import ast
import logging
import os
import sys
from dataclasses import dataclass, field

import pandas as pd
from tqdm import tqdm
from typing import Dict, List, Optional, Tuple

from datasets import load_dataset
from transformers import (
    HfArgumentParser,
)

logger = logging.getLogger(__name__)


@dataclass
class DataArguments:
    """
    Arguments to which dataset we are going to set up.
    """

    output_dir: str = field(
        default=".",
        metadata={"help": "The output directory where the config will be written."},
    )
    dataset_name: str = field(
        default=None,
        metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_data_dir: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )


def main():
    parser = HfArgumentParser([DataArguments])
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
    else:
        data_args = parser.parse_args_into_dataclasses()[0]

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

    logger.info(f"Preparing the dataset")

    if data_args.dataset_name is not None:
        dataset = load_dataset(
            data_args.dataset_name,
            data_dir=data_args.dataset_data_dir,
            cache_dir=data_args.cache_dir
        )
    else:
        dataset = load_dataset(
            data_args.dataset_name,
            cache_dir=data_args.cache_dir
        )

    def cleaning(text, item_type="ner"):
        # NOTE: DO THE CLEANING LATER
        return text

    def recipe_preparation(item_dict):
        requirements = ["ner", "ingredients", "steps"]
        constraints = [3, 3, 10]
        if not all([
            True if requirements[i] in item_dict and len(item_dict[requirements[i]].split()) > constraints[i] else False
            for i in range(len(requirements))
        ]):
            return None

        ner = cleaning(item_dict["ner"], "ner")
        ingredients = cleaning(item_dict["ingredients"], "ingredients")
        steps = cleaning(item_dict["steps"], "steps")

        return {
            "inputs": ner,
            "targets": f"{ingredients}<sep>{steps}"
        }

    for subset in dataset.keys():
        data_dict = []
        for item in tqdm(dataset[subset], position=0, total=len(dataset[subset])):
            item = recipe_preparation(item)
            if item:
                data_dict.append(item)

        data_df = pd.DataFrame(data_dict)
        logger.info(f"Preparation of [{subset}] set consists of {len(data_df)} records!")

        output_path = os.path.join(data_args.output_dir, f"{subset}.csv")
        os.makedirs(os.path.dirname(output_path), exist_ok=True)
        data_df.to_csv(output_path, sep="\t", encoding="utf-8", index=False)
        logger.info(f"Data saved here {output_path}")


if __name__ == '__main__':
    main()