import importlib
import concurrent.futures
import time
from functools import wraps

from data_pipeline.etl.score.etl_score import ScoreETL
from data_pipeline.etl.score.etl_score_geo import GeoScoreETL
from data_pipeline.etl.score.etl_score_post import PostScoreETL

from . import constants


def get_datasets_to_run(dataset_to_run: str):
    """Returns a list of appropriate datasets to run given input args

    Args:
        dataset_to_run (str): Run a specific ETL process. If missing, runs all processes (optional)

    Returns:
        None
    """
    dataset_list = constants.DATASET_LIST
    etls_to_search = dataset_list + [constants.CENSUS_INFO]

    if dataset_to_run:
        dataset_element = next(
            (item for item in etls_to_search if item["name"] == dataset_to_run),
            None,
        )
        if not dataset_element:
            raise ValueError("Invalid dataset name")
        else:
            # reset the list to just the dataset
            dataset_list = [dataset_element]
    return dataset_list


def build_instance_class_from_dataset(dataset: str = None) -> None:
    """Runs a specific dataset

    Args:
        dataset (str): A specific dataset eligible for a specific ETL process.

    Returns:
        None
    """
    etl_module = importlib.import_module(
        f"data_pipeline.etl.sources.{dataset['module_dir']}.etl"
    )
    etl_class = getattr(etl_module, dataset["class_name"])
    etl_instance = etl_class()

    etl_module = importlib.import_module(
        f"data_pipeline.etl.sources.{dataset['module_dir']}.etl"
    )
    etl_class = getattr(etl_module, dataset["class_name"])
    etl_instance = etl_class()

    # run extract
    etl_instance.extract()

    # run transform
    etl_instance.transform()

    # run load
    etl_instance.load()

    # cleanup
    etl_instance.cleanup()


import time
from functools import wraps


def timeit(method):
    @wraps(method)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = method(*args, **kwargs)
        end_time = time.time()
        print(f"{method.__name__} => {(end_time - start_time)*1000} ms")

        return result

    return wrapper


@timeit
def etl_runner(dataset_to_run: str = None) -> None:
    """Runs all etl processes or a specific one

    Args:
        dataset_to_run (str): Run a specific ETL process. If missing, runs all processes (optional)

    Returns:
        None
    """
    dataset_list = get_datasets_to_run(dataset_to_run)

    # Create a pool of processes. By default, one is created for each CPU in your machine.
    with concurrent.futures.ProcessPoolExecutor() as executor:
        # Process the list of ETL datasets, but split the work across the
        # process pool to use all CPUs
        for dataset, _ in zip(
            dataset_list,
            executor.map(
                build_instance_class_from_dataset, dataset_list
            ),
        ):
            print(f"Completed ETL for {dataset}")

    # update the front end JSON/CSV of list of data sources
    pass


def score_generate() -> None:
    """Generates the score and saves it on the local data directory

    Args:
        None

    Returns:
        None
    """

    # Score Gen
    score_gen = ScoreETL()
    score_gen.extract()
    score_gen.transform()
    score_gen.load()


def score_post(data_source: str = "local") -> None:
    """Posts the score files to the local directory

    Args:
        data_source (str): Source for the census data (optional)
                           Options:
                           - local (default): fetch census data from the local data directory
                           - aws: fetch census from AWS S3 J40 data repository

    Returns:
        None
    """
    # Post Score Processing
    score_post = PostScoreETL(data_source=data_source)
    score_post.extract()
    score_post.transform()
    score_post.load()
    score_post.cleanup()


def score_geo(data_source: str = "local") -> None:
    """Generates the geojson files with score data baked in

    Args:
        data_source (str): Source for the census data (optional)
                           Options:
                           - local (default): fetch census data from the local data directory
                           - aws: fetch census from AWS S3 J40 data repository

    Returns:
        None
    """

    # Score Geo
    score_geo = GeoScoreETL(data_source=data_source)
    score_geo.extract()
    score_geo.transform()
    score_geo.load()


def _find_dataset_index(dataset_list, key, value):
    for i, element in enumerate(dataset_list):
        if element[key] == value:
            return i
    return -1