import gedi.config as config
import pandas as pd
from datetime import datetime as dt
from gedi.generator import GenerateEventLogs
from gedi.features import EventLogFeatures
from gedi.augmentation import InstanceAugmentator
from gedi.benchmark import BenchmarkTest
from gedi.plotter import BenchmarkPlotter, FeaturesPlotter, AugmentationPlotter, GenerationPlotter
from utils.default_argparse import ArgParser
from utils.param_keys import PARAMS, PIPELINE_STEP

def run(kwargs:dict, model_params_list: list, filename_list:list):
    """
    This function chooses the running option for the program.
    @param kwargs: dict
        contains the running parameters and the event-log file information
    @param model_params_list: list
        contains a list of model parameters, which are used to analyse this different models.
    @param filename_list: list
        contains the list of the filenames to load multiple event-logs
    @return:
    """
    params = kwargs[PARAMS]
    ft = EventLogFeatures(None)
    augmented_ft = InstanceAugmentator()
    gen = pd.DataFrame(columns=['log'])

    for model_params in model_params_list:
        if model_params.get(PIPELINE_STEP) == 'instance_augmentation':
            augmented_ft = InstanceAugmentator(aug_params=model_params, samples=ft.feat)
            AugmentationPlotter(augmented_ft, model_params)
        elif model_params.get(PIPELINE_STEP) == 'event_logs_generation':
            gen = pd.DataFrame(GenerateEventLogs(model_params).log_config)
            #gen = pd.read_csv("output/features/generated/grid_2objectives_enseef_enve/2_enseef_enve_feat.csv")
            #GenerationPlotter(gen, model_params, output_path="output/plots")
        elif model_params.get(PIPELINE_STEP) == 'benchmark_test':
            benchmark = BenchmarkTest(model_params, event_logs=gen['log'])
            # BenchmarkPlotter(benchmark.features, output_path="output/plots")
        elif model_params.get(PIPELINE_STEP) == 'feature_extraction':
            ft = EventLogFeatures(**kwargs, logs=gen['log'], ft_params=model_params)
            FeaturesPlotter(ft.feat, model_params)
        elif model_params.get(PIPELINE_STEP) == "evaluation_plotter":
            GenerationPlotter(gen, model_params, output_path=model_params['output_path'], input_path=model_params['input_path'])

def gedi(config_path):
    """
    This function runs the GEDI pipeline.
    @param config_path: str
        contains the path to the config file
    @return:
    """
    model_params_list = config.get_model_params_list(config_path)
    run({'params':""}, model_params_list, [])