import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.models.flaubert.modeling_flaubert import FlaubertModel
from transformers.modeling_utils import SequenceSummary


class FlaubertForSequenceClassification(FlaubertModel):
    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 = FlaubertTokenizer.from_pretrained('flaubert-base-uncased')
        model = FlaubertForSequenceClassification.from_pretrained('flaubert-base-uncased')
        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]
    """  # noqa: ignore flake8"

    def __init__(self, config, weight=None):
        super(FlaubertForSequenceClassification, self).__init__(config)
        self.num_labels = config.num_labels
        self.weight = weight

        self.transformer = FlaubertModel(config)
        self.sequence_summary = SequenceSummary(config)

        self.init_weights()

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        langs=None,
        token_type_ids=None,
        position_ids=None,
        lengths=None,
        cache=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
    ):
        transformer_outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            langs=langs,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            lengths=lengths,
            cache=cache,
            head_mask=head_mask,
        )

        output = transformer_outputs[0]
        logits = self.sequence_summary(output)

        outputs = (logits,) + transformer_outputs[1:]  # Keep new_mems and attention/hidden states 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(weight=self.weight)
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            outputs = (loss,) + outputs

        return outputs
