import argparse
import utils.general_utils as utils
from typing import Optional, List
import os
import numpy as np
import pandas as pd
import cobra
from joblib import Parallel, delayed, cpu_count
import sys

################################# process args ###############################
def process_args(args :List[str]) -> argparse.Namespace:
    """
    Processes command-line arguments.

    Args:
        args (list): List of command-line arguments.

    Returns:
        Namespace: An object containing parsed arguments.
    """
    parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
                                     description = 'process some value\'s')
    
    parser.add_argument(
        '-ms', '--model_selector', 
        type = utils.Model, default = utils.Model.ENGRO2, choices = [utils.Model.ENGRO2, utils.Model.Custom],
        help = 'chose which type of model you want use')
    
    parser.add_argument("-mo", "--model", type = str,
        help = "path to input file with custom rules, if provided")

    parser.add_argument("-mn", "--model_name", type = str, help = "custom mode name")

    parser.add_argument('-ol', '--out_log', 
                        help = "Output log")
    
    parser.add_argument('-td', '--tool_dir',
                        type = str,
                        required = True,
                        help = 'your tool directory')
    
    
    parser.add_argument('-im', '--input_medium',
                        required = True,
                        type=str,
                        help = 'input medium')
    
    parser.add_argument('-ir', '--input_ras',
                        required = True,
                        type=str,
                        help = 'input ras')
    
    parser.add_argument('-ot', '--output_type', 
                        type = str,
                        required = True,
                        help = 'output type')ù
    
    parser.add_argument('-of', '--output_model_format', 
                        type = str,
                        required = True,
                        help = 'output type')
    ARGS = parser.parse_args()
    return ARGS

########################### warning ###########################################
def warning(s :str) -> None:
    """
    Log a warning message to an output log file and print it to the console.

    Args:
        s (str): The warning message to be logged and printed.
    
    Returns:
      None
    """
    with open(ARGS.out_log, 'a') as log:
        log.write(s + "\n\n")
    print(s)


def write_to_file(dataset: pd.DataFrame, name: str, keep_index:bool=False)->None:
    dataset.to_csv(ARGS.output_folder + name + ".csv", sep = '\t', index = keep_index)


def generate_model(model, cell_name, ras, medium, output_model_format):
    model_new = model.copy()
    rxns_ids = []
    for rxn in model.reactions:
        rxns_ids.append(rxn.id)
    for reaction in medium.keys():
        if(medium[reaction] != None):
            model_new.reactions.get_by_id(reaction).lower_bound=-float(medium[reaction])
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model_new,fraction_of_optimum=0,processes=1).round(8)
    for reaction in rxns_ids:
        model_new.reactions.get_by_id(reaction).lower_bound=float(df_FVA.loc[reaction,"minimum"])
        model_new.reactions.get_by_id(reaction).upper_bound=float(df_FVA.loc[reaction,"maximum"])

    for reaction in rxns_ids:
        if reaction in ras.keys():
            lower_bound=model_new.reactions.get_by_id(reaction).lower_bound
            upper_bound=model_new.reactions.get_by_id(reaction).upper_bound
            valMax=float((upper_bound)*ras[reaction])
            valMin=float((lower_bound)*ras[reaction])
            if upper_bound!=0 and lower_bound==0:
                model_new.reactions.get_by_id(reaction).upper_bound=valMax
            if upper_bound==0 and lower_bound!=0:
                model_new.reactions.get_by_id(reaction).lower_bound=valMin
            if upper_bound!=0 and lower_bound!=0:
                model_new.reactions.get_by_id(reaction).lower_bound=valMin
                model_new.reactions.get_by_id(reaction).upper_bound=valMax
    return model_new

    if(output_model_format == "SBML"):
        cobra.io.write_sbml_model(model_new, ARGS.output_folder + cell_name+ "/" + cell_name + ".xml")
    else:
        cobra.io.save_json_model(model_new, ARGS.output_folder + cell_name+ "/" + cell_name + ".json")

    pass


############################# main ###########################################
def main() -> None:
    """
    Initializes everything and sets the program in motion based on the fronted input arguments.

    Returns:
        None
    """
    if not os.path.exists('model_generator'):
        os.makedirs('model_generator')

    num_processors = cpu_count()

    global ARGS
    ARGS = process_args(sys.argv)

    ARGS.output_folder = 'model_generator/'
    
    ARGS.output_types = ARGS.output_type.split(",")

    ras = pd.read_table(ARGS.input_ras, header=0, sep=r'\s+', index_col = 0).T
    ras.replace("None", None, inplace=True)
    ras = ras.astype(float)

    #medium has rows cells and columns medium reactions, not common reactions set to None
    medium = pd.read_csv(ARGS.input_medium, sep = '\t', header = 0, engine='python', index_col = 0)
    medium = ras.astype(float)

    model_type :utils.Model = ARGS.model_selector
    if model_type is utils.Model.Custom:
        model = model_type.getCOBRAmodel(customPath = utils.FilePath.fromStrPath(ARGS.model), customExtension = utils.FilePath.fromStrPath(ARGS.model_name).ext)
    else:
        model = model_type.getCOBRAmodel(toolDir=ARGS.tool_dir)

    for index, row in ras.iterrows(): #iterate over cells RAS
        generate_model(model, index, row, medium.loc[index], ARGS.output_model_format)

    pass
        
##############################################################################
if __name__ == "__main__":
    main()