from transformers.modeling_outputs import TokenClassifierOutput
import torch
import torch.nn as nn
from transformers import PreTrainedModel, AutoModel, AutoConfig
from torch.nn import CrossEntropyLoss
from typing import Optional, Tuple, Union
import logging
from configuration_extended_multitask import ImpressoConfig

logger = logging.getLogger(__name__)


class ExtendedMultitaskModelForTokenClassification(PreTrainedModel):

    config_class = ImpressoConfig
    _keys_to_ignore_on_load_missing = [r"position_ids"]

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

        # self.bert = AutoModel.from_config(config)
        self.bert = AutoModel.from_pretrained(
            config.name_or_path, config=config.pretrained_config
        )
        if "classifier_dropout" not in config.__dict__:
            classifier_dropout = 0.1
        else:
            classifier_dropout = (
                config.classifier_dropout
                if config.classifier_dropout is not None
                else config.hidden_dropout_prob
            )
        self.dropout = nn.Dropout(classifier_dropout)

        # Additional transformer layers
        self.transformer_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=config.hidden_size, nhead=config.num_attention_heads
            ),
            num_layers=2,
        )

        # For token classification, create a classifier for each task
        self.token_classifiers = nn.ModuleDict(
            {
                task: nn.Linear(config.hidden_size, num_labels)
                for task, num_labels in num_token_labels_dict.items()
            }
        )

        # Initialize weights and apply final processing
        self.post_init()

    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,
        token_labels: Optional[dict] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        r"""
        token_labels (`dict` of `torch.LongTensor` of shape `(batch_size, seq_length)`, *optional*):
            Labels for computing the token classification loss. Keys should match the tasks.
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        bert_kwargs = {
            "input_ids": 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,
        }

        if any(
            keyword in self.config.name_or_path.lower()
            for keyword in ["llama", "deberta"]
        ):
            bert_kwargs.pop("token_type_ids")
            bert_kwargs.pop("head_mask")

        outputs = self.bert(**bert_kwargs)

        # For token classification
        token_output = outputs[0]
        token_output = self.dropout(token_output)

        # Pass through additional transformer layers
        token_output = self.transformer_encoder(token_output.transpose(0, 1)).transpose(
            0, 1
        )

        # Collect the logits and compute the loss for each task
        task_logits = {}
        total_loss = 0
        for task, classifier in self.token_classifiers.items():
            logits = classifier(token_output)
            task_logits[task] = logits
            if token_labels and task in token_labels:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(
                    logits.view(-1, self.num_token_labels_dict[task]),
                    token_labels[task].view(-1),
                )
                total_loss += loss

        if not return_dict:
            output = (task_logits,) + outputs[2:]
            return ((total_loss,) + output) if total_loss != 0 else output

        return TokenClassifierOutput(
            loss=total_loss,
            logits=task_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )