import logging
import os

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss

from .activations import get_activation
from .configuration_electra import ElectraConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_bert import BertEmbeddings, BertEncoder, BertLayerNorm, BertPreTrainedModel


logger = logging.getLogger(__name__)


ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "google/electra-small-generator": "https://cdn.huggingface.co/google/electra-small-generator/pytorch_model.bin",
    "google/electra-base-generator": "https://cdn.huggingface.co/google/electra-base-generator/pytorch_model.bin",
    "google/electra-large-generator": "https://cdn.huggingface.co/google/electra-large-generator/pytorch_model.bin",
    "google/electra-small-discriminator": "https://cdn.huggingface.co/google/electra-small-discriminator/pytorch_model.bin",
    "google/electra-base-discriminator": "https://cdn.huggingface.co/google/electra-base-discriminator/pytorch_model.bin",
    "google/electra-large-discriminator": "https://cdn.huggingface.co/google/electra-large-discriminator/pytorch_model.bin",
}


def load_tf_weights_in_electra(model, config, tf_checkpoint_path, discriminator_or_generator="discriminator"):
    """ 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):
        original_name: str = name

        try:
            if isinstance(model, ElectraForMaskedLM):
                name = name.replace("electra/embeddings/", "generator/embeddings/")

            if discriminator_or_generator == "generator":
                name = name.replace("electra/", "discriminator/")
                name = name.replace("generator/", "electra/")

            name = name.replace("dense_1", "dense_prediction")
            name = name.replace("generator_predictions/output_bias", "generator_lm_head/bias")

            name = name.split("/")
            # print(original_name, name)
            # 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 ["global_step", "temperature"] for n in name):
                logger.info("Skipping {}".format(original_name))
                continue
            pointer = model
            for m_name in name:
                if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                    scope_names = re.split(r"_(\d+)", m_name)
                else:
                    scope_names = [m_name]
                if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                    pointer = getattr(pointer, "weight")
                elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                    pointer = getattr(pointer, "bias")
                elif scope_names[0] == "output_weights":
                    pointer = getattr(pointer, "weight")
                elif scope_names[0] == "squad":
                    pointer = getattr(pointer, "classifier")
                else:
                    pointer = getattr(pointer, scope_names[0])
                if len(scope_names) >= 2:
                    num = int(scope_names[1])
                    pointer = pointer[num]
            if m_name.endswith("_embeddings"):
                pointer = getattr(pointer, "weight")
            elif m_name == "kernel":
                array = np.transpose(array)
            try:
                assert pointer.shape == array.shape, original_name
            except AssertionError as e:
                e.args += (pointer.shape, array.shape)
                raise
            print("Initialize PyTorch weight {}".format(name), original_name)
            pointer.data = torch.from_numpy(array)
        except AttributeError as e:
            print("Skipping {}".format(original_name), name, e)
            continue
    return model


class ElectraEmbeddings(BertEmbeddings):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config):
        super().__init__(config)
        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
        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 is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = BertLayerNorm(config.embedding_size, eps=config.layer_norm_eps)


class ElectraDiscriminatorPredictions(nn.Module):
    """Prediction module for the discriminator, made up of two dense layers."""

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

        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dense_prediction = nn.Linear(config.hidden_size, 1)
        self.config = config

    def forward(self, discriminator_hidden_states, attention_mask):
        hidden_states = self.dense(discriminator_hidden_states)
        hidden_states = get_activation(self.config.hidden_act)(hidden_states)
        logits = self.dense_prediction(hidden_states).squeeze()

        return logits


class ElectraGeneratorPredictions(nn.Module):
    """Prediction module for the generator, made up of two dense layers."""

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

        self.LayerNorm = BertLayerNorm(config.embedding_size)
        self.dense = nn.Linear(config.hidden_size, config.embedding_size)

    def forward(self, generator_hidden_states):
        hidden_states = self.dense(generator_hidden_states)
        hidden_states = get_activation("gelu")(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)

        return hidden_states


class ElectraPreTrainedModel(BertPreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = ElectraConfig
    pretrained_model_archive_map = ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP
    load_tf_weights = load_tf_weights_in_electra
    base_model_prefix = "electra"


ELECTRA_START_DOCSTRING = r"""
    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#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.

    Parameters:
        config (:class:`~transformers.ElectraConfig`): 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.
"""

ELECTRA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.ElectraTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            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.

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            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

            `What are token type IDs? <../glossary.html#token-type-ids>`_
        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
            if the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask
            is used in the cross-attention if the model is configured as a decoder.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
"""


@add_start_docstrings(
    "The bare Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to "
    "the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the "
    "hidden size and embedding size are different."
    ""
    "Both the generator and discriminator checkpoints may be loaded into this model.",
    ELECTRA_START_DOCSTRING,
)
class ElectraModel(ElectraPreTrainedModel):

    config_class = ElectraConfig

    def __init__(self, config):
        super().__init__(config)
        self.embeddings = ElectraEmbeddings(config)

        if config.embedding_size != config.hidden_size:
            self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)

        self.encoder = BertEncoder(config)
        self.config = config
        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """ Prunes heads of the model.
            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
            See base class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
    ):
        r"""
    Return:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
        last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(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::

        from transformers import ElectraModel, ElectraTokenizer
        import torch

        tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-discriminator')
        model = ElectraModel.from_pretrained('google/electra-small-discriminator')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids)

        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

        """
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        hidden_states = self.embeddings(
            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
        )

        if hasattr(self, "embeddings_project"):
            hidden_states = self.embeddings_project(hidden_states)

        hidden_states = self.encoder(hidden_states, attention_mask=extended_attention_mask, head_mask=head_mask)

        return hidden_states


class ElectraClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x)
        x = self.dense(x)
        x = get_activation("gelu")(x)  # although BERT uses tanh here, it seems Electra authors used gelu here
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


@add_start_docstrings(
    """ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    ELECTRA_START_DOCSTRING,
)
class ElectraForSequenceClassification(ElectraPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.electra = ElectraModel(config)
        self.classifier = ElectraClassificationHead(config)

        self.init_weights()

    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
            Labels for computing the sequence classification/regression loss.
            Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
            If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(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::

        from transformers import BertTokenizer, BertForSequenceClassification
        import torch

        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids, labels=labels)

        loss, logits = outputs[:2]

        """
        discriminator_hidden_states = self.electra(
            input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds
        )

        sequence_output = discriminator_hidden_states[0]
        logits = self.classifier(sequence_output)

        outputs = (logits,) + discriminator_hidden_states[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(
    """
    Electra model with a binary classification head on top as used during pre-training for identifying generated
    tokens.

    It is recommended to load the discriminator checkpoint into that model.""",
    ELECTRA_START_DOCSTRING,
)
class ElectraForPreTraining(ElectraPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.electra = ElectraModel(config)
        self.discriminator_predictions = ElectraDiscriminatorPredictions(config)
        self.init_weights()

    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
    ):
        r"""
        labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`, defaults to :obj:`None`):
            Labels for computing the ELECTRA loss. Input should be a sequence of tokens (see :obj:`input_ids` docstring)
            Indices should be in ``[0, 1]``.
            ``0`` indicates the token is an original token,
            ``1`` indicates the token was replaced.

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
        loss (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Total loss of the ELECTRA objective.
        scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`)
            Prediction scores of the head (scores for each token before SoftMax).
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(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::

        from transformers import ElectraTokenizer, ElectraForPreTraining
        import torch

        tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-discriminator')
        model = ElectraForPreTraining.from_pretrained('google/electra-small-discriminator')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids)

        prediction_scores, seq_relationship_scores = outputs[:2]

        """

        discriminator_hidden_states = self.electra(
            input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds
        )
        discriminator_sequence_output = discriminator_hidden_states[0]

        logits = self.discriminator_predictions(discriminator_sequence_output, attention_mask)

        output = (logits,)

        if labels is not None:
            loss_fct = nn.BCEWithLogitsLoss()
            if attention_mask is not None:
                active_loss = attention_mask.view(-1, discriminator_sequence_output.shape[1]) == 1
                active_logits = logits.view(-1, discriminator_sequence_output.shape[1])[active_loss]
                active_labels = labels[active_loss]
                loss = loss_fct(active_logits, active_labels.float())
            else:
                loss = loss_fct(logits.view(-1, discriminator_sequence_output.shape[1]), labels.float())

            output = (loss,) + output

        output += discriminator_hidden_states[1:]

        return output  # (loss), scores, (hidden_states), (attentions)


@add_start_docstrings(
    """
    Electra model with a language modeling head on top.

    Even though both the discriminator and generator may be loaded into this model, the generator is
    the only model of the two to have been trained for the masked language modeling task.""",
    ELECTRA_START_DOCSTRING,
)
class ElectraForMaskedLM(ElectraPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.electra = ElectraModel(config)
        self.generator_predictions = ElectraGeneratorPredictions(config)

        self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size)
        self.init_weights()

    def get_output_embeddings(self):
        return self.generator_lm_head

    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        masked_lm_labels=None,
    ):
        r"""
        masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Labels for computing the masked language modeling loss.
            Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
            Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
            in ``[0, ..., config.vocab_size]``

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
        masked_lm_loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Masked language modeling loss.
        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(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::

            from transformers import ElectraTokenizer, ElectraForMaskedLM
            import torch

            tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-generator')
            model = ElectraForMaskedLM.from_pretrained('google/electra-small-generator')

            input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
            outputs = model(input_ids, masked_lm_labels=input_ids)

            loss, prediction_scores = outputs[:2]

        """

        generator_hidden_states = self.electra(
            input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds
        )
        generator_sequence_output = generator_hidden_states[0]

        prediction_scores = self.generator_predictions(generator_sequence_output)
        prediction_scores = self.generator_lm_head(prediction_scores)

        output = (prediction_scores,)

        # Masked language modeling softmax layer
        if masked_lm_labels is not None:
            loss_fct = nn.CrossEntropyLoss()  # -100 index = padding token
            loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
            output = (loss,) + output

        output += generator_hidden_states[1:]

        return output  # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)


@add_start_docstrings(
    """
    Electra model with a token classification head on top.

    Both the discriminator and generator may be loaded into this model.""",
    ELECTRA_START_DOCSTRING,
)
class ElectraForTokenClassification(ElectraPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.electra = ElectraModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.init_weights()

    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Labels for computing the token classification loss.
            Indices should be in ``[0, ..., config.num_labels - 1]``.

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
            Classification loss.
        scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
            Classification scores (before SoftMax).
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(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::

        from transformers import ElectraTokenizer, ElectraForTokenClassification
        import torch

        tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-discriminator')
        model = ElectraForTokenClassification.from_pretrained('google/electra-small-discriminator')

        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
        labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids, labels=labels)

        loss, scores = outputs[:2]

        """

        discriminator_hidden_states = self.electra(
            input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds
        )
        discriminator_sequence_output = discriminator_hidden_states[0]

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

        output = (logits,)

        if labels is not None:
            loss_fct = nn.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.config.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.config.num_labels), labels.view(-1))

            output = (loss,) + output

        output += discriminator_hidden_states[1:]

        return output  # (loss), scores, (hidden_states), (attentions)
