text: # coding=utf-8
  # Copyright (c) 2019, Facebook, Inc. and its affiliates. All Rights Reserved
  # 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."""
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import copy
  import json
  import logging
  import math
  import os
  import shutil
  import tarfile
  import tempfile
  import sys
  from io import open
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  
  from .file_utils import cached_path, WEIGHTS_NAME, CONFIG_NAME
  
  logger = logging.getLogger(__name__)
  
  PRETRAINED_MODEL_ARCHIVE_MAP = {
      'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
      'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
      'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
      'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
      'spanbert-base-cased': "https://dl.fbaipublicfiles.com/fairseq/models/spanbert_hf_base.tar.gz",
      'spanbert-large-cased': "https://dl.fbaipublicfiles.com/fairseq/models/spanbert_hf.tar.gz"
  }
  
  BERT_CONFIG_NAME = 'bert_config.json'
  TF_WEIGHTS_NAME = 'model.ckpt'
  
  def load_tf_weights_in_bert(model, tf_checkpoint_path):
      """ Load tf checkpoints in a pytorch model
      """
      try:
          import re
          import numpy as np
          import tensorflow as tf
      except ImportError:
          print("Loading a TensorFlow models 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)
      print("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:
          print("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", "global_step"] for n in name):
              print("Skipping {}".format("/".join(name)))
              continue
          pointer = model
          for m_name in name:
              if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
                  l = re.split(r'_(\d+)', m_name)
              else:
                  l = [m_name]
              if l[0] == 'kernel' or l[0] == 'gamma':
                  pointer = getattr(pointer, 'weight')
              elif l[0] == 'output_bias' or l[0] == 'beta':
                  pointer = getattr(pointer, 'bias')
              elif l[0] == 'output_weights':
                  pointer = getattr(pointer, 'weight')
              elif l[0] == 'squad':
                  pointer = getattr(pointer, 'classifier')
              else:
                  try:
                      pointer = getattr(pointer, l[0])
                  except AttributeError:
                      print("Skipping {}".format("/".join(name)))
                      continue
              if len(l) >= 2:
                  num = int(l[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
          print("Initialize PyTorch weight {}".format(name))
          pointer.data = torch.from_numpy(array)
      return model
  
  
  def gelu(x):
      """Implementation of the gelu activation function.
          For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
          0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
          Also see https://arxiv.org/abs/1606.08415
      """
      return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
  
  
  def swish(x):
      return x * torch.sigmoid(x)
  
  
  ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
  
  
  class BertConfig(object):
      """Configuration class to store the configuration of a `BertModel`.
      """
      def __init__(self,
                   vocab_size_or_config_json_file,
                   hidden_size=768,
                   num_hidden_layers=12,
                   num_attention_heads=12,
                   intermediate_size=3072,
                   hidden_act="gelu",
                   hidden_dropout_prob=0.1,
                   attention_probs_dropout_prob=0.1,
                   max_position_embeddings=512,
                   type_vocab_size=2,
                   initializer_range=0.02):
          """Constructs BertConfig.
  
          Args:
              vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
              hidden_size: Size of the encoder layers and the pooler layer.
              num_hidden_layers: Number of hidden layers in the Transformer encoder.
              num_attention_heads: Number of attention heads for each attention layer in
                  the Transformer encoder.
              intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                  layer in the Transformer encoder.
              hidden_act: The non-linear activation function (function or string) in the
                  encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
              hidden_dropout_prob: The dropout probabilitiy for all fully connected
                  layers in the embeddings, encoder, and pooler.
              attention_probs_dropout_prob: The dropout ratio for the attention
                  probabilities.
              max_position_embeddings: The maximum sequence length that this model might
                  ever be used with. Typically set this to something large just in case
                  (e.g., 512 or 1024 or 2048).
              type_vocab_size: The vocabulary size of the `token_type_ids` passed into
                  `BertModel`.
              initializer_range: The sttdev of the truncated_normal_initializer for
                  initializing all weight matrices.
          """
          if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
                          and isinstance(vocab_size_or_config_json_file, unicode)):
              with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
                  json_config = json.loads(reader.read())
              for key, value in json_config.items():
                  self.__dict__[key] = value
          elif isinstance(vocab_size_or_config_json_file, int):
              self.vocab_size = vocab_size_or_config_json_file
              self.hidden_size = hidden_size
              self.num_hidden_layers = num_hidden_layers
              self.num_attention_heads = num_attention_heads
              self.hidden_act = hidden_act
              self.intermediate_size = intermediate_size
              self.hidden_dropout_prob = hidden_dropout_prob
              self.attention_probs_dropout_prob = attention_probs_dropout_prob
              self.max_position_embeddings = max_position_embeddings
              self.type_vocab_size = type_vocab_size
              self.initializer_range = initializer_range
          else:
              raise ValueError("First argument must be either a vocabulary size (int)"
                               "or the path to a pretrained model config file (str)")
  
      @classmethod
      def from_dict(cls, json_object):
          """Constructs a `BertConfig` from a Python dictionary of parameters."""
          config = BertConfig(vocab_size_or_config_json_file=-1)
          for key, value in json_object.items():
              config.__dict__[key] = value
          return config
  
      @classmethod
      def from_json_file(cls, json_file):
          """Constructs a `BertConfig` from a json file of parameters."""
          with open(json_file, "r", encoding='utf-8') as reader:
              text = reader.read()
          return cls.from_dict(json.loads(text))
  
      def __repr__(self):
          return str(self.to_json_string())
  
      def to_dict(self):
          """Serializes this instance to a Python dictionary."""
          output = copy.deepcopy(self.__dict__)
          return output
  
      def to_json_string(self):
          """Serializes this instance to a JSON string."""
          return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
  
      def to_json_file(self, json_file_path):
          """ Save this instance to a json file."""
          with open(json_file_path, "w", encoding='utf-8') as writer:
              writer.write(self.to_json_string())
  
  # try:
  #     from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
  # except ImportError:
  #     logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
  class BertLayerNorm(nn.Module):
      def __init__(self, hidden_size, eps=1e-12):
          """Construct a layernorm module in the TF style (epsilon inside the square root).
          """
          super(BertLayerNorm, self).__init__()
          self.weight = nn.Parameter(torch.ones(hidden_size))
          self.bias = nn.Parameter(torch.zeros(hidden_size))
          self.variance_epsilon = eps
  
      def forward(self, x):
          u = x.mean(-1, keepdim=True)
          s = (x - u).pow(2).mean(-1, keepdim=True)
          x = (x - u) / torch.sqrt(s + self.variance_epsilon)
          return self.weight * x + self.bias
  
  class BertEmbeddings(nn.Module):
      """Construct the embeddings from word, position and token_type embeddings.
      """
      def __init__(self, config):
          super(BertEmbeddings, self).__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=1e-12)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, input_ids, token_type_ids=None):
          seq_length = input_ids.size(1)
          position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
          position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
          if token_type_ids is None:
              token_type_ids = torch.zeros_like(input_ids)
  
          words_embeddings = self.word_embeddings(input_ids)
          position_embeddings = self.position_embeddings(position_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = words_embeddings + position_embeddings + token_type_embeddings
          embeddings = self.LayerNorm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  class BertSelfAttention(nn.Module):
      def __init__(self, config):
          super(BertSelfAttention, self).__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.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):
          mixed_query_layer = self.query(hidden_states)
          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))
          # assert not torch.isnan(attention_scores).any()
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
          # assert not torch.isnan(attention_scores).any()
          attention_scores = torch.clamp(attention_scores, -10000., 10000.)
          # 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)
  
          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)
          return context_layer
  
  
  class BertSelfOutput(nn.Module):
      def __init__(self, config):
          super(BertSelfOutput, self).__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
          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(BertAttention, self).__init__()
          self.self = BertSelfAttention(config)
          self.output = BertSelfOutput(config)
  
      def forward(self, input_tensor, attention_mask):
          self_output = self.self(input_tensor, attention_mask)
          attention_output = self.output(self_output, input_tensor)
          return attention_output
  
  
  class BertIntermediate(nn.Module):
      def __init__(self, config):
          super(BertIntermediate, self).__init__()
          self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
          if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
              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(BertOutput, self).__init__()
          self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
          self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
          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(BertLayer, self).__init__()
          self.attention = BertAttention(config)
          self.intermediate = BertIntermediate(config)
          self.output = BertOutput(config)
  
      def forward(self, hidden_states, attention_mask):
          attention_output = self.attention(hidden_states, attention_mask)
          intermediate_output = self.intermediate(attention_output)
          layer_output = self.output(intermediate_output, attention_output)
          return layer_output
  
  
  class BertEncoder(nn.Module):
      def __init__(self, config):
          super(BertEncoder, self).__init__()
          layer = BertLayer(config)
          self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
  
      def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
          all_encoder_layers = []
          for layer_module in self.layer:
              hidden_states = layer_module(hidden_states, attention_mask)
              if output_all_encoded_layers:
                  all_encoder_layers.append(hidden_states)
          if not output_all_encoded_layers:
              all_encoder_layers.append(hidden_states)
          return all_encoder_layers
  
  
  class BertPooler(nn.Module):
      def __init__(self, config):
          super(BertPooler, self).__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(BertPredictionHeadTransform, self).__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
              self.transform_act_fn = ACT2FN[config.hidden_act]
          else:
              self.transform_act_fn = config.hidden_act
          self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
  
      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, bert_model_embedding_weights):
          super(BertLMPredictionHead, self).__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(bert_model_embedding_weights.size(1),
                                   bert_model_embedding_weights.size(0),
                                   bias=False)
          self.decoder.weight = bert_model_embedding_weights
          self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
  
      def forward(self, hidden_states):
          hidden_states = self.transform(hidden_states)
          hidden_states = self.decoder(hidden_states) + self.bias
          return hidden_states
  
  
  class BertOnlyMLMHead(nn.Module):
      def __init__(self, config, bert_model_embedding_weights):
          super(BertOnlyMLMHead, self).__init__()
          self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
  
      def forward(self, sequence_output):
          prediction_scores = self.predictions(sequence_output)
          return prediction_scores
  
  
  class BertOnlyNSPHead(nn.Module):
      def __init__(self, config):
          super(BertOnlyNSPHead, self).__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, bert_model_embedding_weights):
          super(BertPreTrainingHeads, self).__init__()
          self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
          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(nn.Module):
      """ An abstract class to handle weights initialization and
          a simple interface for dowloading and loading pretrained models.
      """
      def __init__(self, config, *inputs, **kwargs):
          super(BertPreTrainedModel, self).__init__()
          if not isinstance(config, BertConfig):
              raise ValueError(
                  "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
                  "To create a model from a Google pretrained model use "
                  "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
                      self.__class__.__name__, self.__class__.__name__
                  ))
          self.config = config
  
      def init_bert_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_()
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
                          from_tf=False, *inputs, **kwargs):
          """
          Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
          Download and cache the pre-trained model file if needed.
  
          Params:
              pretrained_model_name_or_path: either:
                  - a str with the name of a pre-trained model to load selected in the list of:
                      . `bert-base-uncased`
                      . `bert-large-uncased`
                      . `bert-base-cased`
                      . `bert-large-cased`
                      . `bert-base-multilingual-uncased`
                      . `bert-base-multilingual-cased`
                      . `bert-base-chinese`
                  - a path or url to a pretrained model archive containing:
                      . `bert_config.json` a configuration file for the model
                      . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
                  - a path or url to a pretrained model archive containing:
                      . `bert_config.json` a configuration file for the model
                      . `model.chkpt` a TensorFlow checkpoint
              from_tf: should we load the weights from a locally saved TensorFlow checkpoint
              cache_dir: an optional path to a folder in which the pre-trained models will be cached.
              state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
              *inputs, **kwargs: additional input for the specific Bert class
                  (ex: num_labels for BertForSequenceClassification)
          """
          if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
              archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
          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)
          except EnvironmentError:
              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(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
                      archive_file))
              return None
          if resolved_archive_file == archive_file:
              logger.info("loading archive file {}".format(archive_file))
          else:
              logger.info("loading archive file {} from cache at {}".format(
                  archive_file, resolved_archive_file))
          tempdir = None
          if os.path.isdir(resolved_archive_file) or from_tf:
              serialization_dir = resolved_archive_file
          else:
              # Extract archive to temp dir
              tempdir = tempfile.mkdtemp()
              logger.info("extracting archive file {} to temp dir {}".format(
                  resolved_archive_file, tempdir))
              with tarfile.open(resolved_archive_file, 'r:gz') as archive:
                  archive.extractall(tempdir)
              serialization_dir = tempdir
          # Load config
          config_file = os.path.join(serialization_dir, CONFIG_NAME)
          if not os.path.exists(config_file):
              # Backward compatibility with old naming format
              config_file = os.path.join(serialization_dir, BERT_CONFIG_NAME)
          config = BertConfig.from_json_file(config_file)
          if pretrained_model_name_or_path == 'bert-base-uncased-1024':
              config.max_position_embeddings = 1024
          logger.info("Model config {}".format(config))
          # Instantiate model.
          model = cls(config, *inputs, **kwargs)
          if state_dict is None and not from_tf:
              weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
              state_dict = torch.load(weights_path, map_location='cpu')
          if tempdir:
              # Clean up temp dir
              shutil.rmtree(tempdir)
          if from_tf:
              # Directly load from a TensorFlow checkpoint
              weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
              return load_tf_weights_in_bert(model, weights_path)
          # Load from a PyTorch state_dict
          old_keys = []
          new_keys = []
          for key in state_dict.keys():
              new_key = key[8:] if key.startswith("decoder.") else key
              if 'gamma' in new_key:
                  new_key = new_key.replace('gamma', 'weight')
              if 'beta' in new_key:
                  new_key = new_key.replace('beta', 'bias')
              if key != 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)
  
          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 + '.')
          start_prefix = ''
          if not hasattr(model, 'bert') and any(s.startswith('bert.') for s in state_dict.keys()):
              start_prefix = 'bert.'
          load(model, 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)))
          return model
  
  
  class BertModel(BertPreTrainedModel):
      """BERT model ("Bidirectional Embedding Representations from a Transformer").
  
      Params:
          config: a BertConfig class instance with the configuration to build a new model
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
  
      Outputs: Tuple of (encoded_layers, pooled_output)
          `encoded_layers`: controled by `output_all_encoded_layers` argument:
              - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
                  of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
                  encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
              - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
                  to the last attention block of shape [batch_size, sequence_length, hidden_size],
          `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
              classifier pretrained on top of the hidden state associated to the first character of the
              input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      model = modeling.BertModel(config=config)
      all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config):
          super(BertModel, self).__init__(config)
          self.embeddings = BertEmbeddings(config)
          self.encoder = BertEncoder(config)
          self.pooler = BertPooler(config)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True):
          if attention_mask is None:
              attention_mask = torch.ones_like(input_ids)
          if token_type_ids is None:
              token_type_ids = torch.zeros_like(input_ids)
  
          # We create a 3D attention mask from a 2D tensor mask.
          # Sizes are [batch_size, 1, 1, to_seq_length]
          # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
          # this attention mask is more simple than the triangular masking of causal attention
          # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
          extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
  
          # 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 = extended_attention_mask.to(dtype=torch.float32)
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
  
          embedding_output = self.embeddings(input_ids, token_type_ids)
          encoded_layers = self.encoder(embedding_output,
                                        extended_attention_mask,
                                        output_all_encoded_layers=output_all_encoded_layers)
          sequence_output = encoded_layers[-1]
          pooled_output = self.pooler(sequence_output)
          if not output_all_encoded_layers:
              encoded_layers = encoded_layers[-1]
          return encoded_layers, pooled_output
  
  
  class BertForPreTraining(BertPreTrainedModel):
      """BERT model with pre-training heads.
      This module comprises the BERT model followed by the two pre-training heads:
          - the masked language modeling head, and
          - the next sentence classification head.
  
      Params:
          config: a BertConfig class instance with the configuration to build a new model.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
              with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
              is only computed for the labels set in [0, ..., vocab_size]
          `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size]
              with indices selected in [0, 1].
              0 => next sentence is the continuation, 1 => next sentence is a random sentence.
  
      Outputs:
          if `masked_lm_labels` and `next_sentence_label` are not `None`:
              Outputs the total_loss which is the sum of the masked language modeling loss and the next
              sentence classification loss.
          if `masked_lm_labels` or `next_sentence_label` is `None`:
              Outputs a tuple comprising
              - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
              - the next sentence classification logits of shape [batch_size, 2].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      model = BertForPreTraining(config)
      masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config):
          super(BertForPreTraining, self).__init__(config)
          self.bert = BertModel(config)
          self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None):
          sequence_output, pooled_output = self.bert(input_ids, token_type_ids, attention_mask,
                                                     output_all_encoded_layers=False)
          prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
  
          if masked_lm_labels is not None and next_sentence_label is not None:
              loss_fct = CrossEntropyLoss(ignore_index=-1)
              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
              return total_loss
          else:
              return prediction_scores, seq_relationship_score
  
  
  class BertForMaskedLM(BertPreTrainedModel):
      """BERT model with the masked language modeling head.
      This module comprises the BERT model followed by the masked language modeling head.
  
      Params:
          config: a BertConfig class instance with the configuration to build a new model.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
              with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
              is only computed for the labels set in [0, ..., vocab_size]
  
      Outputs:
          if `masked_lm_labels` is  not `None`:
              Outputs the masked language modeling loss.
          if `masked_lm_labels` is `None`:
              Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      model = BertForMaskedLM(config)
      masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config):
          super(BertForMaskedLM, self).__init__(config)
          self.bert = BertModel(config)
          self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None):
          sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask,
                                         output_all_encoded_layers=False)
          prediction_scores = self.cls(sequence_output)
  
          if masked_lm_labels is not None:
              loss_fct = CrossEntropyLoss(ignore_index=-1)
              masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
              return masked_lm_loss
          else:
              return prediction_scores
  
  
  class BertForNextSentencePrediction(BertPreTrainedModel):
      """BERT model with next sentence prediction head.
      This module comprises the BERT model followed by the next sentence classification head.
  
      Params:
          config: a BertConfig class instance with the configuration to build a new model.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
              with indices selected in [0, 1].
              0 => next sentence is the continuation, 1 => next sentence is a random sentence.
  
      Outputs:
          if `next_sentence_label` is not `None`:
              Outputs the total_loss which is the sum of the masked language modeling loss and the next
              sentence classification loss.
          if `next_sentence_label` is `None`:
              Outputs the next sentence classification logits of shape [batch_size, 2].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      model = BertForNextSentencePrediction(config)
      seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config):
          super(BertForNextSentencePrediction, self).__init__(config)
          self.bert = BertModel(config)
          self.cls = BertOnlyNSPHead(config)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None):
          _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask,
                                       output_all_encoded_layers=False)
          seq_relationship_score = self.cls( pooled_output)
  
          if next_sentence_label is not None:
              loss_fct = CrossEntropyLoss(ignore_index=-1)
              next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
              return next_sentence_loss
          else:
              return seq_relationship_score
  
  
  class BertForSequenceClassification(BertPreTrainedModel):
      """BERT model for classification.
      This module is composed of the BERT model with a linear layer on top of
      the pooled output.
  
      Params:
          `config`: a BertConfig class instance with the configuration to build a new model.
          `num_labels`: the number of classes for the classifier. Default = 2.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary. Items in the batch should begin with the special "CLS" token. (see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
              with indices selected in [0, ..., num_labels].
  
      Outputs:
          if `labels` is not `None`:
              Outputs the CrossEntropy classification loss of the output with the labels.
          if `labels` is `None`:
              Outputs the classification logits of shape [batch_size, num_labels].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      num_labels = 2
  
      model = BertForSequenceClassification(config, num_labels)
      logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config, num_labels):
          super(BertForSequenceClassification, self).__init__(config)
          self.num_labels = num_labels
          self.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, num_labels)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
          _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
  
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              return loss
          else:
              return logits
  
  
  class BertForMultipleChoice(BertPreTrainedModel):
      """BERT model for multiple choice tasks.
      This module is composed of the BERT model with a linear layer on top of
      the pooled output.
  
      Params:
          `config`: a BertConfig class instance with the configuration to build a new model.
          `num_choices`: the number of classes for the classifier. Default = 2.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
              with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
              and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
              with indices selected in [0, ..., num_choices].
  
      Outputs:
          if `labels` is not `None`:
              Outputs the CrossEntropy classification loss of the output with the labels.
          if `labels` is `None`:
              Outputs the classification logits of shape [batch_size, num_labels].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
      input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
      token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      num_choices = 2
  
      model = BertForMultipleChoice(config, num_choices)
      logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config, num_choices):
          super(BertForMultipleChoice, self).__init__(config)
          self.num_choices = num_choices
          self.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, 1)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
          flat_input_ids = input_ids.view(-1, input_ids.size(-1))
          flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
          flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
          _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False)
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)
          reshaped_logits = logits.view(-1, self.num_choices)
  
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(reshaped_logits, labels)
              return loss
          else:
              return reshaped_logits
  
  
  class BertForTokenClassification(BertPreTrainedModel):
      """BERT model for token-level classification.
      This module is composed of the BERT model with a linear layer on top of
      the full hidden state of the last layer.
  
      Params:
          `config`: a BertConfig class instance with the configuration to build a new model.
          `num_labels`: the number of classes for the classifier. Default = 2.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length]
              with indices selected in [0, ..., num_labels].
  
      Outputs:
          if `labels` is not `None`:
              Outputs the CrossEntropy classification loss of the output with the labels.
          if `labels` is `None`:
              Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      num_labels = 2
  
      model = BertForTokenClassification(config, num_labels)
      logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config, num_labels):
          super(BertForTokenClassification, self).__init__(config)
          self.num_labels = num_labels
          self.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, num_labels)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
          sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          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_loss]
                  active_labels = labels.view(-1)[active_loss]
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              return loss
          else:
              return logits
  
  
  class BertForTriggerClassification(BertPreTrainedModel):
      """BERT model for token-level classification.
      This module is composed of the BERT model with a linear layer on top of
      the full hidden state of the last layer.
  
      Params:
          `config`: a BertConfig class instance with the configuration to build a new model.
          `num_labels`: the number of classes for the classifier. Default = 2.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length]
              with indices selected in [0, ..., num_labels].
  
      Outputs:
          if `labels` is not `None`:
              Outputs the CrossEntropy classification loss of the output with the labels.
          if `labels` is `None`:
              Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      num_labels = 2
  
      model = BertForTokenClassification(config, num_labels)
      logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config, num_labels):
          super(BertForTriggerClassification, self).__init__(config)
          self.num_labels = num_labels
          self.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier = nn.Linear(config.hidden_size, num_labels)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
          sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          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_loss]
                  active_labels = labels.view(-1)[active_loss]
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              return loss
          else:
              return logits
  
  
  class BertLSTMForTriggerClassification(BertPreTrainedModel):
      """BERT LSTM model for token-level classification.
      """
      def __init__(self, config, num_labels):
          super(BertLSTMForTriggerClassification, self).__init__(config)
          self.num_labels = num_labels
          self.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob) 
  
          # newly add lstm initialization
          self.hidden_dim = 200
          self.lstm = nn.LSTM(config.hidden_size, self.hidden_dim // 2, num_layers=2, batch_first=True, bidirectional=True)
          self.droplstm = nn.Dropout(0.1)
  
          self.classifier = nn.Linear(self.hidden_dim, num_labels)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
          # bert layer
          self.bert.eval()
          with torch.no_grad():
              sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
          sequence_output = self.dropout(sequence_output)
  
          # pass through LSTM layer
          ## option 1 (not working)
          # hidden = None
          # lstm_out, _ = self.lstm(sequence_output, hidden)
          # lstm_out = self.droplstm(lstm_out)
          ## option 2 w/ (un)packing
          seq_lengths = attention_mask.sum(dim=1).cpu().numpy()
          packed_words = pack_padded_sequence(sequence_output, seq_lengths, batch_first=True)
          hidden = None
          lstm_out, hidden = self.lstm(packed_words, hidden)
          lstm_out, _ = pad_packed_sequence(lstm_out, total_length=int(input_ids.size(1)))
              ## lstm_out (seq_len, seq_len, hidden_size)
          feature_out = self.droplstm(lstm_out.transpose(1,0))
        
          # get logits
          logits = self.classifier(feature_out)
  
          # import ipdb; ipdb.set_trace()
  
          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_loss]
                  active_labels = labels.view(-1)[active_loss]
                  loss = loss_fct(active_logits, active_labels)
              else:
                  loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
              return loss
          else:
              return logits
  
  
  class BertForQuestionAnswering(BertPreTrainedModel):
      """BERT model for Question Answering (span extraction).
      This module is composed of the BERT model with a linear layer on top of
      the sequence output that computes start_logits and end_logits
  
      Params:
          `config`: a BertConfig class instance with the configuration to build a new model.
  
      Inputs:
          `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
              with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
              `extract_features.py`, `run_classifier.py` and `run_squad.py`)
          `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
              types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
              a `sentence B` token (see BERT paper for more details).
          `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
              selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
              input sequence length in the current batch. It's the mask that we typically use for attention when
              a batch has varying length sentences.
          `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size].
              Positions are clamped to the length of the sequence and position outside of the sequence are not taken
              into account for computing the loss.
          `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size].
              Positions are clamped to the length of the sequence and position outside of the sequence are not taken
              into account for computing the loss.
  
      Outputs:
          if `start_positions` and `end_positions` are not `None`:
              Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
          if `start_positions` or `end_positions` is `None`:
              Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
              position tokens of shape [batch_size, sequence_length].
  
      Example usage:
      ```python
      # Already been converted into WordPiece token ids
      input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
      input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
      token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
  
      config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
          num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
  
      model = BertForQuestionAnswering(config)
      start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config):
          super(BertForQuestionAnswering, self).__init__(config)
          self.bert = BertModel(config)
          # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
          # self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.qa_outputs = nn.Linear(config.hidden_size, 2)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None):
          sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
          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)
  
          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
              return total_loss
          else:
              return start_logits, end_logits
  
  class BertForQuestionAnswering_withIfTriggerEmbedding(BertPreTrainedModel):
      """BERT model for Question Answering (span extraction). with if_trigger_embedding
      """
      def __init__(self, config):
          super(BertForQuestionAnswering_withIfTriggerEmbedding, self).__init__(config)
          self.bert = BertModel(config)
          self.if_trigger_embedding = nn.Embedding(2, config.hidden_size // 10)
  
          # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version
          # self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.qa_outputs = nn.Linear(config.hidden_size + config.hidden_size // 10, 2)
          self.apply(self.init_bert_weights)
  
      def forward(self, input_ids, if_trigger_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None):
          sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
          if_trigger_embedding_output = self.if_trigger_embedding(if_trigger_ids)
  
          # import ipdb; ipdb.set_trace()
          logits = self.qa_outputs(torch.cat([sequence_output, if_trigger_embedding_output], dim=2))
          start_logits, end_logits = logits.split(1, dim=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          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
              return total_loss
          else:
              return start_logits, end_logits
  