Bert的位置编码：position_embeddings（绝对位置可学习参数式编码）

港控/mmm° 2023-10-15 11:22 50阅读 0赞

position\_embeddings（绝对位置可学习参数式编码）

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)  # 32
            position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)  # torch.Size([32])
            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)   # torch.Size([32])---->torch.Size([128, 32])
            if token_type_ids is None:
                token_type_ids = torch.zeros_like(input_ids)
    
            words_embeddings = self.word_embeddings(input_ids)  # torch.Size([128, 32])---->torch.Size([128, 32, 768])
            position_embeddings = self.position_embeddings(position_ids)    # torch.Size([128, 32])---->torch.Size([128, 32, 768])
            token_type_embeddings = self.token_type_embeddings(token_type_ids)  # torch.Size([128, 32])---->torch.Size([128, 32, 768])
    
            embeddings = words_embeddings + position_embeddings + token_type_embeddings # torch.Size([128, 32, 768])
            embeddings = self.LayerNorm(embeddings) # torch.Size([128, 32, 768])---->torch.Size([128, 32, 768])
            embeddings = self.dropout(embeddings)   # torch.Size([128, 32, 768])
            return embeddings

完整代码：

# coding=utf-8
    # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
    # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    """PyTorch BERT model."""
    
    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 .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",
        'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
        'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
        'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.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))
            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=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, *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)
            """
            state_dict = kwargs.get('state_dict', None)
            kwargs.pop('state_dict', None)
            cache_dir = kwargs.get('cache_dir', None)
            kwargs.pop('cache_dir', None)
            from_tf = kwargs.get('from_tf', False)
            kwargs.pop('from_tf', None)
    
            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)
            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 = 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 + '.')
            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 = (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)) if token_type_ids is not None else None
            flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
            _, 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 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