import os
import json
import pandas as pd
from glob import glob
from tqdm import tqdm
import time
from utils.logger import logger
from utils.pdf_download import download_pdf_from_documents_warehouse
from utils.sql_query_util import query_document_fund_mapping
from core.page_filter import FilterPages
from core.metrics import Metrics


class EMEA_AR_Parsing:
    def __init__(self, doc_id: str, pdf_folder: str = r"/data/emea_ar/pdf/") -> None:
        self.doc_id = doc_id
        self.pdf_folder = pdf_folder
        os.makedirs(self.pdf_folder, exist_ok=True)
        self.pdf_file = self.download_pdf()
        self.document_mapping_info_df = query_document_fund_mapping(doc_id)
        self.datapoint_page_info, self.result_details = self.get_datapoint_page_info()

    def download_pdf(self) -> str:
        pdf_file = download_pdf_from_documents_warehouse(self.pdf_folder, self.doc_id)
        return pdf_file

    def get_datapoint_page_info(self) -> dict:
        filter_pages = FilterPages(
            self.doc_id, self.pdf_file, self.document_mapping_info_df
        )
        datapoint_page_info, result_details = filter_pages.start_job()
        return datapoint_page_info, result_details


def filter_pages(doc_id: str, pdf_folder: str) -> None:
    logger.info(f"Parsing EMEA AR for doc_id: {doc_id}")
    emea_ar_parsing = EMEA_AR_Parsing(doc_id, pdf_folder)
    return emea_ar_parsing.datapoint_page_info, emea_ar_parsing.result_details


def batch_filter_pdf_files(
    pdf_folder: str,
    doc_data_excel_file: str = None,
    output_folder: str = r"/data/emea_ar/output/filter_pages/",
    special_doc_id_list: list = None,
) -> None:
    pdf_files = glob(pdf_folder + "*.pdf")
    doc_list = []
    if special_doc_id_list is not None and len(special_doc_id_list) > 0:
        doc_list = special_doc_id_list
    if (
        len(doc_list) == 0
        and doc_data_excel_file is not None
        and len(doc_data_excel_file) > 0
        and os.path.exists(doc_data_excel_file)
    ):
        doc_data_df = pd.read_excel(doc_data_excel_file)
        doc_data_df = doc_data_df[doc_data_df["Checked"] == 1]
        doc_list = [str(doc_id) for doc_id in doc_data_df["doc_id"].tolist()]
    result_list = []
    result_details = []
    for pdf_file in tqdm(pdf_files):
        pdf_base_name = os.path.basename(pdf_file)
        doc_id = pdf_base_name.split(".")[0]
        if doc_list is not None and doc_id not in doc_list:
            continue
        doc_datapoint_page_info, doc_result_details = filter_pages(doc_id=doc_id, pdf_folder=pdf_folder)
        result_list.append(doc_datapoint_page_info)
        result_details.extend(doc_result_details)
    
    result_df = pd.DataFrame(result_list)
    result_df.reset_index(drop=True, inplace=True)
    
    result_details_df = pd.DataFrame(result_details)
    result_details_df.reset_index(drop=True, inplace=True)

    logger.info(f"Saving the result to {output_folder}")
    os.makedirs(output_folder, exist_ok=True)
    time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime())
    output_file = os.path.join(
        output_folder,
        f"datapoint_page_info_{len(result_df)}_documents_{time_stamp}.xlsx",
    )
    with pd.ExcelWriter(output_file) as writer:
        result_df.to_excel(writer, index=False, sheet_name="dp_page_info")
        result_details_df.to_excel(writer, index=False, sheet_name="dp_page_info_details")

    if len(special_doc_id_list) == 0:
        logger.info(f"Calculating metrics for {output_file}")
        metrics_output_folder = r"/data/emea_ar/output/metrics/"
        missing_error_list, metrics_list, metrics_file = get_metrics(
            data_type="page_filter",
            prediction_file=output_file,
            prediction_sheet_name="dp_page_info",
            ground_truth_file=doc_data_excel_file,
            output_folder=metrics_output_folder,
        )
        return missing_error_list, metrics_list, metrics_file


def get_metrics(
    data_type: str,
    prediction_file: str,
    prediction_sheet_name: str,
    ground_truth_file: str,
    output_folder: str = None
) -> None:
    metrics = Metrics(
        data_type=data_type,
        prediction_file=prediction_file,
        prediction_sheet_name=prediction_sheet_name,
        ground_truth_file=ground_truth_file,
        output_folder=output_folder
    )
    missing_error_list, metrics_list, metrics_file = metrics.get_metrics()
    return missing_error_list, metrics_list, metrics_file


if __name__ == "__main__":
    pdf_folder = r"/data/emea_ar/small_pdf/"
    page_filter_ground_truth_file = (
        r"/data/emea_ar/ground_truth/page_filter/datapoint_page_info_88_documents.xlsx"
    )
    prediction_output_folder = r"/data/emea_ar/output/filter_pages/"
    metrics_output_folder = r"/data/emea_ar/output/metrics/"
    special_doc_id_list = []
    batch_filter_pdf_files(
        pdf_folder, page_filter_ground_truth_file, prediction_output_folder, special_doc_id_list
    )

    # data_type = "page_filter"
    # prediction_file = r"/data/emea_ar/output/filter_pages/datapoint_page_info_73_documents_20240903145002.xlsx"
    # missing_error_list, metrics_list, metrics_file = get_metrics(
    #     data_type, prediction_file, page_filter_ground_truth_file, metrics_output_folder
    # )