text: """
  这个页面的代码很大程度上参考(复制粘贴)了https://github.com/huggingface/pytorch-pretrained-BERT的代码， 如果你发现该代码对你
      有用，也请引用一下他们。
  """
  
  __all__ = [
      "BertModel"
  ]
  
  import collections
  
  import unicodedata
  import copy
  import json
  import math
  import os
  
  import torch
  from torch import nn
  import sys
  
  from ..utils import _get_file_name_base_on_postfix
  
  CONFIG_FILE = 'bert_config.json'
  VOCAB_NAME = 'vocab.txt'
  
  
  
  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,
                   layer_norm_eps=1e-12):
          """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.
              layer_norm_eps: The epsilon used by LayerNorm.
          """
          if isinstance(vocab_size_or_config_json_file, str):
              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
              self.layer_norm_eps = layer_norm_eps
          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())
  
  
  def gelu(x):
      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 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=config.layer_norm_eps)
          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))
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
          # 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=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  class BertAttention(nn.Module):
      def __init__(self, config):
          super(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):
              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=config.layer_norm_eps)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(self, hidden_states, input_tensor):
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
          hidden_states = self.LayerNorm(hidden_states + input_tensor)
          return hidden_states
  
  
  class BertLayer(nn.Module):
      def __init__(self, config):
          super(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 BertModel(nn.Module):
      """
      别名：:class:`fastNLP.modules.BertModel`   :class:`fastNLP.modules.encoder.BertModel`
  
      BERT(Bidirectional Embedding Representations from Transformers).
  
      如果你想使用预训练好的权重矩阵，请在以下网址下载.
      sources::
  
      'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin",
      'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-pytorch_model.bin",
      'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-pytorch_model.bin",
      'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-pytorch_model.bin",
      'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-pytorch_model.bin",
      'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-pytorch_model.bin",
      'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-pytorch_model.bin",
      'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-pytorch_model.bin",
      'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-pytorch_model.bin",
      'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-pytorch_model.bin",
      'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-pytorch_model.bin",
      'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-pytorch_model.bin",
      'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-pytorch_model.bin"
  
  
      用预训练权重矩阵来建立BERT模型::
  
          model = BertModel.from_pretrained("path/to/weights/directory")
  
      用随机初始化权重矩阵来建立BERT模型::
  
          model = BertModel()
  
      :param int vocab_size: 词表大小，默认值为30522，为BERT English uncase版本的词表大小
      :param int hidden_size: 隐层大小，默认值为768，为BERT base的版本
      :param int num_hidden_layers: 隐藏层数，默认值为12，为BERT base的版本
      :param int num_attention_heads: 多头注意力头数，默认值为12，为BERT base的版本
      :param int intermediate_size: FFN隐藏层大小，默认值是3072，为BERT base的版本
      :param str hidden_act: FFN隐藏层激活函数，默认值为``gelu``
      :param float hidden_dropout_prob: FFN隐藏层dropout，默认值为0.1
      :param float attention_probs_dropout_prob: Attention层的dropout，默认值为0.1
      :param int max_position_embeddings: 最大的序列长度，默认值为512，
      :param int type_vocab_size: 最大segment数量，默认值为2
      :param int initializer_range: 初始化权重范围，默认值为0.02
      """
  
      def __init__(self, config, *inputs, **kwargs):
          super(BertModel, 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__
                  ))
          super(BertModel, self).__init__()
          self.config = config
          self.hidden_size = self.config.hidden_size
          self.embeddings = BertEmbeddings(config)
          self.encoder = BertEncoder(config)
          self.pooler = BertPooler(config)
          self.apply(self.init_bert_weights)
  
      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_()
  
      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 = (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
  
      @classmethod
      def from_pretrained(cls, pretrained_model_dir, *inputs, **kwargs):
          state_dict = kwargs.get('state_dict', None)
          kwargs.pop('state_dict', None)
          kwargs.pop('cache_dir', None)
          kwargs.pop('from_tf', None)
          # Load config
          config_file = _get_file_name_base_on_postfix(pretrained_model_dir, '.json')
          config = BertConfig.from_json_file(config_file)
          # logger.info("Model config {}".format(config))
          # Instantiate model.
          model = cls(config, *inputs, **kwargs)
          if state_dict is None:
              weights_path = _get_file_name_base_on_postfix(pretrained_model_dir, '.bin')
              state_dict = torch.load(weights_path, map_location='cpu')
  
          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)
  
          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 + '.')
  
          load(model, prefix='' if hasattr(model, 'bert') else 'bert.')
          if len(missing_keys) > 0:
              print("Weights of {} not initialized from pretrained model: {}".format(
                  model.__class__.__name__, missing_keys))
          if len(unexpected_keys) > 0:
              print("Weights from pretrained model not used in {}: {}".format(
                  model.__class__.__name__, unexpected_keys))
          return model
  
  
  def whitespace_tokenize(text):
      """Runs basic whitespace cleaning and splitting on a piece of text."""
      text = text.strip()
      if not text:
          return []
      tokens = text.split()
      return tokens
  
  
  class WordpieceTokenizer(object):
      """Runs WordPiece tokenization."""
  
      def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
          self.vocab = vocab
          self.unk_token = unk_token
          self.max_input_chars_per_word = max_input_chars_per_word
  
      def tokenize(self, text):
          """Tokenizes a piece of text into its word pieces.
  
          This uses a greedy longest-match-first algorithm to perform tokenization
          using the given vocabulary.
  
          For example:
            input = "unaffable"
            output = ["un", "##aff", "##able"]
  
          Args:
            text: A single token or whitespace separated tokens. This should have
              already been passed through `BasicTokenizer`.
  
          Returns:
            A list of wordpiece tokens.
          """
  
          output_tokens = []
          for token in whitespace_tokenize(text):
              chars = list(token)
              if len(chars) > self.max_input_chars_per_word:
                  output_tokens.append(self.unk_token)
                  continue
  
              is_bad = False
              start = 0
              sub_tokens = []
              while start < len(chars):
                  end = len(chars)
                  cur_substr = None
                  while start < end:
                      substr = "".join(chars[start:end])
                      if start > 0:
                          substr = "##" + substr
                      if substr in self.vocab:
                          cur_substr = substr
                          break
                      end -= 1
                  if cur_substr is None:
                      is_bad = True
                      break
                  sub_tokens.append(cur_substr)
                  start = end
  
              if is_bad:
                  output_tokens.append(self.unk_token)
              else:
                  output_tokens.extend(sub_tokens)
          return output_tokens
  
  
  def load_vocab(vocab_file):
      """Loads a vocabulary file into a dictionary."""
      vocab = collections.OrderedDict()
      index = 0
      with open(vocab_file, "r", encoding="utf-8") as reader:
          while True:
              token = reader.readline()
              if not token:
                  break
              token = token.strip()
              vocab[token] = index
              index += 1
      return vocab
  
  
  class BasicTokenizer(object):
      """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
  
      def __init__(self,
                   do_lower_case=True,
                   never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
          """Constructs a BasicTokenizer.
  
          Args:
            do_lower_case: Whether to lower case the input.
          """
          self.do_lower_case = do_lower_case
          self.never_split = never_split
  
      def tokenize(self, text):
          """Tokenizes a piece of text."""
          text = self._clean_text(text)
          # This was added on November 1st, 2018 for the multilingual and Chinese
          # models. This is also applied to the English models now, but it doesn't
          # matter since the English models were not trained on any Chinese data
          # and generally don't have any Chinese data in them (there are Chinese
          # characters in the vocabulary because Wikipedia does have some Chinese
          # words in the English Wikipedia.).
          text = self._tokenize_chinese_chars(text)
          orig_tokens = whitespace_tokenize(text)
          split_tokens = []
          for token in orig_tokens:
              if self.do_lower_case and token not in self.never_split:
                  token = token.lower()
                  token = self._run_strip_accents(token)
              split_tokens.extend(self._run_split_on_punc(token))
  
          output_tokens = whitespace_tokenize(" ".join(split_tokens))
          return output_tokens
  
      def _run_strip_accents(self, text):
          """Strips accents from a piece of text."""
          text = unicodedata.normalize("NFD", text)
          output = []
          for char in text:
              cat = unicodedata.category(char)
              if cat == "Mn":
                  continue
              output.append(char)
          return "".join(output)
  
      def _run_split_on_punc(self, text):
          """Splits punctuation on a piece of text."""
          if text in self.never_split:
              return [text]
          chars = list(text)
          i = 0
          start_new_word = True
          output = []
          while i < len(chars):
              char = chars[i]
              if _is_punctuation(char):
                  output.append([char])
                  start_new_word = True
              else:
                  if start_new_word:
                      output.append([])
                  start_new_word = False
                  output[-1].append(char)
              i += 1
  
          return ["".join(x) for x in output]
  
      def _tokenize_chinese_chars(self, text):
          """Adds whitespace around any CJK character."""
          output = []
          for char in text:
              cp = ord(char)
              if self._is_chinese_char(cp):
                  output.append(" ")
                  output.append(char)
                  output.append(" ")
              else:
                  output.append(char)
          return "".join(output)
  
      def _is_chinese_char(self, cp):
          """Checks whether CP is the codepoint of a CJK character."""
          # This defines a "chinese character" as anything in the CJK Unicode block:
          #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
          #
          # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
          # despite its name. The modern Korean Hangul alphabet is a different block,
          # as is Japanese Hiragana and Katakana. Those alphabets are used to write
          # space-separated words, so they are not treated specially and handled
          # like the all of the other languages.
          if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
                  (cp >= 0x3400 and cp <= 0x4DBF) or  #
                  (cp >= 0x20000 and cp <= 0x2A6DF) or  #
                  (cp >= 0x2A700 and cp <= 0x2B73F) or  #
                  (cp >= 0x2B740 and cp <= 0x2B81F) or  #
                  (cp >= 0x2B820 and cp <= 0x2CEAF) or
                  (cp >= 0xF900 and cp <= 0xFAFF) or  #
                  (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
              return True
  
          return False
  
      def _clean_text(self, text):
          """Performs invalid character removal and whitespace cleanup on text."""
          output = []
          for char in text:
              cp = ord(char)
              if cp == 0 or cp == 0xfffd or _is_control(char):
                  continue
              if _is_whitespace(char):
                  output.append(" ")
              else:
                  output.append(char)
          return "".join(output)
  
  
  def _is_whitespace(char):
      """Checks whether `chars` is a whitespace character."""
      # \t, \n, and \r are technically contorl characters but we treat them
      # as whitespace since they are generally considered as such.
      if char == " " or char == "\t" or char == "\n" or char == "\r":
          return True
      cat = unicodedata.category(char)
      if cat == "Zs":
          return True
      return False
  
  
  def _is_control(char):
      """Checks whether `chars` is a control character."""
      # These are technically control characters but we count them as whitespace
      # characters.
      if char == "\t" or char == "\n" or char == "\r":
          return False
      cat = unicodedata.category(char)
      if cat.startswith("C"):
          return True
      return False
  
  
  def _is_punctuation(char):
      """Checks whether `chars` is a punctuation character."""
      cp = ord(char)
      # We treat all non-letter/number ASCII as punctuation.
      # Characters such as "^", "$", and "`" are not in the Unicode
      # Punctuation class but we treat them as punctuation anyways, for
      # consistency.
      if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
              (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
          return True
      cat = unicodedata.category(char)
      if cat.startswith("P"):
          return True
      return False
  
  
  class BertTokenizer(object):
      """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
  
      def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
                   never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
          """Constructs a BertTokenizer.
  
          Args:
            vocab_file: Path to a one-wordpiece-per-line vocabulary file
            do_lower_case: Whether to lower case the input
                           Only has an effect when do_wordpiece_only=False
            do_basic_tokenize: Whether to do basic tokenization before wordpiece.
            max_len: An artificial maximum length to truncate tokenized sequences to;
                           Effective maximum length is always the minimum of this
                           value (if specified) and the underlying BERT model's
                           sequence length.
            never_split: List of tokens which will never be split during tokenization.
                           Only has an effect when do_wordpiece_only=False
          """
          if not os.path.isfile(vocab_file):
              raise ValueError(
                  "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
                  "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
          self.vocab = load_vocab(vocab_file)
          self.ids_to_tokens = collections.OrderedDict(
              [(ids, tok) for tok, ids in self.vocab.items()])
          self.do_basic_tokenize = do_basic_tokenize
          if do_basic_tokenize:
              self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
                                                    never_split=never_split)
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
          self.max_len = max_len if max_len is not None else int(1e12)
  
      def _reinit_on_new_vocab(self, vocab):
          """
          在load bert之后，可能会对vocab进行重新排列。重新排列之后调用这个函数重新初始化与vocab相关的性质
  
          :param vocab:
          :return:
          """
          self.vocab = vocab
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
  
      def tokenize(self, text):
          split_tokens = []
          if self.do_basic_tokenize:
              for token in self.basic_tokenizer.tokenize(text):
                  for sub_token in self.wordpiece_tokenizer.tokenize(token):
                      split_tokens.append(sub_token)
          else:
              split_tokens = self.wordpiece_tokenizer.tokenize(text)
          return split_tokens
  
      def convert_tokens_to_ids(self, tokens):
          """Converts a sequence of tokens into ids using the vocab."""
          ids = []
          for token in tokens:
              ids.append(self.vocab[token])
          if len(ids) > self.max_len:
              print(
                  "Token indices sequence length is longer than the specified maximum "
                  " sequence length for this BERT model ({} > {}). Running this"
                  " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
              )
          return ids
  
      def convert_ids_to_tokens(self, ids):
          """Converts a sequence of ids in wordpiece tokens using the vocab."""
          tokens = []
          for i in ids:
              tokens.append(self.ids_to_tokens[i])
          return tokens
  
      def save_vocabulary(self, vocab_path):
          """Save the tokenizer vocabulary to a directory or file."""
          index = 0
          if os.path.isdir(vocab_path):
              vocab_file = os.path.join(vocab_path, VOCAB_NAME)
          else:
              vocab_file = vocab_path
          with open(vocab_file, "w", encoding="utf-8") as writer:
              for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
                  if index != token_index:
                      print("Saving vocabulary to {}: vocabulary indices are not consecutive."
                            " Please check that the vocabulary is not corrupted!".format(vocab_file))
                      index = token_index
                  writer.write(token + u'\n')
                  index += 1
          return vocab_file
  
      @classmethod
      def from_pretrained(cls, model_dir, *inputs, **kwargs):
          """
          给定path，直接读取vocab.
  
          """
          pretrained_model_name_or_path = _get_file_name_base_on_postfix(model_dir, '.txt')
          print("loading vocabulary file {}".format(pretrained_model_name_or_path))
          max_len = 512
          kwargs['max_len'] = min(kwargs.get('max_position_embeddings', int(1e12)), max_len)
          # Instantiate tokenizer.
          tokenizer = cls(pretrained_model_name_or_path, *inputs, **kwargs)
          return tokenizer
  
  class _WordPieceBertModel(nn.Module):
      """
      这个模块用于直接计算word_piece的结果.
  
      """
  
      def __init__(self, model_dir: str, layers: str = '-1'):
          super().__init__()
  
          self.tokenzier = BertTokenizer.from_pretrained(model_dir)
          self.encoder = BertModel.from_pretrained(model_dir)
          #  检查encoder_layer_number是否合理
          encoder_layer_number = len(self.encoder.encoder.layer)
          self.layers = list(map(int, layers.split(',')))
          for layer in self.layers:
              if layer < 0:
                  assert -layer <= encoder_layer_number, f"The layer index:{layer} is out of scope for " \
                      f"a bert model with {encoder_layer_number} layers."
              else:
                  assert layer < encoder_layer_number, f"The layer index:{layer} is out of scope for " \
                      f"a bert model with {encoder_layer_number} layers."
  
          self._cls_index = self.tokenzier.vocab['[CLS]']
          self._sep_index = self.tokenzier.vocab['[SEP]']
          self._wordpiece_pad_index = self.tokenzier.vocab['[PAD]']  # 需要用于生成word_piece
  
      def index_dataset(self, *datasets, field_name):
          """
          使用bert的tokenizer新生成word_pieces列加入到datasets中，并将他们设置为input。如果首尾不是
              [CLS]与[SEP]会在首尾额外加入[CLS]与[SEP], 且将word_pieces这一列的pad value设置为了bert的pad value。
  
          :param datasets: DataSet对象
          :param field_name: 基于哪一列index
          :return:
          """
  
          def convert_words_to_word_pieces(words):
              word_pieces = []
              for word in words:
                  tokens = self.tokenzier.wordpiece_tokenizer.tokenize(word)
                  word_piece_ids = self.tokenzier.convert_tokens_to_ids(tokens)
                  word_pieces.extend(word_piece_ids)
              if word_pieces[0] != self._cls_index:
                  word_pieces.insert(0, self._cls_index)
              if word_pieces[-1] != self._sep_index:
                  word_pieces.insert(-1, self._sep_index)
              return word_pieces
  
          for index, dataset in enumerate(datasets):
              try:
                  dataset.apply_field(convert_words_to_word_pieces, field_name=field_name, new_field_name='word_pieces',
                                      is_input=True)
                  dataset.set_pad_val('word_pieces', self._wordpiece_pad_index)
              except Exception as e:
                  print(f"Exception happens when processing the {index} dataset.")
                  raise e
  
      def forward(self, word_pieces, token_type_ids=None):
          """
  
          :param word_pieces: torch.LongTensor, batch_size x max_len
          :param token_type_ids: torch.LongTensor, batch_size x max_len
          :return: num_layers x batch_size x max_len x hidden_size或者num_layers x batch_size x (max_len+2) x hidden_size
          """
          batch_size, max_len = word_pieces.size()
  
          attn_masks = word_pieces.ne(self._wordpiece_pad_index)
          bert_outputs, _ = self.encoder(word_pieces, token_type_ids=token_type_ids, attention_mask=attn_masks,
                                         output_all_encoded_layers=True)
          # output_layers = [self.layers]  # len(self.layers) x batch_size x max_word_piece_length x hidden_size
          outputs = bert_outputs[0].new_zeros((len(self.layers), batch_size, max_len, bert_outputs[0].size(-1)))
          for l_index, l in enumerate(self.layers):
              outputs[l_index] = bert_outputs[l]
          return outputs
  