import argparse
import logging
from torch.utils.data import Dataset, IterableDataset
import gzip
import json
from transformers import Seq2SeqTrainer, AutoModelForSeq2SeqLM, AutoTokenizer, Seq2SeqTrainingArguments
import sys
from datetime import datetime
import torch
import random
from shutil import copyfile
import os
import wandb
import random
import re


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    handlers=[logging.StreamHandler(sys.stdout)],
)

parser = argparse.ArgumentParser()
parser.add_argument("--model_name", default="google/t5-v1_1-base")
parser.add_argument("--train_files", required=True, nargs='+', default=[])
parser.add_argument("--epochs", default=1, type=int)
parser.add_argument("--batch_size", default=32, type=int)
parser.add_argument("--max_source_length", default=320, type=int)
parser.add_argument("--max_target_length", default=64, type=int)
parser.add_argument("--name", required=True)
parser.add_argument("--train_size", default=10*1000*1000, type=int)
parser.add_argument("--eval_size", default=10000, type=int)
parser.add_argument("--fp16", default=False, action='store_true')
args = parser.parse_args()

wandb.init(project="doc2query", name=f"{args.name}-{args.model_name}")




class PairDataset:
    def __init__(self, filepath):
        self.filepath = filepath
        self.examples = []

    def __iter__(self):
        print("open", self.filepath)
        with gzip.open(self.filepath, 'rt') as fIn:
            for line in fIn:
                example = self.get_example(json.loads(line))
                if example is not None:
                    self.examples.append(example)
                    yield example

        while True:
            random.shuffle(self.examples)
            for ex in self.examples:
                yield ex


    def get_example(self, raw_example):
        if isinstance(raw_example, dict):
            return [raw_example['query'], random.choice(raw_example['pos'])]
        else:
            return [raw_example[0], raw_example[1]]


class RedditTitleDataset(PairDataset):
    def get_example(self, raw_example):
        return [self.clean_title(raw_example['title']), raw_example['body']]


    def clean_title(self, text):
        text = text.replace("&", "&").strip()
        if text.startswith("["):
            text = re.sub("^\[[a-zA-Z0-9]+\]", "", text).strip()

        if text.endswith("]"):
            text = re.sub("\[[a-zA-Z0-9\.]+\]$", "", text).strip()

        if text.startswith("/r"):
            text = re.sub("^/[a-zA-Z0-9/]+[;,: \-]+", "", text).strip()

        return text


class StackExchangeTitleBodyDataset(PairDataset):
    def get_example(self, raw_example):
        return raw_example['texts']


class MultiDataset(IterableDataset):
    def __init__(self, filepaths, num_samples):
        self.num_samples = num_samples
        self.datasets = []
        self.data_iterators = []

        for filepath in filepaths:
            if 'reddit_title_text' in filepath:
                dataset = RedditTitleDataset(filepath)
            elif 'stackexchange_archive/jsonl' in filepath:
                dataset = StackExchangeTitleBodyDataset(filepath)
            else:
                dataset = PairDataset(filepath)
            self.datasets.append(dataset)
            self.data_iterators.append(iter(dataset))

    def __len__(self):
        return self.num_samples

    def __iter__(self):
        while True:
            for dataset in self.data_iterators:
                yield next(dataset)

            random.shuffle(self.data_iterators)

    def delete_examples_cache(self):
        for dataset in self.datasets:
            dataset.examples = []



def main():
    ############ Model
    model = AutoModelForSeq2SeqLM.from_pretrained(args.model_name)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name)

    save_steps = 1000

    output_dir = 'output/'+args.name+'-'+args.model_name.replace("/", "-")+'-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    print("Output dir:", output_dir)

    # Write self to path
    os.makedirs(output_dir, exist_ok=True)

    train_script_path = os.path.join(output_dir, 'train_script.py')
    copyfile(__file__, train_script_path)
    with open(train_script_path, 'a') as fOut:
        fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))

    ####

    training_args = Seq2SeqTrainingArguments(
        output_dir=output_dir,
        fp16=args.fp16,
        fp16_backend="amp",
        per_device_train_batch_size=args.batch_size,
        evaluation_strategy="steps",
        save_steps=save_steps,
        logging_steps=100,
        eval_steps=save_steps, #logging_steps,
        warmup_steps=1000,
        save_total_limit=1,
        num_train_epochs=args.epochs,
        report_to="wandb",
    )

    ############ Arguments

    ############ Load datasets


    train_dataset = MultiDataset(args.train_files, args.train_size)
    train_dataset_iter = iter(train_dataset)
    eval_dataset = [next(train_dataset_iter) for _ in range(args.eval_size)]
    train_dataset.delete_examples_cache()  #Make sure dev data is no re-used for training
    print("Target:", eval_dataset[0][0])
    print("Input:", eval_dataset[0][1])

    print("Train dataset len:", len(train_dataset))


    def data_collator(examples):
        targets = [row[0] for row in examples]
        inputs = [row[1] for row in examples]
        label_pad_token_id = -100

        model_inputs = tokenizer(inputs, max_length=args.max_source_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8 if training_args.fp16 else None)

        # Setup the tokenizer for targets
        with tokenizer.as_target_tokenizer():
            labels = tokenizer(targets, max_length=args.max_target_length, padding=True, truncation=True, pad_to_multiple_of=8 if training_args.fp16 else None)

        # replace all tokenizer.pad_token_id in the labels by -100 to ignore padding in the loss.
        labels["input_ids"] = [
            [(l if l != tokenizer.pad_token_id else label_pad_token_id) for l in label] for label in labels["input_ids"]
        ]


        model_inputs["labels"] = torch.tensor(labels["input_ids"])
        return model_inputs

    ## Define the trainer
    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        tokenizer=tokenizer,
        data_collator=data_collator
    )

    ### Save the model
    train_result = trainer.train()
    trainer.save_model()
    
    
if __name__ == "__main__":
    main()

# Script was called via:
#python train_hf_trainer.py --model_name google/t5-v1_1-small --train_files /home/sbert_pretrained_models/datasets/embedding-training-data/msmarco-triplets.jsonl.gz --name msmarco --train_size 2000000