import argparse
import utils.general_utils as utils
from typing import Optional, Dict, Set, List, Tuple, Union
import os
import numpy as np
import pandas as pd
import cobra
from cobra import Model, Reaction, Metabolite
import re
import sys
import csv
from joblib import Parallel, delayed, cpu_count
import utils.rule_parsing  as rulesUtils
import utils.reaction_utils as reactionUtils

# , medium

################################# process args ###############################
def process_args(args :List[str] = None) -> 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("-mo", "--model_upload", type = str,
        help = "path to input file with custom rules, if provided")

    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('-ir', '--input_ras',
                        type=str,
                        required = False,
                        help = 'input ras')
    
    parser.add_argument('-rn', '--name',
                type=str,
                help = 'ras class names')
    
    parser.add_argument('-rs', '--ras_selector',
                        required = True,
                        type=utils.Bool("using_RAS"),
                        help = 'ras selector')

    parser.add_argument('-cc', '--cell_class',
                    type = str,
                    help = 'output of cell class')
    parser.add_argument(
        '-idop', '--output_path', 
        type = str,
        default='ras_to_bounds/',
        help = 'output path for maps')
    
    parser.add_argument('-sm', '--save_models',
                    type=utils.Bool("save_models"),
                    default=False,
                    help = 'whether to save models with applied bounds')
    
    parser.add_argument('-smp', '--save_models_path',
                        type = str,
                        default='saved_models/',
                        help = 'output path for saved models')
    
    parser.add_argument('-smf', '--save_models_format',
                        type = str,
                        default='csv',
                        help = 'format for saved models (csv, xml, json, mat, yaml, tabular)')

    
    ARGS = parser.parse_args(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
    """
    if ARGS.out_log:
        with open(ARGS.out_log, 'a') as log:
            log.write(s + "\n\n")
    print(s)

############################ dataset input ####################################
def read_dataset(data :str, name :str) -> pd.DataFrame:
    """
    Read a dataset from a CSV file and return it as a pandas DataFrame.

    Args:
        data (str): Path to the CSV file containing the dataset.
        name (str): Name of the dataset, used in error messages.

    Returns:
        pandas.DataFrame: DataFrame containing the dataset.

    Raises:
        pd.errors.EmptyDataError: If the CSV file is empty.
        sys.exit: If the CSV file has the wrong format, the execution is aborted.
    """
    try:
        dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
    except pd.errors.EmptyDataError:
        sys.exit('Execution aborted: wrong format of ' + name + '\n')
    if len(dataset.columns) < 2:
        sys.exit('Execution aborted: wrong format of ' + name + '\n')
    return dataset


def apply_ras_bounds(bounds, ras_row):
    """
    Adjust the bounds of reactions in the model based on RAS values.

    Args:
        bounds (pd.DataFrame): Model bounds.
        ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
    Returns:
        new_bounds (pd.DataFrame): integrated bounds.
    """
    new_bounds = bounds.copy()
    for reaction in ras_row.index:
        scaling_factor = ras_row[reaction]
        if not np.isnan(scaling_factor):
            lower_bound=bounds.loc[reaction, "lower_bound"]
            upper_bound=bounds.loc[reaction, "upper_bound"]
            valMax=float((upper_bound)*scaling_factor)
            valMin=float((lower_bound)*scaling_factor)
            if upper_bound!=0 and lower_bound==0:
                new_bounds.loc[reaction, "upper_bound"] = valMax
            if upper_bound==0 and lower_bound!=0:
                new_bounds.loc[reaction, "lower_bound"] = valMin
            if upper_bound!=0 and lower_bound!=0:
                new_bounds.loc[reaction, "lower_bound"] = valMin
                new_bounds.loc[reaction, "upper_bound"] = valMax
    return new_bounds

################################- DATA GENERATION -################################
ReactionId = str
def generate_rules(model: cobra.Model, *, asParsed = True) -> Union[Dict[ReactionId, rulesUtils.OpList], Dict[ReactionId, str]]:
    """
    Generates a dictionary mapping reaction ids to rules from the model.

    Args:
        model : the model to derive data from.
        asParsed : if True parses the rules to an optimized runtime format, otherwise leaves them as strings.

    Returns:
        Dict[ReactionId, rulesUtils.OpList] : the generated dictionary of parsed rules.
        Dict[ReactionId, str] : the generated dictionary of raw rules.
    """
    # Is the below approach convoluted? yes
    # Ok but is it inefficient? probably
    # Ok but at least I don't have to repeat the check at every rule (I'm clinically insane)
    _ruleGetter   =  lambda reaction : reaction.gene_reaction_rule
    ruleExtractor = (lambda reaction :
        rulesUtils.parseRuleToNestedList(_ruleGetter(reaction))) if asParsed else _ruleGetter

    return {
        reaction.id : ruleExtractor(reaction)
        for reaction in model.reactions
        if reaction.gene_reaction_rule }

def generate_reactions(model :cobra.Model, *, asParsed = True) -> Dict[ReactionId, str]:
    """
    Generates a dictionary mapping reaction ids to reaction formulas from the model.

    Args:
        model : the model to derive data from.
        asParsed : if True parses the reactions to an optimized runtime format, otherwise leaves them as they are.

    Returns:
        Dict[ReactionId, str] : the generated dictionary.
    """

    unparsedReactions = {
        reaction.id : reaction.reaction
        for reaction in model.reactions
        if reaction.reaction 
    }

    if not asParsed: return unparsedReactions
    
    return reactionUtils.create_reaction_dict(unparsedReactions)

def get_medium(model:cobra.Model) -> pd.DataFrame:
    trueMedium=[]
    for r in model.reactions:
        positiveCoeff=0
        for m in r.metabolites:
            if r.get_coefficient(m.id)>0:
                positiveCoeff=1;
        if (positiveCoeff==0 and r.lower_bound<0):
            trueMedium.append(r.id)

    df_medium = pd.DataFrame()
    df_medium["reaction"] = trueMedium
    return df_medium

def generate_bounds(model:cobra.Model) -> pd.DataFrame:

    rxns = []
    for reaction in model.reactions:
        rxns.append(reaction.id)

    bounds = pd.DataFrame(columns = ["lower_bound", "upper_bound"], index=rxns)

    for reaction in model.reactions:
        bounds.loc[reaction.id] = [reaction.lower_bound, reaction.upper_bound]
    return bounds



def generate_compartments(model: cobra.Model) -> pd.DataFrame:
    """
    Generates a DataFrame containing compartment information for each reaction.
    Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
    
    Args:
        model: the COBRA model to extract compartment data from.
        
    Returns:
        pd.DataFrame: DataFrame with ReactionID and compartment columns
    """
    pathway_data = []

    # First pass: determine the maximum number of pathways any reaction has
    max_pathways = 0
    reaction_pathways = {}

    for reaction in model.reactions:
        # Get unique pathways from all metabolites in the reaction
        if type(reaction.annotation['pathways']) == list:
            reaction_pathways[reaction.id] = reaction.annotation['pathways']
            max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
        else:
            reaction_pathways[reaction.id] = [reaction.annotation['pathways']]

    # Create column names for pathways
    pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]

    # Second pass: create the data
    for reaction_id, pathways in reaction_pathways.items():
        row = {"ReactionID": reaction_id}
        
        # Fill pathway columns
        for i in range(max_pathways):
            col_name = pathway_columns[i]
            if i < len(pathways):
                row[col_name] = pathways[i]
            else:
                row[col_name] = None  # or "" if you prefer empty strings

        pathway_data.append(row)

    return pd.DataFrame(pathway_data)

def save_model(model, filename, output_folder, file_format='csv'):
    """
    Save a COBRA model to file in the specified format.
    
    Args:
        model (cobra.Model): The model to save.
        filename (str): Base filename (without extension).
        output_folder (str): Output directory.
        file_format (str): File format ('xml', 'json', 'mat', 'yaml', 'tabular', 'csv').
    
    Returns:
        None
    """
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    
    try:
        if file_format == 'tabular' or file_format == 'csv':
            # Special handling for tabular format using utils functions
            filepath = os.path.join(output_folder, f"{filename}.csv")
            
            rules = generate_rules(model, asParsed = False)
            reactions = generate_reactions(model, asParsed = False)
            bounds = generate_bounds(model)
            medium = get_medium(model)
            
            try:
                compartments = utils.generate_compartments(model)
            except:
                compartments = None

            df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
            df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
            df_bounds = bounds.reset_index().rename(columns = {"index": "ReactionID"})
            df_medium = medium.rename(columns = {"reaction": "ReactionID"})
            df_medium["InMedium"] = True # flag per indicare la presenza nel medium

            merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
            merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
            
            # Add compartments only if they exist and model name is ENGRO2
            if compartments is not None and hasattr(ARGS, 'name') and ARGS.name == "ENGRO2": 
                merged = merged.merge(compartments, on = "ReactionID", how = "outer")
            
            merged = merged.merge(df_medium, on = "ReactionID", how = "left")
            merged["InMedium"] = merged["InMedium"].fillna(False)
            merged = merged.sort_values(by = "InMedium", ascending = False)
            
            merged.to_csv(filepath, sep="\t", index=False)
            
        else:
            # Standard COBRA formats
            filepath = os.path.join(output_folder, f"{filename}.{file_format}")
            
            if file_format == 'xml':
                cobra.io.write_sbml_model(model, filepath)
            elif file_format == 'json':
                cobra.io.save_json_model(model, filepath)
            elif file_format == 'mat':
                cobra.io.save_matlab_model(model, filepath)
            elif file_format == 'yaml':
                cobra.io.save_yaml_model(model, filepath)
            else:
                raise ValueError(f"Unsupported format: {file_format}")
        
        print(f"Model saved: {filepath}")
        
    except Exception as e:
        warning(f"Error saving model {filename}: {str(e)}")

def apply_bounds_to_model(model, bounds):
    """
    Apply bounds from a DataFrame to a COBRA model.
    
    Args:
        model (cobra.Model): The metabolic model to modify.
        bounds (pd.DataFrame): DataFrame with reaction bounds.
    
    Returns:
        cobra.Model: Modified model with new bounds.
    """
    model_copy = model.copy()
    for reaction_id in bounds.index:
        try:
            reaction = model_copy.reactions.get_by_id(reaction_id)
            reaction.lower_bound = bounds.loc[reaction_id, "lower_bound"]
            reaction.upper_bound = bounds.loc[reaction_id, "upper_bound"]
        except KeyError:
            # Reaction not found in model, skip
            continue
    return model_copy

def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder, save_models=False, save_models_path='saved_models/', save_models_format='csv'):
    """
    Process a single RAS cell, apply bounds, and save the bounds to a CSV file.

    Args:
        cellName (str): The name of the RAS cell (used for naming the output file).
        ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
        model (cobra.Model): The metabolic model to be modified.
        rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
        output_folder (str): Folder path where the output CSV file will be saved.
        save_models (bool): Whether to save models with applied bounds.
        save_models_path (str): Path where to save models.
        save_models_format (str): Format for saved models.
    
    Returns:
        None
    """
    bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
    new_bounds = apply_ras_bounds(bounds, ras_row)
    new_bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
    
    # Save model if requested
    if save_models:
        modified_model = apply_bounds_to_model(model, new_bounds)
        save_model(modified_model, cellName, save_models_path, save_models_format)
    
    pass

def generate_bounds_model(model: cobra.Model, ras=None, output_folder='output/', save_models=False, save_models_path='saved_models/', save_models_format='csv') -> pd.DataFrame:
    """
    Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments.
    
    Args:
        model (cobra.Model): The metabolic model for which bounds will be generated.
        ras (pd.DataFrame, optional): RAS pandas dataframe. Defaults to None.
        output_folder (str, optional): Folder path where output CSV files will be saved. Defaults to 'output/'.
        save_models (bool): Whether to save models with applied bounds.
        save_models_path (str): Path where to save models.
        save_models_format (str): Format for saved models.

    Returns:
        pd.DataFrame: DataFrame containing the bounds of reactions in the model.
    """
    rxns_ids = [rxn.id for rxn in model.reactions]            
            
    # Perform Flux Variability Analysis (FVA) on this medium
    df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
    
    # Set FVA bounds
    for reaction in rxns_ids:
        model.reactions.get_by_id(reaction).lower_bound = float(df_FVA.loc[reaction, "minimum"])
        model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"])

    if ras is not None:
        Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(
            cellName, ras_row, model, rxns_ids, output_folder, 
            save_models, save_models_path, save_models_format
        ) for cellName, ras_row in ras.iterrows())
    else:
        bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
        newBounds = apply_ras_bounds(bounds, pd.Series([1]*len(rxns_ids), index=rxns_ids))
        newBounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)

        # Save model if requested
        if save_models:
            modified_model = apply_bounds_to_model(model, newBounds)
            save_model(modified_model, "model_with_bounds", save_models_path, save_models_format)
    
    pass

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

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

    global ARGS
    ARGS = process_args(args)

    if(ARGS.ras_selector == True):
        ras_file_list = ARGS.input_ras.split(",")
        ras_file_names = ARGS.name.split(",")
        if len(ras_file_names) != len(set(ras_file_names)):
            error_message = "Duplicated file names in the uploaded RAS matrices."
            warning(error_message)
            raise ValueError(error_message)
            pass
        ras_class_names = []
        for file in ras_file_names:
            ras_class_names.append(file.rsplit(".", 1)[0])
        ras_list = []
        class_assignments = pd.DataFrame(columns=["Patient_ID", "Class"])
        for ras_matrix, ras_class_name in zip(ras_file_list, ras_class_names):
            ras = read_dataset(ras_matrix, "ras dataset")
            ras.replace("None", None, inplace=True)
            ras.set_index("Reactions", drop=True, inplace=True)
            ras = ras.T
            ras = ras.astype(float)
            if(len(ras_file_list)>1):
                #append class name to patient id (dataframe index)
                ras.index = [f"{idx}_{ras_class_name}" for idx in ras.index]
            else:
                ras.index = [f"{idx}" for idx in ras.index]
            ras_list.append(ras)
            for patient_id in ras.index:
                class_assignments.loc[class_assignments.shape[0]] = [patient_id, ras_class_name]
        
        
        # Concatenate all ras DataFrames into a single DataFrame
        ras_combined = pd.concat(ras_list, axis=0)
        # Normalize the RAS values by max RAS
        ras_combined = ras_combined.div(ras_combined.max(axis=0))
        ras_combined.dropna(axis=1, how='all', inplace=True)

    model = utils.build_cobra_model_from_csv(ARGS.model_upload)

    validation = utils.validate_model(model)

    print("\n=== VALIDAZIONE MODELLO ===")
    for key, value in validation.items():
        print(f"{key}: {value}")

    if(ARGS.ras_selector == True):
        generate_bounds_model(model, ras=ras_combined, output_folder=ARGS.output_path,
                       save_models=ARGS.save_models, save_models_path=ARGS.save_models_path,
                       save_models_format=ARGS.save_models_format)
        class_assignments.to_csv(ARGS.cell_class, sep='\t', index=False)
    else:
        generate_bounds_model(model, output_folder=ARGS.output_path,
                       save_models=ARGS.save_models, save_models_path=ARGS.save_models_path,
                       save_models_format=ARGS.save_models_format)

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