import torch
import transformers
from torch import nn
from torch.nn import CrossEntropyLoss
from typing import Optional, Tuple, Union
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.models.bert.modeling_bert import (
    BertPreTrainedModel,
    BERT_INPUTS_DOCSTRING,
    _TOKENIZER_FOR_DOC,
    _CHECKPOINT_FOR_DOC,
    BERT_START_DOCSTRING,
    _CONFIG_FOR_DOC,
    _SEQ_CLASS_EXPECTED_OUTPUT,
    _SEQ_CLASS_EXPECTED_LOSS,
    BertModel,
)

from transformers.file_utils import (
    add_code_sample_docstrings,
    add_start_docstrings_to_model_forward,
    add_start_docstrings
)

@add_start_docstrings(
    """
    Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
    output) e.g. for GLUE tasks.
    """,
    BERT_START_DOCSTRING,
)
class BertForSequenceClassification(BertPreTrainedModel):
    def __init__(self, config, **kwargs):
        super().__init__(transformers.PretrainedConfig())
        #task_labels_map={"binary_classification": 2, "label_classification": 5}
        self.tasks = kwargs.get("tasks_map", {})
        self.config = config

        self.bert = BertModel(config)
        classifier_dropout = (
            config.classifier_dropout
            if config.classifier_dropout is not None
            else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        ## add task specific output heads
        self.classifier1 = nn.Linear(
            config.hidden_size, self.tasks[0].num_labels
        )
        self.classifier2 = nn.Linear(
            config.hidden_size, self.tasks[1].num_labels
        )

        self.init_weights()

    @add_start_docstrings_to_model_forward(
    BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
    )
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
        expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        task_ids=None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            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).
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
    
        unique_task_ids_list = torch.unique(task_ids).tolist()
        loss_list = []
        logits = None
        for unique_task_id in unique_task_ids_list:
            loss = None
            task_id_filter = task_ids == unique_task_id 

            if unique_task_id == 0:
                logits = self.classifier1(pooled_output[task_id_filter])
            elif unique_task_id == 1:
                logits = self.classifier2(pooled_output[task_id_filter])

            
            if labels is not None: 
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.tasks[unique_task_id].num_labels), labels[task_id_filter].view(-1))
                loss_list.append(loss)
        
        # logits are only used for eval. and in case of eval the batch is not multi task
        # For training only the loss is used

        if loss_list:
            loss = torch.stack(loss_list).mean()
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        
        return SequenceClassifierOutput(
                loss=loss,
                logits=logits,
                hidden_states=outputs.hidden_states,
                attentions=outputs.attentions,
            )