import pandas as pd
from multiprocessing import Pool

def get_encounter_dates(df_list, pif_key):
    encounter_dates = []
    for df in df_list:
        df['encounter_date'].fillna('', inplace=True)
        df_date = df.loc[df['pif_key'].astype(str) == str(pif_key), 'encounter_date'].values
        if len(df_date) > 0:
            encounter_dates.extend(df_date)
    return encounter_dates

def get_latest_date(encounter_dates):
    if encounter_dates:
        return max(encounter_dates)
    return ''

def create_date_insert_dict(ingested_date):
    return {
        'attribute_name': 'report_date',
        'attribute_method': 'cv',
        'attribute_normalized_prediction': '',
        'attribute_prediction': str(ingested_date),
        'attribute_version': 'v2_090523',
        'attribute_vocab': '',
        'attribute_code': '',
        'date_of_service': ''
    }

def add_logging_entry(logging_df, pif_key, json_report_date_exists, encounter_dates, ingested_date, multiple_date, old_date):
    logging_df = logging_df.append({
        'pif_key': pif_key,
        'json_report_date_exists': json_report_date_exists,
        'encounter_dates': encounter_dates,
        'ingested_date': ingested_date,
        'multiple_date': multiple_date,
        'old_date': old_date
    }, ignore_index=True)
    return logging_df

def date_dict(df_list, pif_key):
    encounter_dates = get_encounter_dates(df_list, pif_key)
    ingested_date = get_latest_date(encounter_dates)
    return create_date_insert_dict(ingested_date), encounter_dates, ingested_date

def report_date_insertion(dict_list, df_list, logging_df):
    col_names = {col['attribute_name'] for col in dict_list}
    pif_key = next((col['attribute_prediction'] for col in dict_list if col['attribute_name'] == 'pif_key'), None)
    
    if 'report_date' not in col_names and pif_key is not None:
        date_insert_dict, encounter_dates, ingested_date = date_dict(df_list, pif_key)
        dict_list.insert(1, date_insert_dict)
        logging_df = add_logging_entry(logging_df, pif_key, False, encounter_dates, ingested_date, len(encounter_dates) > 1, 'missing' if not ingested_date else '')

    elif 'report_date' in col_names and pif_key is not None:
        date_insert_dict, encounter_dates, ingested_date = date_dict(df_list, pif_key)
        if ingested_date:
            for report_date_idx, tm in enumerate(dict_list):
                if tm['attribute_name'] == 'report_date':
                    old_date = tm['attribute_prediction']
                    break
            dict_list.pop(report_date_idx)
            dict_list.insert(1, date_insert_dict)
            logging_df = add_logging_entry(logging_df, pif_key, True, encounter_dates, ingested_date, len(encounter_dates) > 1, old_date)
        else:
            for report_date_idx, tm in enumerate(dict_list):
                if tm['attribute_name'] == 'report_date':
                    old_date = tm['attribute_prediction']
                    break
            logging_df = add_logging_entry(logging_df, pif_key, True, None, '', False, old_date)

    return dict_list, logging_df

def process_biomarker_detail(args):
    biomarker_detail, df_list, logging_df = args
    attributes = biomarker_detail['attribute']
    for attribute in attributes:
        attribute_details = attribute['attribute_details']
        attribute['attribute_details'], logging_df = report_date_insertion(attribute_details, df_list, logging_df)
    return biomarker_detail, logging_df

def json_report_date_insertion(json_data, df_list, logging_df):
    biomarker_details = json_data['patient_level']['biomarkers']['details']
    with Pool() as pool:
        results = pool.map(process_biomarker_detail, [(biomarker_detail, df_list, logging_df) for biomarker_detail in biomarker_details])
    updated_biomarker_details, updated_logging_df = zip(*results)
    json_data['patient_level']['biomarkers']['details'] = updated_biomarker_details
    return json_data, updated_logging_df