import time
import torch
import numpy as np
import pandas as pd
from dagshub.pytorch_lightning import DAGsHubLogger
from transformers import (
    AdamW,
    T5ForConditionalGeneration,
    T5TokenizerFast as T5Tokenizer, MT5Tokenizer, MT5ForConditionalGeneration, ByT5Tokenizer,
)
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl
from pytorch_lightning.loggers import MLFlowLogger
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning import LightningDataModule
from pytorch_lightning import LightningModule
from datasets import load_metric

torch.cuda.empty_cache()
pl.seed_everything(42)


class DataModule(Dataset):
    """
    Data Module for pytorch
    """

    def __init__(
            self,
            data: pd.DataFrame,
            tokenizer: T5Tokenizer,
            source_max_token_len: int = 512,
            target_max_token_len: int = 512,
    ):
        """
        :param data:
        :param tokenizer:
        :param source_max_token_len:
        :param target_max_token_len:
        """
        self.data = data
        self.target_max_token_len = target_max_token_len
        self.source_max_token_len = source_max_token_len
        self.tokenizer = tokenizer

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index: int):
        data_row = self.data.iloc[index]

        input_encoding = self.tokenizer(
            data_row["input_text"],
            max_length=self.source_max_token_len,
            padding="max_length",
            truncation=True,
            return_attention_mask=True,
            add_special_tokens=True,
            return_tensors="pt",
        )

        output_encoding = self.tokenizer(
            data_row["output_text"],
            max_length=self.target_max_token_len,
            padding="max_length",
            truncation=True,
            return_attention_mask=True,
            add_special_tokens=True,
            return_tensors="pt",
        )

        labels = output_encoding["input_ids"]
        labels[
            labels == 0
            ] = -100

        return dict(
            keywords=data_row["keywords"],
            text=data_row["text"],
            keywords_input_ids=input_encoding["input_ids"].flatten(),
            keywords_attention_mask=input_encoding["attention_mask"].flatten(),
            labels=labels.flatten(),
            labels_attention_mask=output_encoding["attention_mask"].flatten(),
        )


class PLDataModule(LightningDataModule):
    def __init__(
            self,
            train_df: pd.DataFrame,
            test_df: pd.DataFrame,
            tokenizer: T5Tokenizer,
            source_max_token_len: int = 512,
            target_max_token_len: int = 512,
            batch_size: int = 4,
            split: float = 0.1
    ):
        """
        :param data_df:
        :param tokenizer:
        :param source_max_token_len:
        :param target_max_token_len:
        :param batch_size:
        :param split:
        """
        super().__init__()
        self.train_df = train_df
        self.test_df = test_df
        self.split = split
        self.batch_size = batch_size
        self.target_max_token_len = target_max_token_len
        self.source_max_token_len = source_max_token_len
        self.tokenizer = tokenizer

    def setup(self, stage=None):
        self.train_dataset = DataModule(
            self.train_df,
            self.tokenizer,
            self.source_max_token_len,
            self.target_max_token_len,
        )
        self.test_dataset = DataModule(
            self.test_df,
            self.tokenizer,
            self.source_max_token_len,
            self.target_max_token_len,
        )

    def train_dataloader(self):
        """ training dataloader """
        return DataLoader(
            self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=2
        )

    def test_dataloader(self):
        """ test dataloader """
        return DataLoader(
            self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=2
        )

    def val_dataloader(self):
        """ validation dataloader """
        return DataLoader(
            self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=2
        )


class LightningModel(LightningModule):
    """ PyTorch Lightning Model class"""

    def __init__(self, tokenizer, model, output: str = "outputs"):
        """
        initiates a PyTorch Lightning Model
        Args:
            tokenizer : T5 tokenizer
            model : T5 model
            output (str, optional): output directory to save model checkpoints. Defaults to "outputs".
        """
        super().__init__()
        self.model = model
        self.tokenizer = tokenizer
        self.output = output

    def forward(self, input_ids, attention_mask, decoder_attention_mask, labels=None):
        """ forward step """
        output = self.model(
            input_ids,
            attention_mask=attention_mask,
            labels=labels,
            decoder_attention_mask=decoder_attention_mask,
        )

        return output.loss, output.logits

    def training_step(self, batch, batch_size):
        """ training step """
        input_ids = batch["keywords_input_ids"]
        attention_mask = batch["keywords_attention_mask"]
        labels = batch["labels"]
        labels_attention_mask = batch["labels_attention_mask"]

        loss, outputs = self(
            input_ids=input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=labels_attention_mask,
            labels=labels,
        )
        self.log("train_loss", loss, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_size):
        """ validation step """
        input_ids = batch["keywords_input_ids"]
        attention_mask = batch["keywords_attention_mask"]
        labels = batch["labels"]
        labels_attention_mask = batch["labels_attention_mask"]

        loss, outputs = self(
            input_ids=input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=labels_attention_mask,
            labels=labels,
        )
        self.log("val_loss", loss, prog_bar=True, logger=True)
        return loss

    def test_step(self, batch, batch_size):
        """ test step """
        input_ids = batch["keywords_input_ids"]
        attention_mask = batch["keywords_attention_mask"]
        labels = batch["labels"]
        labels_attention_mask = batch["labels_attention_mask"]

        loss, outputs = self(
            input_ids=input_ids,
            attention_mask=attention_mask,
            decoder_attention_mask=labels_attention_mask,
            labels=labels,
        )

        self.log("test_loss", loss, prog_bar=True, logger=True)
        return loss

    def configure_optimizers(self):
        """ configure optimizers """
        model = self.model
        no_decay = ["bias", "LayerNorm.weight"]
        optimizer_grouped_parameters = [
            {
                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                "weight_decay": self.hparams.weight_decay,
            },
            {
                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                "weight_decay": 0.0,
            },
        ]
        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
        self.opt = optimizer
        return [optimizer]


class Summarization:
    """ Custom Summarization class """

    def __init__(self) -> None:
        """ initiates Summarization class """
        pass

    def from_pretrained(self, model_type="t5", model_name="t5-base") -> None:
        """
        loads T5/MT5 Model model for training/finetuning
        Args:
            model_name (str, optional): exact model architecture name, "t5-base" or "t5-large". Defaults to "t5-base".
            :param model_type:
        """
        if model_type == "t5":
            self.tokenizer = T5Tokenizer.from_pretrained(f"{model_name}")
            self.model = T5ForConditionalGeneration.from_pretrained(
                f"{model_name}", return_dict=True
            )
        elif model_type == "mt5":
            self.tokenizer = MT5Tokenizer.from_pretrained(f"{model_name}")
            self.model = MT5ForConditionalGeneration.from_pretrained(
                f"{model_name}", return_dict=True
            )
        elif model_type == "byt5":
            self.tokenizer = ByT5Tokenizer.from_pretrained(f"{model_name}")
            self.model = T5ForConditionalGeneration.from_pretrained(
                f"{model_name}", return_dict=True
            )

    def train(
            self,
            train_df: pd.DataFrame,
            eval_df: pd.DataFrame,
            source_max_token_len: int = 512,
            target_max_token_len: int = 512,
            batch_size: int = 8,
            max_epochs: int = 5,
            use_gpu: bool = True,
            outputdir: str = "models",
            early_stopping_patience_epochs: int = 0,  # 0 to disable early stopping feature
    ):
        """
        trains T5/MT5 model on custom dataset
        Args:
            train_df (pd.DataFrame): training datarame. Dataframe must have 2 column --> "input_text" and "output_text"
            eval_df ([type], optional): validation datarame. Dataframe must have 2 column --> "input_text" and
            "output_text"
            source_max_token_len (int, optional): max token length of source text. Defaults to 512.
            target_max_token_len (int, optional): max token length of target text. Defaults to 512.
            batch_size (int, optional): batch size. Defaults to 8.
            max_epochs (int, optional): max number of epochs. Defaults to 5.
            use_gpu (bool, optional): if True, model uses gpu for training. Defaults to True.
            outputdir (str, optional): output directory to save model checkpoints. Defaults to "outputs".
            early_stopping_patience_epochs (int, optional): monitors val_loss on epoch end and stops training,
            if val_loss does not improve after the specied number of epochs. set 0 to disable early stopping.
            Defaults to 0 (disabled)
        """
        self.target_max_token_len = target_max_token_len
        self.data_module = PLDataModule(
            train_df,
            eval_df,
            self.tokenizer,
            batch_size=batch_size,
            source_max_token_len=source_max_token_len,
            target_max_token_len=target_max_token_len,
        )

        self.T5Model = LightningModel(
            tokenizer=self.tokenizer, model=self.model, output=outputdir
        )

        MLlogger = MLFlowLogger(experiment_name="Summarization",
                                tracking_uri="https://dagshub.com/gagan3012/summarization.mlflow")

        logger = DAGsHubLogger(metrics_path='reports/metrics.txt')

        early_stop_callback = (
            [
                EarlyStopping(
                    monitor="val_loss",
                    min_delta=0.00,
                    patience=early_stopping_patience_epochs,
                    verbose=True,
                    mode="min",
                )
            ]
            if early_stopping_patience_epochs > 0
            else None
        )

        gpus = 1 if use_gpu else 0

        trainer = Trainer(
            logger=[logger, MLlogger],
            callbacks=early_stop_callback,
            max_epochs=max_epochs,
            gpus=gpus,
            progress_bar_refresh_rate=5,
        )

        trainer.fit(self.T5Model, self.data_module)

    def load_model(
            self, model_type: str = 't5', model_dir: str = "../../models", use_gpu: bool = False
    ):
        """
        loads a checkpoint for inferencing/prediction
        Args:
            model_type (str, optional): "t5" or "mt5". Defaults to "t5".
            model_dir (str, optional): path to model directory. Defaults to "outputs".
            use_gpu (bool, optional): if True, model uses gpu for inferencing/prediction. Defaults to True.
        """
        if model_type == "t5":
            self.tokenizer = T5Tokenizer.from_pretrained(f"{model_dir}")
            self.model = T5ForConditionalGeneration.from_pretrained(
                f"{model_dir}", return_dict=True
            )
        elif model_type == "mt5":
            self.tokenizer = MT5Tokenizer.from_pretrained(f"{model_dir}")
            self.model = MT5ForConditionalGeneration.from_pretrained(
                f"{model_dir}", return_dict=True
            )
        elif model_type == "byt5":
            self.tokenizer = ByT5Tokenizer.from_pretrained(f"{model_dir}")
            self.model = T5ForConditionalGeneration.from_pretrained(
                f"{model_dir}", return_dict=True
            )

        if use_gpu:
            if torch.cuda.is_available():
                self.device = torch.device("cuda")
            else:
                raise Exception("exception ---> no gpu found. set use_gpu=False, to use CPU")
        else:
            self.device = torch.device("cpu")

        self.model = self.model.to(self.device)

    def save_model(
            self,
            model_dir="../../models"
    ):
        """
        Save model to dir
        :param model_dir:
        :return: model is saved
        """
        path = f"{model_dir}"
        self.tokenizer.save_pretrained(path)
        self.model.save_pretrained(path)

    def predict(
            self,
            source_text: str,
            max_length: int = 512,
            num_return_sequences: int = 1,
            num_beams: int = 2,
            top_k: int = 50,
            top_p: float = 0.95,
            do_sample: bool = True,
            repetition_penalty: float = 2.5,
            length_penalty: float = 1.0,
            early_stopping: bool = True,
            skip_special_tokens: bool = True,
            clean_up_tokenization_spaces: bool = True,
    ):
        """
        generates prediction for T5/MT5 model
        Args:
            source_text (str): any text for generating predictions
            max_length (int, optional): max token length of prediction. Defaults to 512.
            num_return_sequences (int, optional): number of predictions to be returned. Defaults to 1.
            num_beams (int, optional): number of beams. Defaults to 2.
            top_k (int, optional): Defaults to 50.
            top_p (float, optional): Defaults to 0.95.
            do_sample (bool, optional): Defaults to True.
            repetition_penalty (float, optional): Defaults to 2.5.
            length_penalty (float, optional): Defaults to 1.0.
            early_stopping (bool, optional): Defaults to True.
            skip_special_tokens (bool, optional): Defaults to True.
            clean_up_tokenization_spaces (bool, optional): Defaults to True.
        Returns:
            list[str]: returns predictions
        """
        input_ids = self.tokenizer.encode(
            source_text, return_tensors="pt", add_special_tokens=True
        )

        input_ids = input_ids.to(self.device)
        generated_ids = self.model.generate(
            input_ids=input_ids,
            num_beams=num_beams,
            max_length=max_length,
            repetition_penalty=repetition_penalty,
            length_penalty=length_penalty,
            early_stopping=early_stopping,
            top_p=top_p,
            top_k=top_k,
            num_return_sequences=num_return_sequences,
        )
        preds = [
            self.tokenizer.decode(
                g,
                skip_special_tokens=skip_special_tokens,
                clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            )
            for g in generated_ids
        ]
        return preds[0]

    def evaluate(
            self,
            test_df: pd.DataFrame,
            metrics: str = "rouge"
    ):
        metric = load_metric(metrics)
        input_text = test_df['input_text']
        references = test_df['output_text']
        predictions = [self.predict(x) for x in input_text]

        results = metric.compute(predictions=predictions, references=references)

        output = {
            'Rouge 1': {
                'Rouge_1 Low Precision': results["rouge1"].low.precision,
                'Rouge_1 Low recall': results["rouge1"].low.recall,
                'Rouge_1 Low F1': results["rouge1"].low.fmeasure,
                'Rouge_1 Mid Precision': results["rouge1"].mid.precision,
                'Rouge_1 Mid recall': results["rouge1"].mid.recall,
                'Rouge_1 Mid F1': results["rouge1"].mid.fmeasure,
                'Rouge_1 High Precision': results["rouge1"].high.precision,
                'Rouge_1 High recall': results["rouge1"].high.recall,
                'Rouge_1 High F1': results["rouge1"].high.fmeasure,
            },
            'Rouge 2': {
                'Rouge_2 Low Precision': results["rouge2"].low.precision,
                'Rouge_2 Low recall': results["rouge2"].low.recall,
                'Rouge_2 Low F1': results["rouge2"].low.fmeasure,
                'Rouge_2 Mid Precision': results["rouge2"].mid.precision,
                'Rouge_2 Mid recall': results["rouge2"].mid.recall,
                'Rouge_2 Mid F1': results["rouge2"].mid.fmeasure,
                'Rouge_2 High Precision': results["rouge2"].high.precision,
                'Rouge_2 High recall': results["rouge2"].high.recall,
                'Rouge_2 High F1': results["rouge2"].high.fmeasure,
            },
            'Rouge L': {
                'Rouge_L Low Precision': results["rougeL"].low.precision,
                'Rouge_L Low recall': results["rougeL"].low.recall,
                'Rouge_L Low F1': results["rougeL"].low.fmeasure,
                'Rouge_L Mid Precision': results["rougeL"].mid.precision,
                'Rouge_L Mid recall': results["rougeL"].mid.recall,
                'Rouge_L Mid F1': results["rougeL"].mid.fmeasure,
                'Rouge_L High Precision': results["rougeL"].high.precision,
                'Rouge_L High recall': results["rougeL"].high.recall,
                'Rouge_L High F1': results["rougeL"].high.fmeasure,
            },
            'rougeLsum': {
                'rougeLsum Low Precision': results["rougeLsum"].low.precision,
                'rougeLsum Low recall': results["rougeLsum"].low.recall,
                'rougeLsum Low F1': results["rougeLsum"].low.fmeasure,
                'rougeLsum Mid Precision': results["rougeLsum"].mid.precision,
                'rougeLsum Mid recall': results["rougeLsum"].mid.recall,
                'rougeLsum Mid F1': results["rougeLsum"].mid.fmeasure,
                'rougeLsum High Precision': results["rougeLsum"].high.precision,
                'rougeLsum High recall': results["rougeLsum"].high.recall,
                'rougeLsum High F1': results["rougeLsum"].high.fmeasure,
            }
        }
        return output