
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and the HuggingFace 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 ALBERT model. """

import os
import math
import logging
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.modeling_utils import PreTrainedModel
from transformers.configuration_albert import AlbertConfig
from transformers.modeling_bert import BertEmbeddings, BertSelfAttention, prune_linear_layer, ACT2FN
from .file_utils import add_start_docstrings

logger = logging.getLogger(__name__)


ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin",
    'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin",
    'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin",
    'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin",
    'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-pytorch_model.bin",
    'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-pytorch_model.bin",
    'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-pytorch_model.bin",
    'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-pytorch_model.bin",
}


def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
    """ Load tf checkpoints in a pytorch model."""
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error("Loading a TensorFlow model 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)
    logger.info("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:
        logger.info("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):
        print(name)
    
    for name, array in zip(names, arrays):
        original_name = name
        name = name.replace("ffn_1", "ffn")
        name = name.replace("/bert/", "/albert/")
        name = name.replace("ffn/intermediate/output", "ffn_output")
        name = name.replace("attention_1", "attention")   
        name = name.replace("cls/predictions", "predictions")
        name = name.replace("transform/", "")
        name = name.replace("LayerNorm_1", "full_layer_layer_norm")    
        name = name.replace("LayerNorm", "attention/LayerNorm")    
        name = name.replace("inner_group_", "albert_layers/") 
        name = name.replace("group_", "albert_layer_groups/")    
        name = name.split('/')
        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:
                    logger.info("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 {} from {}".format(name, original_name))
        pointer.data = torch.from_numpy(array)

    return model


class AlbertEmbeddings(BertEmbeddings):
    """
    Construct the embeddings from word, position and token_type embeddings.
    """
    def __init__(self, config):
        super(AlbertEmbeddings, self).__init__(config)

        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
        self.LayerNorm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)


class AlbertAttention(BertSelfAttention):
    def __init__(self, config):
        super(AlbertAttention, self).__init__(config)

        self.output_attentions = config.output_attentions
        self.num_attention_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size 
        self.attention_head_size = config.hidden_size // config.num_attention_heads
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pruned_heads = set()

    def forward(self, input_ids, attention_mask=None, head_mask=None):
        mixed_query_layer = self.query(input_ids)
        mixed_key_layer = self.key(input_ids)
        mixed_value_layer = self.value(input_ids)

        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)
        if attention_mask is not None:
            # 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)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        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,)
        reshaped_context_layer = context_layer.view(*new_context_layer_shape)
        

        # Should find a better way to do this
        w = self.dense.weight.T.view(self.num_attention_heads, self.attention_head_size, self.hidden_size)
        b = self.dense.bias

        projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b
        projected_context_layer_dropout = self.dropout(projected_context_layer)
        layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer_dropout)
        return (layernormed_context_layer, attention_probs) if self.output_attentions else (layernormed_context_layer,)


class AlbertLayer(nn.Module):
    def __init__(self, config):
        super(AlbertLayer, self).__init__()
        
        self.config = config
        self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attention = AlbertAttention(config)
        self.ffn = nn.Linear(config.hidden_size, config.intermediate_size) 
        self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
        self.activation = ACT2FN[config.hidden_act]

    def forward(self, hidden_states, attention_mask=None, head_mask=None):
        attention_output = self.attention(hidden_states, attention_mask)
        ffn_output = self.ffn(attention_output[0])
        ffn_output = self.activation(ffn_output)
        ffn_output = self.ffn_output(ffn_output)
        hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])

        return (hidden_states,) + attention_output[1:]  # add attentions if we output them


class AlbertLayerGroup(nn.Module):
    def __init__(self, config):
        super(AlbertLayerGroup, self).__init__()
        
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])

    def forward(self, hidden_states, attention_mask=None, head_mask=None):
        layer_hidden_states = ()
        layer_attentions = ()

        for albert_layer in self.albert_layers:
            layer_output = albert_layer(hidden_states, attention_mask, head_mask)
            hidden_states = layer_output[0]

            if self.output_attentions:
                layer_attentions = layer_attentions + (layer_output[1],)

            if self.output_hidden_states:
                layer_hidden_states = layer_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if self.output_hidden_states:
            outputs = outputs + (layer_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (layer_attentions,)
        return outputs  # last-layer hidden state, (layer hidden states), (layer attentions)


class AlbertTransformer(nn.Module):
    def __init__(self, config):
        super(AlbertTransformer, self).__init__()
        
        self.config = config
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
        self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])

    def forward(self, hidden_states, attention_mask=None, head_mask=None):
        hidden_states = self.embedding_hidden_mapping_in(hidden_states)

        all_attentions = ()

        if self.output_hidden_states:
            all_hidden_states = (hidden_states,)

        for layer_idx in range(self.config.num_hidden_layers):
            group_idx = int(layer_idx / self.config.num_hidden_layers * self.config.num_hidden_groups)
            layer_group_output = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask)  
        
            hidden_states = layer_group_output[0]

            if self.output_attentions:
                all_attentions = all_attentions + layer_group_output[-1]

            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        
        outputs = (hidden_states,)
        if self.output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (all_attentions,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions)



class AlbertPreTrainedModel(PreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for dowloading and loading pretrained models.
    """
    config_class = AlbertConfig
    pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "albert"

    def _init_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)
            if isinstance(module, (nn.Linear)) and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


ALBERT_START_DOCSTRING = r"""    The ALBERT model was proposed in
    `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`_
    by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
    two parameter-reduction techniques to lower memory consumption and increase the trainig speed of BERT.

    This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
    refer to the PyTorch documentation for all matter related to general usage and behavior.

    .. _`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`:
        https://arxiv.org/abs/1909.11942

    .. _`torch.nn.Module`:
        https://pytorch.org/docs/stable/nn.html#module

    Parameters:
        config (:class:`~transformers.AlbertConfig`): Model configuration class with all the parameters of the model. 
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

ALBERT_INPUTS_DOCSTRING = r"""
    Inputs:
        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Indices of input sequence tokens in the vocabulary.
            To match pre-training, BERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:

            (a) For sequence pairs:

                ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
                
                ``token_type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1``

            (b) For single sequences:

                ``tokens:         [CLS] the dog is hairy . [SEP]``
                
                ``token_type_ids:   0   0   0   0  0     0   0``

            Albert is a model with absolute position embeddings so it's usually advised to pad the inputs on
            the right rather than the left.

            Indices can be obtained using :class:`transformers.AlbertTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
        **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token
            (see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
        **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.
        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""

@add_start_docstrings("The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.",
                      ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertModel(AlbertPreTrainedModel):
    r"""
    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
            Sequence of hidden-states at the output of the last layer of the model.
        **pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
            Last layer hidden-state of the first token of the sequence (classification token)
            further processed by a Linear layer and a Tanh activation function. The Linear
            layer weights are trained from the next sentence prediction (classification)
            objective during Bert pretraining. This output is usually *not* a good summary
            of the semantic content of the input, you're often better with averaging or pooling
            the sequence of hidden-states for the whole input sequence.
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
    """

    config_class = AlbertConfig
    pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
    load_tf_weights = load_tf_weights_in_albert
    base_model_prefix = "albert"

    def __init__(self, config):
        super(AlbertModel, self).__init__(config)

        self.config = config
        self.embeddings = AlbertEmbeddings(config)
        self.encoder = AlbertTransformer(config)
        self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
        self.pooler_activation = nn.Tanh()

        self.init_weights()

    def _resize_token_embeddings(self, new_num_tokens):
        old_embeddings = self.embeddings.word_embeddings
        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
        self.embeddings.word_embeddings = new_embeddings
        return self.embeddings.word_embeddings

    def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_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)

        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
        extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        if head_mask is not None:
            if head_mask.dim() == 1:
                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
            elif head_mask.dim() == 2:
                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
            head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
        else:
            head_mask = [None] * self.config.num_hidden_layers

        embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
        encoder_outputs = self.encoder(embedding_output,
                                       extended_attention_mask,
                                       head_mask=head_mask)

        sequence_output = encoder_outputs[0]

        pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0]))

        outputs = (sequence_output, pooled_output) + encoder_outputs[1:]  # add hidden_states and attentions if they are here
        return outputs

class AlbertMLMHead(nn.Module):
    def __init__(self, config):
        super(AlbertMLMHead, self).__init__()

        self.LayerNorm = nn.LayerNorm(config.embedding_size)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.dense = nn.Linear(config.hidden_size, config.embedding_size)
        self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
        self.activation = ACT2FN[config.hidden_act]

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.decoder(hidden_states)

        prediction_scores = hidden_states + self.bias

        return prediction_scores


@add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertForMaskedLM(AlbertPreTrainedModel):
    r"""
        **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Labels for computing the masked language modeling loss.
            Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
            Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
            in ``[0, ..., config.vocab_size]``

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Masked language modeling loss.
        **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
    """

    def __init__(self, config):
        super(AlbertForMaskedLM, self).__init__(config)

        self.albert = AlbertModel(config)
        self.predictions = AlbertMLMHead(config)

        self.init_weights()
        self.tie_weights()

    def tie_weights(self):
        """ Make sure we are sharing the input and output embeddings.
            Export to TorchScript can't handle parameter sharing so we are cloning them instead.
        """
        self._tie_or_clone_weights(self.predictions.decoder,
                                   self.albert.embeddings.word_embeddings)

    def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
                masked_lm_labels=None):
        outputs = self.albert(input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None)
        sequence_outputs = outputs[0]

        prediction_scores = self.predictions(sequence_outputs)

        outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
        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))
            outputs = (masked_lm_loss,) + outputs

        return outputs


@add_start_docstrings("""Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertForSequenceClassification(AlbertPreTrainedModel):
    r"""
        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels for computing the sequence classification/regression loss.
            Indices should be in ``[0, ..., config.num_labels - 1]``.
            If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Classification (or regression if config.num_labels==1) loss.
        **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
        model = AlbertForSequenceClassification.from_pretrained('albert-base-v2')
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids, labels=labels)
        loss, logits = outputs[:2]

    """
    def __init__(self, config):
        super(AlbertForSequenceClassification, self).__init__(config)
        self.num_labels = config.num_labels

        self.albert = AlbertModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)

        self.init_weights()

    def forward(self, input_ids, attention_mask=None, token_type_ids=None,
                position_ids=None, head_mask=None, labels=None):

        outputs = self.albert(input_ids,
                            attention_mask=attention_mask,
                            token_type_ids=token_type_ids,
                            position_ids=position_ids, 
                            head_mask=head_mask)

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here

        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            outputs = (loss,) + outputs

        return outputs  # (loss), logits, (hidden_states), (attentions)



@add_start_docstrings("""Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
    the hidden-states output to compute `span start logits` and `span end logits`). """,
    ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertForQuestionAnswering(AlbertPreTrainedModel):
    r"""
        **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`).
            Position outside of the sequence are not taken into account for computing the loss.
        **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`).
            Position outside of the sequence are not taken into account for computing the loss.

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
        **start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
            Span-start scores (before SoftMax).
        **end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
            Span-end scores (before SoftMax).
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
        model = AlbertForQuestionAnswering.from_pretrained('albert-base-v2')
        question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
        input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]"
        input_ids = tokenizer.encode(input_text)
        token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))] 
        start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids]))
        all_tokens = tokenizer.convert_ids_to_tokens(input_ids)  
        print(' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]))
        # a nice puppet


    """
    def __init__(self, config):
        super(AlbertForQuestionAnswering, self).__init__(config)
        self.num_labels = config.num_labels

        self.albert = AlbertModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
                start_positions=None, end_positions=None):

        outputs = self.albert(input_ids,
                            attention_mask=attention_mask,
                            token_type_ids=token_type_ids,
                            position_ids=position_ids, 
                            head_mask=head_mask)

        sequence_output = outputs[0]

        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)

        outputs = (start_logits, end_logits,) + outputs[2:]
        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
            outputs = (total_loss,) + outputs

        return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)
