text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
  #
  # 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
  from __future__ import division
  from __future__ import print_function
  
  import copy
  import json
  import math
  import six
  import torch
  import torch.nn as nn
  from torch.nn import CrossEntropyLoss
  
  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))))
      """
      return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
  
  
  class BertConfig(object):
      """Configuration class to store the configuration of a `BertModel`.
      """
      def __init__(self,
                  vocab_size,
                  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=16,
                  initializer_range=0.02):
          """Constructs BertConfig.
  
          Args:
              vocab_size: 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.
              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.
          """
          self.vocab_size = vocab_size
          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
  
      @classmethod
      def from_dict(cls, json_object):
          """Constructs a `BertConfig` from a Python dictionary of parameters."""
          config = BertConfig(vocab_size=None)
          for (key, value) in six.iteritems(json_object):
              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") as reader:
              text = reader.read()
          return cls.from_dict(json.loads(text))
  
      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"
  
  
  class BERTLayerNorm(nn.Module):
      def __init__(self, config, variance_epsilon=1e-12):
          """Construct a layernorm module in the TF style (epsilon inside the square root).
          """
          super(BERTLayerNorm, self).__init__()
          self.gamma = nn.Parameter(torch.ones(config.hidden_size))
          self.beta = nn.Parameter(torch.zeros(config.hidden_size))
          self.variance_epsilon = variance_epsilon
  
      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.gamma * x + self.beta
  
  class BERTEmbeddings(nn.Module):
      def __init__(self, config):
          super(BERTEmbeddings, self).__init__()
          """Construct the embedding module from word, position and token_type embeddings.
          """
          self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
          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)
          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)
          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)
          self.intermediate_act_fn = gelu
  
      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)
          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):
          all_encoder_layers = []
          for layer_module in self.layer:
              hidden_states = layer_module(hidden_states, attention_mask)
              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):
      """BERT model ("Bidirectional Embedding Representations from a Transformer").
  
      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, 2, 0]])
  
      config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
          num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
  
      model = modeling.BertModel(config=config)
      all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config: BertConfig):
          """Constructor for BertModel.
  
          Args:
              config: `BertConfig` instance.
          """
          super(BertModel, self).__init__()
          self.embeddings = BERTEmbeddings(config)
          self.encoder = BERTEncoder(config)
          self.pooler = BERTPooler(config)
  
      def forward(self, input_ids, token_type_ids=None, attention_mask=None):
          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, from_seq_length]
          # So we can broadcast to [batch_size, num_heads, to_seq_length, from_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.float()
          extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
  
          embedding_output = self.embeddings(input_ids, token_type_ids)
          all_encoder_layers = self.encoder(embedding_output, extended_attention_mask)
          sequence_output = all_encoder_layers[-1]
          pooled_output = self.pooler(sequence_output)
          return all_encoder_layers, pooled_output
  
  class BertForSequenceClassification(nn.Module):
      """BERT model for classification.
      This module is composed of the BERT model with a linear layer on top of
      the pooled output.
  
      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, 2, 0]])
  
      config = BertConfig(vocab_size=32000, hidden_size=512,
          num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
  
      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__()
          self.bert = BertModel(config)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.classifier_1 = nn.Linear(config.hidden_size, 2)
          self.classifier_2 = nn.Linear(config.hidden_size, 2)
          self.classifier_3 = nn.Linear(config.hidden_size, 4)
          self.classifier_4 = nn.Linear(config.hidden_size, 14)
  
          def init_weights(module):
              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=config.initializer_range)
              elif isinstance(module, BERTLayerNorm):
                  module.beta.data.normal_(mean=0.0, std=config.initializer_range)
                  module.gamma.data.normal_(mean=0.0, std=config.initializer_range)
              if isinstance(module, nn.Linear):
                  module.bias.data.zero_()
          self.apply(init_weights)
  
      def forward(self, input_ids, token_type_ids, attention_mask, labels=None, dataset_labels=None):
          _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask)
          pooled_output = self.dropout(pooled_output)
          #print("dataset_label=",dataset_labels[0].item())
          if dataset_labels[0].item()==1:logits = self.classifier_1(pooled_output)
          if dataset_labels[0].item()==2:logits = self.classifier_2(pooled_output)
          if dataset_labels[0].item()==3:logits = self.classifier_3(pooled_output)
          if dataset_labels[0].item()==4:logits = self.classifier_4(pooled_output)
          if labels is not None:
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(logits, labels)
              return loss, logits
          else:
              return logits
  
  
  class BertForQuestionAnswering(nn.Module):
      """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
  
      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, 2, 0]])
  
      config = BertConfig(vocab_size=32000, hidden_size=512,
          num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
  
      model = BertForQuestionAnswering(config)
      start_logits, end_logits = model(input_ids, token_type_ids, input_mask)
      ```
      """
      def __init__(self, config):
          super(BertForQuestionAnswering, self).__init__()
          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)
  
          def init_weights(module):
              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=config.initializer_range)
              elif isinstance(module, BERTLayerNorm):
                  module.beta.data.normal_(mean=0.0, std=config.initializer_range)
                  module.gamma.data.normal_(mean=0.0, std=config.initializer_range)
              if isinstance(module, nn.Linear):
                  module.bias.data.zero_()
          self.apply(init_weights)
  
      def forward(self, input_ids, token_type_ids, attention_mask, start_positions=None, end_positions=None):
          all_encoder_layers, _ = self.bert(input_ids, token_type_ids, attention_mask)
          sequence_output = all_encoder_layers[-1]
          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 not this is a no-op
              start_positions = start_positions.squeeze(-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
  