=None):
      """ Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index.
          Return the pruned layer as a new layer with requires_grad=True.
          Used to remove heads.
      """
      if isinstance(layer, nn.Linear):
          return prune_linear_layer(layer, index, dim=0 if dim is None else dim)
      elif isinstance(layer, Conv1D):
          return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
      else:
          raise ValueError("Can't prune layer of class {}".format(layer.__class__))
  