import torch
import pytorch_lightning as pl
from nltk import edit_distance
import re
import numpy as np


class DonutModelPLModuleStream(pl.LightningModule):
    def __init__(self, config, processor, model, max_length, train_dataloader, val_dataloader):
        super().__init__()
        self.config = config
        self.processor = processor
        self.model = model
        self.max_length = max_length
        self._train_dataloader = train_dataloader
        self._val_dataloader = val_dataloader

    def training_step(self, batch, batch_idx):
        # pixel_values, labels, _ = batch
        pixel_values = batch['pixel_values']
        labels = batch['labels']
        outputs = self.model(pixel_values, labels=labels)
        loss = outputs.loss
        self.log_dict({"train_loss": loss}, sync_dist=True)
        return loss

    def validation_step(self, batch, batch_idx, dataset_idx=0):
        # pixel_values, labels, answers = batch

        pixel_values = batch['pixel_values']
        labels = batch['labels']
        answers = batch['target_sequence']
        batch_size = pixel_values.shape[0]
        # we feed the prompt to the model
        decoder_input_ids = torch.full((batch_size, 1), self.model.config.decoder_start_token_id, device=self.device)
        
        outputs = self.model.generate(pixel_values,
                                   decoder_input_ids=decoder_input_ids,
                                   max_length=self.max_length,
                                   early_stopping=True,
                                   pad_token_id=self.processor.tokenizer.pad_token_id,
                                   eos_token_id=self.processor.tokenizer.eos_token_id,
                                   use_cache=True,
                                   num_beams=1,
                                   bad_words_ids=[[self.processor.tokenizer.unk_token_id]],
                                   return_dict_in_generate=True,)
    
        predictions = []
        for seq in self.processor.tokenizer.batch_decode(outputs.sequences):
            seq = seq.replace(self.processor.tokenizer.eos_token, "").replace(self.processor.tokenizer.pad_token, "")
            seq = re.sub(r"<.*?>", "", seq, count=1).strip()  # remove first task start token
            predictions.append(seq)

        scores = list()
        for pred, answer in zip(predictions, answers):
            pred = re.sub(r"(?:(?<=>) | (?=</s_))", "", pred)
            # NOT NEEDED ANYMORE
            # answer = re.sub(r"<.*?>", "", answer, count=1)
            answer = answer.replace(self.processor.tokenizer.eos_token, "")
            scores.append(edit_distance(pred, answer) / max(len(pred), len(answer)))

            if self.config.get("verbose", False) and len(scores) == 1:
                print(f"Prediction: {pred}")
                print(f"    Answer: {answer}")
                print(f" Normed ED: {scores[0]}")

        return scores

    def validation_epoch_end(self, validation_step_outputs):
        # I set this to 1 manually
        # (previously set to len(self.config.dataset_name_or_paths))
        num_of_loaders = 1
        if num_of_loaders == 1:
            validation_step_outputs = [validation_step_outputs]
        assert len(validation_step_outputs) == num_of_loaders
        cnt = [0] * num_of_loaders
        total_metric = [0] * num_of_loaders
        val_metric = [0] * num_of_loaders
        for i, results in enumerate(validation_step_outputs):
            for scores in results:
                cnt[i] += len(scores)
                total_metric[i] += np.sum(scores)
            val_metric[i] = total_metric[i] / cnt[i]
            val_metric_name = f"val_metric_{i}th_dataset"
            self.log_dict({val_metric_name: val_metric[i]}, sync_dist=True)
        self.log_dict({"val_metric": np.sum(total_metric) / np.sum(cnt)}, sync_dist=True)

    def configure_optimizers(self):
        # TODO add scheduler
        optimizer = torch.optim.Adam(self.parameters(), lr=self.config.get("lr"))
    
        return optimizer

    def train_dataloader(self):
        return self._train_dataloader

    def val_dataloader(self):
        return self._val_dataloader