dc-ml-emea-ar/prepare_data.py

import pandas as pd
import os
from tqdm import tqdm
import json
from glob import glob
import fitz
import re
import time
import traceback
import json_repair

from utils.logger import logger
from utils.pdf_download import download_pdf_from_documents_warehouse
from utils.pdf_util import PDFUtil
from pdf_table_extraction import PDFTableExtraction


def get_unique_docids_from_doc_provider_data(doc_provider_file_path: str):
    doc_provider_data = pd.read_excel(doc_provider_file_path)
    # get new data by grouping by docid, and count the number of rows for each docid,
    # set the new data with 2 columns: docid and provider_count
    doc_provider_count = (
        doc_provider_data.groupby("DocumentId")
        .size()
        .reset_index(name="provider_count")
    )
    # sort new data by provider_count in descending order
    doc_provider_count = doc_provider_count.sort_values(
        by="provider_count", ascending=False
    )

    # save excel by doc_provider_data and new_data
    with pd.ExcelWriter(doc_provider_file_path) as writer:
        doc_provider_data.to_excel(
            writer, sheet_name="doc_provider_details", index=False
        )
        doc_provider_count.to_excel(
            writer, sheet_name="doc_provider_count", index=False
        )


def download_pdf(doc_provider_file_path: str, 
                 sheet_name: str, 
                 pdf_path: str,
                 doc_id_column: str = "DocumentId"):
    document_data = pd.read_excel(doc_provider_file_path, sheet_name=sheet_name)
    # get all unique docids as list
    doc_id_list = [
        str(doc_id) for doc_id in document_data[doc_id_column].unique().tolist()
    ]
    # download pdfs
    logger.info(f"Start downloading {len(doc_id_list)} pdfs")
    os.makedirs(pdf_path, exist_ok=True)
    for doc_id in tqdm(doc_id_list):
        logger.info(f"Downloading pdf for docid: {doc_id}")
        download_pdf_from_documents_warehouse(pdf_directory=pdf_path, doc_id=doc_id)
        time.sleep(1)


def output_pdf_page_text(pdf_folder: str, output_folder: str):
    if pdf_folder is None or len(pdf_folder) == 0 or not os.path.exists(pdf_folder):
        logger.error(f"Invalid pdf_folder: {pdf_folder}")
        return
    if output_folder is None or len(output_folder) == 0:
        logger.error(f"Invalid output_folder: {output_folder}")
        return

    os.makedirs(output_folder, exist_ok=True)
    pdf_files = glob(os.path.join(pdf_folder, "*.pdf"))
    logger.info(f"Total {len(pdf_files)} pdf files found in {pdf_folder}")
    for pdf_file in pdf_files:
        logger.info(f"Start processing {pdf_file}")
        pdf_util = PDFUtil(pdf_file)
        success, text, page_text_dict = pdf_util.extract_text(
            output_folder=output_folder
        )
        if success:
            logger.info(f"Successfully extracted text from {pdf_file}")


def extract_pdf_table(pdf_folder: str, output_folder: str):
    if pdf_folder is None or len(pdf_folder) == 0 or not os.path.exists(pdf_folder):
        logger.error(f"Invalid pdf_folder: {pdf_folder}")
        return
    if output_folder is None or len(output_folder) == 0:
        logger.error(f"Invalid output_folder: {output_folder}")
        return
    os.makedirs(output_folder, exist_ok=True)

    pdf_files = glob(os.path.join(pdf_folder, "*.pdf"))
    logger.info(f"Total {len(pdf_files)} pdf files found in {pdf_folder}")
    for pdf_file in pdf_files:
        logger.info(f"Start processing {pdf_file}")
        pdf_table_extraction = PDFTableExtraction(pdf_file, output_folder)
        pdf_table_extraction.extract_tables()


def analyze_json_error():
    text_file = r"/data/emea_ar/output/pdf_table_prompts/445877368_4.txt"
    with open(text_file, "r", encoding="utf-8") as file:
        text = file.read()
        json_response = re.search(r"\`\`\`json([\s\S]*)\`\`\`", text)
        if json_response:
            json_text = json_response.group(1)
            json_data = {"tables": []}
            try:
                json_data = json.loads(json_text)
            except:
                json_data = json_repair.loads(json_text)
            table_list = json_data.get("tables", [])
            for table_num, table in enumerate(table_list):
                table_md_file = os.path.join("/temp/", f"temp_{table_num}.md")
                table = re.sub(r"(\n)+", "\n", table)
                with open(table_md_file, "w", encoding="utf-8") as file:
                    file.write(table)


def statistics_document(
    pdf_folder: str, 
    doc_mapping_file_path: str,
    doc_ar_data_file_path: str,
    mapping_sheet_name: str = "Sheet1",
    ar_data_sheet_name: str = "doc_ar_data_in_db",
    output_folder: str = "/data/emea_ar/basic_information/English/", 
    output_file: str = "doc_mapping_statistics_data.xlsx"
):
    if pdf_folder is None or len(pdf_folder) == 0 or not os.path.exists(pdf_folder):
        logger.error(f"Invalid pdf_folder: {pdf_folder}")
        return
    if (
        doc_mapping_file_path is None
        or len(doc_mapping_file_path) == 0
        or not os.path.exists(doc_mapping_file_path)
    ):
        logger.error(f"Invalid doc_mapping_file_path: {doc_mapping_file_path}")
        return
    if output_folder is None or len(output_folder) == 0:
        logger.error(f"Invalid output_folder: {output_folder}")
        return
    os.makedirs(output_folder, exist_ok=True)

    describe_stat_df_list = []
    # statistics document mapping information
    doc_mapping_data = pd.read_excel(doc_mapping_file_path, sheet_name=mapping_sheet_name)

    # statistics doc_mapping_data for counting FundId count based on DocumentId
    logger.info(
        "statistics doc_mapping_data for counting FundId count based on DocumentId"
    )
    doc_fund_id_df = doc_mapping_data[["DocumentId", "FundId"]].drop_duplicates()
    doc_fund_count = (
        doc_fund_id_df.groupby("DocumentId").size().reset_index(name="fund_count")
    )
    # order by fund_count in descending order
    doc_fund_count = doc_fund_count.sort_values(by="fund_count", ascending=False)
    # statistics fund_count in doc_fund_count by describe and transform to DataFrame
    doc_fund_count_stat_df = get_describe_stat(
        doc_fund_count, "fund_count", "doc_fund_count"
    )
    describe_stat_df_list.append(doc_fund_count_stat_df)

    # statistics doc_mapping_data for counting FundClassId count based on DocumentId
    logger.info(
        "statistics doc_mapping_data for counting FundClassId count based on DocumentId"
    )
    doc_share_class_id_df = doc_mapping_data[
        ["DocumentId", "FundClassId"]
    ].drop_duplicates()
    doc_share_class_count = (
        doc_share_class_id_df.groupby("DocumentId")
        .size()
        .reset_index(name="share_class_count")
    )
    # order by share_class_count in descending order
    doc_share_class_count = doc_share_class_count.sort_values(
        by="share_class_count", ascending=False
    )
    # statistics share_class_count in doc_share_class_count by describe and transform to DataFrame
    doc_share_class_count_stat_df = get_describe_stat(
        doc_share_class_count, "share_class_count", "doc_share_class_count"
    )
    describe_stat_df_list.append(doc_share_class_count_stat_df)

    # statistics doc_mapping_data for counting FundId count based on CompanyId and CompanyName
    logger.info(
        "statistics doc_mapping_data for counting FundId count based on CompanyId and CompanyName"
    )
    provider_fund_id_df = doc_mapping_data[
        ["CompanyId", "CompanyName", "FundId"]
    ].drop_duplicates()
    provider_fund_count = (
        provider_fund_id_df.groupby(["CompanyId", "CompanyName"])
        .size()
        .reset_index(name="fund_count")
    )
    # order by fund_count in descending order
    provider_fund_count = provider_fund_count.sort_values(
        by="fund_count", ascending=False
    )
    # statistics fund_count in provider_fund_count by describe and transform to DataFrame
    provider_fund_count_stat_df = get_describe_stat(
        provider_fund_count, "fund_count", "provider_fund_count"
    )
    describe_stat_df_list.append(provider_fund_count_stat_df)

    # statistics doc_mapping_data for counting FundClassId count based on CompanyId
    logger.info(
        "statistics doc_mapping_data for counting FundClassId count based on CompanyId"
    )
    provider_share_class_id_df = doc_mapping_data[
        ["CompanyId", "CompanyName", "FundClassId"]
    ].drop_duplicates()
    provider_share_class_count = (
        provider_share_class_id_df.groupby(["CompanyId", "CompanyName"])
        .size()
        .reset_index(name="share_class_count")
    )
    # order by share_class_count in descending order
    provider_share_class_count = provider_share_class_count.sort_values(
        by="share_class_count", ascending=False
    )
    # statistics share_class_count in provider_share_class_count by describe and transform to DataFrame
    provider_share_class_count_stat_df = get_describe_stat(
        provider_share_class_count, "share_class_count", "provider_share_class_count"
    )
    describe_stat_df_list.append(provider_share_class_count_stat_df)

    # statistics doc_mapping_data for counting FundClassId count based on FundId and FundLegalName
    logger.info(
        "statistics doc_mapping_data for counting FundClassId count based on FundId and FundLegalName"
    )
    fund_share_class_id_df = doc_mapping_data[
        ["FundId", "FundLegalName", "FundClassId"]
    ].drop_duplicates()
    fund_share_class_count = (
        fund_share_class_id_df.groupby(["FundId", "FundLegalName"])
        .size()
        .reset_index(name="share_class_count")
    )
    # order by share_class_count in fund_share_class_count
    fund_share_class_count = fund_share_class_count.sort_values(
        by="share_class_count", ascending=False
    )
    # statistics share_class_count in fund_share_class_count by describe and transform to DataFrame
    fund_share_class_count_stat_df = get_describe_stat(
        fund_share_class_count, "share_class_count", "fund_share_class_count"
    )
    describe_stat_df_list.append(fund_share_class_count_stat_df)

    stat_file = os.path.join(output_folder, output_file)
    
    doc_id_list = [str(docid) for docid in doc_mapping_data["DocumentId"].unique().tolist()]
    # statistics document page number
    pdf_files = glob(os.path.join(pdf_folder, "*.pdf"))
    logger.info(f"Total {len(pdf_files)} pdf files found in {pdf_folder}")
    logger.info("statistics document page number")
    doc_page_num_list = []
    for pdf_file in tqdm(pdf_files):
        pdf_base_name = os.path.basename(pdf_file).replace(".pdf", "")
        if pdf_base_name not in doc_id_list:
            continue
        docid = os.path.basename(pdf_file).split(".")[0]
        doc = fitz.open(pdf_file)
        page_num = doc.page_count
        doc_page_num_list.append({"DocumentId": docid, "page_num": page_num})
        doc.close()
    doc_page_num_df = pd.DataFrame(doc_page_num_list)
    # order by page_num in descending order
    doc_page_num_df = doc_page_num_df.sort_values(by="page_num", ascending=False)
    # statistics page_num by describe and transform to DataFrame
    doc_page_num_stat_df = get_describe_stat(
        doc_page_num_df, "page_num", "doc_page_num"
    )
    describe_stat_df_list.append(doc_page_num_stat_df)

    describe_stat_df = pd.concat(describe_stat_df_list)
    describe_stat_df.reset_index(drop=True, inplace=True)
    
    doc_dp_data_df = None
    if doc_ar_data_file_path is not None and os.path.exists(doc_ar_data_file_path):
        doc_ar_data = pd.read_excel(doc_ar_data_file_path, sheet_name=ar_data_sheet_name)
        doc_dp_result = get_document_with_all_4_data_points(None, None, doc_ar_data)
        doc_dp_data_list = []
        for doc_id in doc_id_list:
            doc_id = int(doc_id)
            doc_dp_data = {"DocumentId": doc_id, "tor": 0, "ter": 0, "ogc": 0, "perf_fee": 0}
            if doc_id in doc_dp_result["tor"]:
                doc_dp_data["tor"] = 1
            if doc_id in doc_dp_result["ter"]:
                doc_dp_data["ter"] = 1
            if doc_id in doc_dp_result["ogc"]:
                doc_dp_data["ogc"] = 1
            if doc_id in doc_dp_result["perf_fee"]:
                doc_dp_data["perf_fee"] = 1
            doc_dp_data_list.append(doc_dp_data)
        doc_dp_data_df = pd.DataFrame(doc_dp_data_list)
        doc_dp_data_df = doc_dp_data_df.sort_values(by="DocumentId", ascending=True)
        doc_dp_data_df.reset_index(drop=True, inplace=True)
    
    # set all of DocumentId in DataFrame objects to be string type
    doc_page_num_df["DocumentId"] = doc_page_num_df["DocumentId"].astype(str)
    doc_fund_count["DocumentId"] = doc_fund_count["DocumentId"].astype(str)
    doc_share_class_count["DocumentId"] = doc_share_class_count["DocumentId"].astype(str)
    if doc_dp_data_df is not None:
        doc_dp_data_df["DocumentId"] = doc_dp_data_df["DocumentId"].astype(str)
    
    # merge statistics data for doc_page_num_df, doc_dp_data_df, doc_fund_count, doc_share_class_count based on DocumentId
    doc_page_num_df = doc_page_num_df.merge(doc_fund_count, on="DocumentId", how="left")
    doc_page_num_df = doc_page_num_df.merge(doc_share_class_count, on="DocumentId", how="left")
    if doc_dp_data_df is not None:
        doc_page_num_df = doc_page_num_df.merge(doc_dp_data_df, on="DocumentId", how="left")
    
    # save statistics data to excel
    with pd.ExcelWriter(stat_file) as writer:
        doc_page_num_df.to_excel(writer, sheet_name="doc_level_stats", index=False)
        # doc_dp_data_df.to_excel(writer, sheet_name="doc_dp_data", index=False)
        # doc_fund_count.to_excel(writer, sheet_name="doc_fund_count", index=False)
        # doc_share_class_count.to_excel(
        #     writer, sheet_name="doc_share_class_count", index=False
        # )
        provider_fund_count.to_excel(
            writer, sheet_name="provider_fund_count", index=False
        )
        provider_share_class_count.to_excel(
            writer, sheet_name="provider_share_class_count", index=False
        )
        fund_share_class_count.to_excel(
            writer, sheet_name="fund_share_class_count", index=False
        )
        describe_stat_df.to_excel(
            writer, sheet_name="all_describe_statistics", index=False
        )


def get_document_with_all_4_data_points(folder: str, file_name: str, data: pd.DataFrame):
    if data is None:
        file_path = os.path.join(folder, file_name)
        if os.path.exists(file_path):
            data = pd.read_excel(file_path, sheet_name="doc_ar_data_in_db")
        else:
            logger.error(f"Invalid file path: {file_path}")
            return
    # get document id list which noTor is 0
    noTor_0_doc_id_list = data[data["noTor"] == 0]["DocumentId"].unique().tolist()
    
    # get document id list which share_noTer is 0
    share_noTer_0_doc_id_list = data[data["share_noTer"] == 0]["DocumentId"].unique().tolist()
    
    # get document id list which share_noOgc is 0
    share_noOgc_0_doc_id_list = data[data["share_noOgc"] == 0]["DocumentId"].unique().tolist()
    
    # get document id list which share_noPerfFee is 0
    share_noPerfFee_0_doc_id_list = data[data["share_noPerfFee"] == 0]["DocumentId"].unique().tolist()
    
    logger.info(f"noTor_0_doc_id_list: {len(noTor_0_doc_id_list)}")
    logger.info(f"share_noTer_0_doc_id_list: {len(share_noTer_0_doc_id_list)}")
    logger.info(f"share_noOgc_0_doc_id_list: {len(share_noOgc_0_doc_id_list)}")
    logger.info(f"share_noPerfFee_0_doc_id_list: {len(share_noPerfFee_0_doc_id_list)}")
    
    all_4_data_points_doc_id_list = list(set(noTor_0_doc_id_list) & set(share_noTer_0_doc_id_list) & set(share_noOgc_0_doc_id_list) & set(share_noPerfFee_0_doc_id_list))
    
    logger.info(f"all_4_data_points_doc_id_list: {len(all_4_data_points_doc_id_list)}")
    result = {"tor": noTor_0_doc_id_list,
              "ter": share_noTer_0_doc_id_list,
              "ogc": share_noOgc_0_doc_id_list,
              "perf_fee": share_noPerfFee_0_doc_id_list}
    return result


def statistics_provider_mapping(provider_mapping_data_file: str, output_folder: str):
    if (
        provider_mapping_data_file is None
        or len(provider_mapping_data_file) == 0
        or not os.path.exists(provider_mapping_data_file)
    ):
        logger.error(
            f"Invalid provider_mapping_data_file: {provider_mapping_data_file}"
        )
        return
    provider_mapping_data = pd.read_excel(provider_mapping_data_file)

    describe_stat_df_list = []
    # statistics provider_mapping_data for counting FundId count based on CompanyId and CompanyName
    logger.info(
        "statistics provider_mapping_data for counting FundId count based on CompanyId and CompanyName"
    )
    provider_fund_id_df = provider_mapping_data[
        ["CompanyId", "CompanyName", "FundId"]
    ].drop_duplicates()
    provider_fund_count = (
        provider_fund_id_df.groupby(["CompanyId", "CompanyName"])
        .size()
        .reset_index(name="fund_count")
    )
    # order by fund_count in descending order
    provider_fund_count = provider_fund_count.sort_values(
        by="fund_count", ascending=False
    )
    # statistics fund_count in provider_fund_count by describe and transform to DataFrame
    provider_fund_count_stat_df = get_describe_stat(
        provider_fund_count, "fund_count", "provider_fund_count"
    )
    describe_stat_df_list.append(provider_fund_count_stat_df)

    # Get the fund_count sum of all companies
    all_companies_fund_count_sum = provider_fund_count["fund_count"].sum()

    top_n_company_fund_count_list = []
    # Get the fund_count sum of top 5 companies
    top_5_companies_fund_count, top_5_companies_fund_count_percent = (
        get_top_n_records_count(
            provider_fund_count, "fund_count", 5, all_companies_fund_count_sum
        )
    )
    top_n_company_fund_count_list.append(
        {
            "top_n_providers": 5,
            "fund_count": top_5_companies_fund_count,
            "percent": top_5_companies_fund_count_percent,
        }
    )
    logger.info(f"Top 5 companies fund count sum: {top_5_companies_fund_count}")
    # Get the fund_count sum of top 10 companies
    top_10_companies_fund_count, top_10_companies_fund_count_percent = (
        get_top_n_records_count(
            provider_fund_count, "fund_count", 10, all_companies_fund_count_sum
        )
    )
    top_n_company_fund_count_list.append(
        {
            "top_n_providers": 10,
            "fund_count": top_10_companies_fund_count,
            "percent": top_10_companies_fund_count_percent,
        }
    )
    logger.info(f"Top 10 companies fund count sum: {top_10_companies_fund_count}")

    # Get the fund_count sum of top 50 companies
    top_50_companies_fund_count, top_50_companies_fund_count_percent = (
        get_top_n_records_count(
            provider_fund_count, "fund_count", 50, all_companies_fund_count_sum
        )
    )
    top_n_company_fund_count_list.append(
        {
            "top_n_providers": 50,
            "fund_count": top_50_companies_fund_count,
            "percent": top_50_companies_fund_count_percent,
        }
    )
    logger.info(f"Top 50 companies fund count sum: {top_50_companies_fund_count}")

    # Get the fund_count sum of top 100 companies
    top_100_companies_fund_count, top_100_companies_fund_count_percent = (
        get_top_n_records_count(
            provider_fund_count, "fund_count", 100, all_companies_fund_count_sum
        )
    )
    top_n_company_fund_count_list.append(
        {
            "top_n_providers": 100,
            "fund_count": top_100_companies_fund_count,
            "percent": top_100_companies_fund_count_percent,
        }
    )
    top_n_company_fund_count_list.append(
        {
            "top_n_providers": len(provider_fund_count),
            "fund_count": all_companies_fund_count_sum,
            "percent": 100,
        }
    )
    logger.info(f"Top 100 companies fund count sum: {top_100_companies_fund_count}")
    top_n_company_fund_count_df = pd.DataFrame(top_n_company_fund_count_list)

    # statistics provider_mapping_data for counting FundClassId count based on CompanyId and CompanyName
    logger.info(
        "statistics provider_mapping_data for counting SecId count based on CompanyId and CompanyName"
    )
    provider_share_class_id_df = provider_mapping_data[
        ["CompanyId", "CompanyName", "SecId"]
    ].drop_duplicates()
    provider_share_class_count = (
        provider_share_class_id_df.groupby(["CompanyId", "CompanyName"])
        .size()
        .reset_index(name="share_class_count")
    )
    # order by share_class_count in descending order
    provider_share_class_count = provider_share_class_count.sort_values(
        by="share_class_count", ascending=False
    )
    # statistics share_class_count in provider_share_class_count by describe and transform to DataFrame
    provider_share_class_count_stat_df = get_describe_stat(
        provider_share_class_count, "share_class_count", "provider_share_class_count"
    )
    describe_stat_df_list.append(provider_share_class_count_stat_df)

    # Get the fund_count sum of all companies
    all_companies_share_class_count_sum = provider_share_class_count[
        "share_class_count"
    ].sum()

    top_n_company_share_class_count_list = []
    # Get the fund_count sum of top 5 companies
    top_5_companies_share_class_count, top_5_companies_share_class_count_percent = (
        get_top_n_records_count(
            provider_share_class_count,
            "share_class_count",
            5,
            all_companies_share_class_count_sum,
        )
    )
    top_n_company_share_class_count_list.append(
        {
            "top_n_providers": 5,
            "share_class_count": top_5_companies_share_class_count,
            "percent": top_5_companies_share_class_count_percent,
        }
    )
    logger.info(
        f"Top 5 companies share class count sum: {top_5_companies_share_class_count}"
    )
    # Get the fund_count sum of top 10 companies
    top_10_companies_share_class_count, top_10_companies_share_class_count_percent = (
        get_top_n_records_count(
            provider_share_class_count,
            "share_class_count",
            10,
            all_companies_share_class_count_sum,
        )
    )
    top_n_company_share_class_count_list.append(
        {
            "top_n_providers": 10,
            "share_class_count": top_10_companies_share_class_count,
            "percent": top_10_companies_share_class_count_percent,
        }
    )
    logger.info(
        f"Top 10 companies share class count sum: {top_10_companies_share_class_count}"
    )

    # Get the fund_count sum of top 50 companies
    top_50_companies_share_class_count, top_50_companies_share_class_count_percent = (
        get_top_n_records_count(
            provider_share_class_count,
            "share_class_count",
            50,
            all_companies_share_class_count_sum,
        )
    )
    top_n_company_share_class_count_list.append(
        {
            "top_n_providers": 50,
            "share_class_count": top_50_companies_share_class_count,
            "percent": top_50_companies_share_class_count_percent,
        }
    )
    logger.info(
        f"Top 50 companies share class count sum: {top_50_companies_share_class_count}"
    )

    # Get the fund_count sum of top 100 companies
    top_100_companies_share_class_count, top_100_companies_share_class_count_percent = (
        get_top_n_records_count(
            provider_share_class_count,
            "share_class_count",
            100,
            all_companies_share_class_count_sum,
        )
    )
    top_n_company_share_class_count_list.append(
        {
            "top_n_providers": 100,
            "share_class_count": top_100_companies_share_class_count,
            "percent": top_100_companies_share_class_count_percent,
        }
    )
    logger.info(
        f"Top 100 companies share class count sum: {top_100_companies_share_class_count}"
    )
    top_n_company_share_class_count_list.append(
        {
            "top_n_providers": len(provider_share_class_count),
            "share_class_count": all_companies_share_class_count_sum,
            "percent": 100,
        }
    )
    top_n_company_share_class_count_df = pd.DataFrame(
        top_n_company_share_class_count_list
    )

    # statistics provider_mapping_data for counting SecId count based on FundId and FundLegalName
    logger.info(
        "statistics provider_mapping_data for counting SecId count based on FundId and FundLegalName"
    )
    fund_share_class_id_df = provider_mapping_data[
        ["FundId", "FundLegalName", "SecId"]
    ].drop_duplicates()
    fund_share_class_count = (
        fund_share_class_id_df.groupby(["FundId", "FundLegalName"])
        .size()
        .reset_index(name="share_class_count")
    )
    # order by share_class_count in fund_share_class_count
    fund_share_class_count = fund_share_class_count.sort_values(
        by="share_class_count", ascending=False
    )
    # statistics share_class_count in fund_share_class_count by describe and transform to DataFrame
    fund_share_class_count_stat_df = get_describe_stat(
        fund_share_class_count, "share_class_count", "fund_share_class_count"
    )
    describe_stat_df_list.append(fund_share_class_count_stat_df)
    describe_stat_df = pd.concat(describe_stat_df_list)
    describe_stat_df.reset_index(drop=True, inplace=True)

    stat_file = os.path.join(output_folder, "provider_mapping_data_statistics.xlsx")
    # save statistics data to excel
    with pd.ExcelWriter(stat_file) as writer:
        top_n_company_fund_count_df.to_excel(
            writer, sheet_name="top_n_provider_fund_count", index=False
        )
        top_n_company_share_class_count_df.to_excel(
            writer, sheet_name="top_n_provider_share_count", index=False
        )
        provider_fund_count.to_excel(
            writer, sheet_name="provider_fund_count", index=False
        )
        provider_share_class_count.to_excel(
            writer, sheet_name="provider_share_count", index=False
        )
        fund_share_class_count.to_excel(
            writer, sheet_name="fund_share_count", index=False
        )
        describe_stat_df.to_excel(
            writer, sheet_name="all_describe_statistics", index=False
        )


def statistics_document_fund_share_count(provider_mapping_data_file: str):
    if (
        provider_mapping_data_file is None
        or len(provider_mapping_data_file) == 0
        or not os.path.exists(provider_mapping_data_file)
    ):
        logger.error(f"Invalid file_path: {provider_mapping_data_file}")
        return

    describe_stat_df_list = []
    # statistics document mapping information
    doc_mapping_data = pd.read_excel(provider_mapping_data_file, sheet_name="all_data")
    # set noTor column value to 0 if column tor value is not nan, set 1 otherwise
    doc_mapping_data["noTor"] = doc_mapping_data["tor"].apply(
        lambda x: 0 if pd.notna(x) else 1
    )
    # set share_noTer column value to 0 if column share_ter value is not nan, set 1 otherwise
    doc_mapping_data["share_noTer"] = doc_mapping_data["share_ter"].apply(
        lambda x: 0 if pd.notna(x) else 1
    )
    # set share_noOgc column value to 0 if column share_ter value is not nan, set 1 otherwise
    doc_mapping_data["share_noOgc"] = doc_mapping_data["share_ogc"].apply(
        lambda x: 0 if pd.notna(x) else 1
    )
    # set share_noPerfFee column value to 0 if column share_ter value is not nan, set 1 otherwise
    doc_mapping_data["share_noPerfFee"] = doc_mapping_data["share_perfFee"].apply(
        lambda x: 0 if pd.notna(x) else 1
    )

    # statistics doc_mapping_data for counting FundId count based on DocumentId
    logger.info(
        "statistics doc_mapping_data for counting FundId count based on DocumentId"
    )
    doc_fund_id_df = doc_mapping_data[["DocumentId", "EffectiveDate", "CompanyId", "CompanyName", "FundId"]].drop_duplicates()
    doc_fund_count = (
        doc_fund_id_df.groupby(["DocumentId", "EffectiveDate", "CompanyId", "CompanyName"]).size().reset_index(name="fund_count")
    )
    # order by fund_count in descending order
    doc_fund_count = doc_fund_count.sort_values(by="fund_count", ascending=True)
    
    # set with_ar_data to True if noTor == 0 or share_noOgc == 0 or share_noPerfFee == 0
    doc_fund_count["with_ar_data"] = False
    for index, row in doc_fund_count.iterrows():
        document_id = row["DocumentId"]
        ar_data = doc_mapping_data[
            (doc_mapping_data["DocumentId"] == document_id)
            & (
                (
                    (doc_mapping_data["noTor"] == 0)
                    | (doc_mapping_data["share_noTer"] == 0)
                    | (doc_mapping_data["share_noOgc"] == 0)
                    | (doc_mapping_data["share_noPerfFee"] == 0)
                )
            )
        ]
        if len(ar_data) > 0:
            doc_fund_count.loc[index, "with_ar_data"] = True

    
    # statistics fund_count in doc_fund_count by describe and transform to DataFrame
    doc_fund_count_stat_df = get_describe_stat(
        doc_fund_count, "fund_count", "doc_fund_count"
    )
    describe_stat_df_list.append(doc_fund_count_stat_df)

    # statistics doc_mapping_data for counting FundClassId count based on DocumentId
    logger.info(
        "statistics doc_mapping_data for counting FundClassId count based on DocumentId"
    )
    doc_share_class_id_df = doc_mapping_data[
        ["DocumentId", "EffectiveDate", "CompanyId", "CompanyName", "FundClassId"]
    ].drop_duplicates()
    doc_share_class_count = (
        doc_share_class_id_df.groupby(["DocumentId", "EffectiveDate", "CompanyId", "CompanyName"])
        .size()
        .reset_index(name="share_class_count")
    )
    # order by share_class_count in descending order
    doc_share_class_count = doc_share_class_count.sort_values(
        by="share_class_count", ascending=True
    )
    # set with_ar_data to True if noTor == 0 or share_noOgc == 0 or share_noPerfFee == 0
    doc_share_class_count["with_ar_data"] = False
    for index, row in doc_share_class_count.iterrows():
        document_id = row["DocumentId"]
        ar_data = doc_mapping_data[
            (doc_mapping_data["DocumentId"] == document_id)
            & (
                (
                    (doc_mapping_data["noTor"] == 0)
                    | (doc_mapping_data["share_noTer"] == 0)
                    | (doc_mapping_data["share_noOgc"] == 0)
                    | (doc_mapping_data["share_noPerfFee"] == 0)
                )
            )
        ]
        if len(ar_data) > 0:
            doc_share_class_count.loc[index, "with_ar_data"] = True
            
    # statistics share_class_count in doc_share_class_count by describe and transform to DataFrame
    doc_share_class_count_stat_df = get_describe_stat(
        doc_share_class_count, "share_class_count", "doc_share_class_count"
    )
    describe_stat_df_list.append(doc_share_class_count_stat_df)
    
    describe_stat_df = pd.concat(describe_stat_df_list)
    describe_stat_df.reset_index(drop=True, inplace=True)
    
    with pd.ExcelWriter(provider_mapping_data_file) as writer:
        doc_mapping_data.to_excel(writer, sheet_name="all_data", index=False)
        doc_fund_count.to_excel(writer, sheet_name="doc_fund_count", index=False)
        doc_share_class_count.to_excel(writer, sheet_name="doc_share_class_count", index=False)
        describe_stat_df.to_excel(writer, sheet_name="all_describe_statistics", index=False)
    

def get_top_n_records_count(
    df: pd.DataFrame, column_name: str, n: int, total_count: int
):
    top_n_records = df.head(n)
    top_n_records_count = top_n_records[column_name].sum()
    top_n_records_count_percent = round((top_n_records_count / total_count) * 100, 2)
    return top_n_records_count, top_n_records_count_percent


def get_describe_stat(df: pd.DataFrame, column_name: str, stat_type_name: str):
    stat_df = df[column_name].describe().reset_index().T
    stat_df.columns = ["count", "mean", "std", "min", "25%", "50%", "75%", "max"]
    stat_df.reset_index(inplace=True)
    stat_df.rename(columns={"index": "Stat"}, inplace=True)
    # remove the first row
    stat_df = stat_df[1:]
    if stat_type_name is not None:
        stat_df["Stat_Type"] = stat_type_name
        stat_df = stat_df[
            [
                "Stat_Type",
                "count",
                "mean",
                "std",
                "min",
                "25%",
                "50%",
                "75%",
                "max",
            ]
        ]
    return stat_df


def pickup_document_from_top_100_providers():
    """
    Pickup 100 documents from top 100 providers.
    The documents are with less 10 share classes.
    The purpose is to analyze the document structure and content from small documents.
    """
    provider_mapping_data_file = (
        r"/data/emea_ar/basic_information/English/provider_mapping_data_statistics.xlsx"
    )
    top_100_provider_document_file = (
        r"/data/emea_ar/basic_information/English/lux_english_ar_from_top_100_provider_since_2020.xlsx"
    )
    provider_share_count = pd.read_excel(
        provider_mapping_data_file, sheet_name="provider_share_count"
    )
    # add a new column with name share_count_rank to provider_share_count
    provider_share_count["share_count_rank"] = provider_share_count[
        "share_class_count"
    ].rank(method="min", ascending=False)
    
    top_100_provider_document_all_data = pd.read_excel(
        top_100_provider_document_file, sheet_name="all_data"
    )
    
    top_100_provider_document_fund_count = pd.read_excel(
        top_100_provider_document_file, sheet_name="doc_fund_count"
    )
    top_100_provider_document_fund_count.reset_index(drop=True, inplace=True)
    
    top_100_provider_document_share_count = pd.read_excel(
        top_100_provider_document_file, sheet_name="doc_share_class_count"
    )
    top_100_provider_document_share_count = \
        top_100_provider_document_share_count[top_100_provider_document_share_count["with_ar_data"] == True]
    top_100_provider_document_share_count.reset_index(drop=True, inplace=True)
    
    top_100_provider_document_share_count = pd.merge(
        top_100_provider_document_share_count,
        top_100_provider_document_fund_count,
        on=["DocumentId"],
        how="left",
    )
    top_100_provider_document_share_count = top_100_provider_document_share_count[
        ["DocumentId", "CompanyId_x", "CompanyName_x", "fund_count", "share_class_count"]
    ]
    top_100_provider_document_share_count.rename(
        columns={"CompanyId_x": "CompanyId"}, inplace=True
    )
    
    # add a new column with name share_count_rank to top_100_provider_document_share_count by merge with provider_share_count
    top_100_provider_document_share_count = pd.merge(
        top_100_provider_document_share_count,
        provider_share_count,
        on=["CompanyId"],
        how="left",
    )
    # Keep columns: DocumentId, CompanyId, CompanyName, share_class_count_x, share_count_rank
    top_100_provider_document_share_count = top_100_provider_document_share_count[
        ["DocumentId", "CompanyId", "CompanyName", "fund_count", "share_class_count_x", "share_count_rank"]
    ]
    # rename column share_class_count_x to share_class_count
    top_100_provider_document_share_count.rename(
        columns={"share_class_count_x": "share_class_count",
                 "share_count_rank": "provider_share_count_rank"}, inplace=True
    )
    top_100_provider_document_share_count = top_100_provider_document_share_count.sort_values(
        by=["provider_share_count_rank", "share_class_count"], ascending=True
    )
    
    # According to share_count_rank, from 1 to 10, 
    # random pickup one documents with 1 to 10 share classes for each rank
    data_filter = top_100_provider_document_share_count[
        (top_100_provider_document_share_count["share_class_count"] <= 10)
        & (top_100_provider_document_share_count["share_class_count"] >= 1)
    ]
    data_filter = data_filter.sort_values(
        by=["provider_share_count_rank", "share_class_count"], ascending=[True, True]
    )
    unique_rank_list = top_100_provider_document_share_count["provider_share_count_rank"].unique().tolist()
    random_pickup_document_data_list = []
    for rank in unique_rank_list:
        data_filter_rank = data_filter[data_filter["provider_share_count_rank"] == rank]
        if len(data_filter_rank) == 0:
            # get the first document with rank from top_100_provider_document_share_count
            data_filter_rank = top_100_provider_document_share_count[
                top_100_provider_document_share_count["provider_share_count_rank"] == rank
            ].head(1)
        data_filter_rank = data_filter_rank.sample(n=1, random_state=88)
        random_pickup_document_data_list.append(data_filter_rank)
    random_pickup_document_data = pd.concat(random_pickup_document_data_list)
    # sort by share_count_rank in ascending order
    random_pickup_document_data = random_pickup_document_data.sort_values(
        by="provider_share_count_rank", ascending=True
    )
    random_pickup_document_data.reset_index(drop=True, inplace=True)
    
    random_pickup_document_mini_data = random_pickup_document_data[
        ["DocumentId", "provider_share_count_rank"]
    ]
    # get all data from top_100_provider_document_all_data by merge with random_pickup_document_mini_data
    random_pickup_document_all_data = pd.merge(
        random_pickup_document_mini_data,
        top_100_provider_document_all_data,
        on=["DocumentId"],
        how="left",
    )
    # sort random_pickup_document_all_data by provider_share_count_rank, FundLegalName, FundClassLegalName in ascending order
    random_pickup_document_all_data = random_pickup_document_all_data.sort_values(
        by=["provider_share_count_rank", "FundLegalName", "FundClassLegalName"], ascending=True
    )
    
    random_small_document_data_file = (
        r"/data/emea_ar/basic_information/English/lux_english_ar_top_100_provider_random_small_document.xlsx"
    )
    with pd.ExcelWriter(random_small_document_data_file) as writer:
        top_100_provider_document_share_count.to_excel(
            writer, sheet_name="all_doc_with_ar_data", index=False
        )
        random_pickup_document_data.to_excel(
            writer, sheet_name="random_small_document", index=False
        )
        
        random_pickup_document_all_data.to_excel(
            writer, sheet_name="random_small_document_all_data", index=False
        )


def compare_records_count_by_document_id():
    data_from_document = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_73_documents.xlsx"
    sheet_name = "mapping_data"
    data_from_document_df = pd.read_excel(data_from_document, sheet_name=sheet_name)
    data_from_document_df.rename(
        columns={"doc_id": "DocumentId"}, inplace=True
    )
    # get the count of records by DocumentId
    document_records_count = data_from_document_df.groupby("DocumentId").size().reset_index(name="records_count")
    
    data_from_database = r"/data/emea_ar/basic_information/English/lux_english_ar_top_100_provider_random_small_document_from_DocumentAcquisition.xlsx"
    sheet_name = "random_small_document_all_data"
    data_from_database_df = pd.read_excel(data_from_database, sheet_name=sheet_name)
    database_records_count = data_from_database_df.groupby("DocumentId").size().reset_index(name="records_count")
    
    # merge document_records_count with database_records_count
    records_count_compare = pd.merge(
        document_records_count,
        database_records_count,
        on=["DocumentId"],
        how="left",
    )
    records_count_compare["records_count_diff"] = records_count_compare["records_count_x"] - records_count_compare["records_count_y"]
    records_count_compare = records_count_compare.sort_values(by="records_count_diff", ascending=False)
    # rename records_count_x to records_count_document, records_count_y to records_count_database
    records_count_compare.rename(
        columns={"records_count_x": "records_count_document",
                 "records_count_y": "records_count_database"}, inplace=True
    )
    records_count_compare.reset_index(drop=True, inplace=True)
    
    records_count_compare_file = (
        r"/data/emea_ar/basic_information/English/records_count_compare_between_document_database_from_DocumentAcquisition.xlsx"
    )
    with pd.ExcelWriter(records_count_compare_file) as writer:
        records_count_compare.to_excel(
            writer, sheet_name="records_count_compare", index=False
        )


def get_document_extracted_share_diff_by_db():
    db_data_file = r"/data/emea_ar/basic_information/English/lux_english_ar_top_100_provider_random_small_document_from_DocumentAcquisition.xlsx"
    extract_data_file = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_73_documents.xlsx"
    
    doc_mapping_folder = r"/data/emea_ar/output/mapping/document/"
    db_data = pd.read_excel(db_data_file, sheet_name="Sheet1")
    extract_data = pd.read_excel(extract_data_file, sheet_name="mapping_data")
    # only get data which investment_type is 1
    # extract_data = extract_data[extract_data["investment_type"] == 1]
    extract_data.reset_index(drop=True, inplace=True)
    unique_doc_id = extract_data["doc_id"].unique().tolist()
    
    status_info = {
        1: "WIP",
        5: "Junked",
        3: "AutoSignoff",
        2: "Signoffed",
        10: "Complete",
        20: "AutoDetect",
        21: "Checkduplicate",
        22: "Mapping",
        33: "Not Matched",
        99: "Unknown",
    }
    document_extract_db_compare = []
    for doc_id in unique_doc_id:
        doc_mapping_file = os.path.join(doc_mapping_folder, f"{doc_id}.xlsx")
        if not os.path.exists(doc_mapping_file):
            logger.error(f"Invalid mapping_file: {doc_mapping_file}")
            doc_mapping_share_class_id_df = pd.DataFrame()
        else:
            doc_mapping_data = pd.read_excel(doc_mapping_file)
            doc_mapping_share_class_id_df = doc_mapping_data[["SecId"]].drop_duplicates()
        
        ar_db_data_doc = db_data[db_data["DocumentId"] == doc_id]
        try:
            masterProcess_status = ar_db_data_doc["MasterProcess_Status"].values[0]
        except Exception as e:
            logger.error(f"Error: {e}")
            masterProcess_status = 99
        masterProcess_status = int(masterProcess_status)
        masterProcess_status_defination = status_info.get(masterProcess_status, "Unknown")
        # get data from ar_db_data_doc which noTor == 0 or share_noOgc == 0 or share_noPerfFee == 0
        ar_db_data_doc = ar_db_data_doc[
            (ar_db_data_doc["noTor"] == 0)
            | (ar_db_data_doc["share_noTer"] == 0)
            | (ar_db_data_doc["share_noOgc"] == 0)
            | (ar_db_data_doc["share_noPerfFee"] == 0)
        ]
        
        extract_data_doc = extract_data[extract_data["doc_id"] == doc_id]
        # unique raw_name in extract_data_doc
        unique_raw_name = extract_data_doc["raw_name"].unique().tolist()
        
        doc_mapping_share_class_count = len(doc_mapping_share_class_id_df)
        extract_share_class_count = len(unique_raw_name)
        extract_vs_doc_share_count_diff = extract_share_class_count - doc_mapping_share_class_count
        db_share_class_count = len(ar_db_data_doc)
        extract_vs_ar_db_share_count_diff = extract_share_class_count - db_share_class_count
        document_extract_db_compare.append({
            "DocumentId": doc_id,
            "status": masterProcess_status,
            "status_defination": masterProcess_status_defination,
            "extract_share_count": extract_share_class_count,
            "doc_share_count": doc_mapping_share_class_count,
            "extract_vs_doc_share_count_diff": extract_vs_doc_share_count_diff,
            "ar_db_share_count": db_share_class_count,
            "extract_vs_ar_db_share_count_diff": extract_vs_ar_db_share_count_diff,
        })
    document_extract_db_compare_df = pd.DataFrame(document_extract_db_compare)
    # output to excel
    document_extract_db_compare_file = (
        r"/data/emea_ar/basic_information/English/document_extract_db_compare.xlsx"
    )
    with pd.ExcelWriter(document_extract_db_compare_file) as writer:
        document_extract_db_compare_df.to_excel(
            writer, sheet_name="document_extract_db_compare", index=False
        )


def concat_mapping(mapping_folder: str,
                   output_file: str):
    excel_files = glob(os.path.join(mapping_folder, "*.xlsx"))
    logger.info(f"Total {len(excel_files)} excel files found in {mapping_folder}")
    all_data_list = []
    for excel_file in excel_files:
        doc_mapping_data = pd.read_excel(excel_file)
        all_data_list.append(doc_mapping_data)
    all_data = pd.concat(all_data_list)
    all_data.reset_index(drop=True, inplace=True)
    with open(output_file, "wb") as f:
        all_data.to_excel(f, index=False)
        
        
def calc_typical_doc_metrics_v2():
    """
    Statistics metrics for typical document.
    1. Fund level datapoint: TOR
    2. Share level datapoint: OGC, TER, Performance fees
    3. Only statistics the record which with document investment mapping
    """
    from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
    result_file = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_20_new_emea_documents_sample_Accuracy.xlsx"
    sheet_name = "record_level_Results"
    data = pd.read_excel(result_file, sheet_name=sheet_name)
    data.fillna("", inplace=True)
    # filter data which valid is 1
    data = data[data["valid"] == 1]
    
    fund_raw_data_gt = []
    fund_raw_data_pred = []
    
    fund_mapping_data_gt = []
    fund_mapping_data_pred = []
    
    share_raw_data_gt = []
    share_raw_data_pred = []
    
    share_mapping_data_gt = []
    share_mapping_data_pred = []
    
    for idx, row in data.iterrows():
        raw_data_gt_count = row["Raw data in Doc"]
        raw_data_infer_count = row["Raw data in Inference"]
        if len(str(raw_data_gt_count)) > 0:
            raw_data_gt_count = int(raw_data_gt_count)
            raw_data_infer_count = int(raw_data_infer_count)
            
            raw_gt_list = [1 for i in range(raw_data_gt_count)]
            raw_pred_list = []
            if raw_data_infer_count > 0:
                raw_pred_list = [1 for i in range(raw_data_infer_count)]
            if len(raw_pred_list) < len(raw_gt_list):
                raw_pred_list.extend([0 for i in range(len(raw_gt_list) - len(raw_pred_list))])
        
        mapping_data_gt_count = row["data in DB"]
        mapping_data_infer_count = row["data in Inferencce"]
        if len(str(mapping_data_gt_count)) > 0:
            mapping_data_gt_count = int(mapping_data_gt_count)
            mapping_data_infer_count = int(mapping_data_infer_count)
            
            mapping_gt_list = [1 for i in range(mapping_data_gt_count)]
            mapping_pred_list = []
            if mapping_data_infer_count > 0:
                mapping_pred_list = [1 for i in range(mapping_data_infer_count)]
            if len(mapping_pred_list) < len(mapping_gt_list):
                mapping_pred_list.extend([0 for i in range(len(mapping_gt_list) - len(mapping_pred_list))])
        
        
        data_level = row["data_level"]
        if data_level == "fund":
            fund_raw_data_gt.extend(raw_gt_list)
            fund_raw_data_pred.extend(raw_pred_list)
            
            fund_mapping_data_gt.extend(mapping_gt_list)
            fund_mapping_data_pred.extend(mapping_pred_list)
        else:
            share_raw_data_gt.extend(raw_gt_list)
            share_raw_data_pred.extend(raw_pred_list)
            
            share_mapping_data_gt.extend(mapping_gt_list)
            share_mapping_data_pred.extend(mapping_pred_list)
    
    share_raw_data_gt.extend([0, 0, 0, 0, 0, 0])
    share_raw_data_pred.extend([1, 1, 1, 1, 1, 1])
    
    share_mapping_data_gt.extend([0, 0, 0, 0, 0, 0])
    share_mapping_data_pred.extend([1, 1, 1, 1, 1, 1])
    
    fund_raw_data_accuracy = accuracy_score(fund_raw_data_gt, fund_raw_data_pred)
    fund_raw_data_precision = precision_score(fund_raw_data_gt, fund_raw_data_pred)
    fund_raw_data_recall = recall_score(fund_raw_data_gt, fund_raw_data_pred)
    fund_raw_data_f1 = f1_score(fund_raw_data_gt, fund_raw_data_pred)
    
    fund_mapping_data_accuracy = accuracy_score(fund_mapping_data_gt, fund_mapping_data_pred)
    fund_mapping_data_precision = precision_score(fund_mapping_data_gt, fund_mapping_data_pred)
    fund_mapping_data_recall = recall_score(fund_mapping_data_gt, fund_mapping_data_pred)
    fund_mapping_data_f1 = f1_score(fund_mapping_data_gt, fund_mapping_data_pred)
    
    share_raw_data_accuracy = accuracy_score(share_raw_data_gt, share_raw_data_pred)
    share_raw_data_precision = precision_score(share_raw_data_gt, share_raw_data_pred)
    share_raw_data_recall = recall_score(share_raw_data_gt, share_raw_data_pred)
    share_raw_data_f1 = f1_score(share_raw_data_gt, share_raw_data_pred)
    
    share_mapping_data_accuracy = accuracy_score(share_mapping_data_gt, share_mapping_data_pred)
    share_mapping_data_precision = precision_score(share_mapping_data_gt, share_mapping_data_pred)
    share_mapping_data_recall = recall_score(share_mapping_data_gt, share_mapping_data_pred)
    share_mapping_data_f1 = f1_score(share_mapping_data_gt, share_mapping_data_pred)
    
    final_data = []
    
    fund_raw_data_metrics = {"title": "Fund_Datapoint_Raw_Data",
                             "accuracy": fund_raw_data_accuracy,
                             "precision": fund_raw_data_precision,
                             "recall": fund_raw_data_recall,
                             "f1": fund_raw_data_f1,
                             "support": len(fund_raw_data_gt)}
    final_data.append(fund_raw_data_metrics)
    logger.info(f"fund_raw_data_accuracy: {fund_raw_data_accuracy}")
    logger.info(f"fund_raw_data_precision: {fund_raw_data_precision}")
    logger.info(f"fund_raw_data_recall: {fund_raw_data_recall}")
    logger.info(f"fund_raw_data_f1: {fund_raw_data_f1}")
    logger.info(f"fund_raw_data_support: {len(fund_raw_data_gt)}")
    
    fund_mapping_data_metrics = {"title": "Fund_Datapoint_Mapping_Data",
                                 "accuracy": fund_mapping_data_accuracy,
                                 "precision": fund_mapping_data_precision,
                                 "recall": fund_mapping_data_recall,
                                 "f1": fund_mapping_data_f1,
                                 "support": len(fund_mapping_data_gt)}
    final_data.append(fund_mapping_data_metrics)
    logger.info(f"fund_mapping_data_accuracy: {fund_mapping_data_accuracy}")
    logger.info(f"fund_mapping_data_precision: {fund_mapping_data_precision}")
    logger.info(f"fund_mapping_data_recall: {fund_mapping_data_recall}")
    logger.info(f"fund_mapping_data_f1: {fund_mapping_data_f1}")
    logger.info(f"fund_mapping_data_support: {len(fund_mapping_data_gt)}")
    
    share_raw_data_metrics = {"title": "Share_Datapoint_Raw_Data",
                                "accuracy": share_raw_data_accuracy,
                                "precision": share_raw_data_precision,
                                "recall": share_raw_data_recall,
                                "f1": share_raw_data_f1,
                                "support": len(share_raw_data_gt)}
    final_data.append(share_raw_data_metrics)
    logger.info(f"share_raw_data_accuracy: {share_raw_data_accuracy}")
    logger.info(f"share_raw_data_precision: {share_raw_data_precision}")
    logger.info(f"share_raw_data_recall: {share_raw_data_recall}")
    logger.info(f"share_raw_data_f1: {share_raw_data_f1}")
    logger.info(f"share_raw_data_support: {len(share_raw_data_gt)}")
    
    share_mapping_data_metrics = {"title": "Share_Datapoint_Mapping_Data",
                                    "accuracy": share_mapping_data_accuracy,
                                    "precision": share_mapping_data_precision,
                                    "recall": share_mapping_data_recall,
                                    "f1": share_mapping_data_f1,
                                    "support": len(share_mapping_data_gt)}
    final_data.append(share_mapping_data_metrics)
    logger.info(f"share_mapping_data_accuracy: {share_mapping_data_accuracy}")
    logger.info(f"share_mapping_data_precision: {share_mapping_data_precision}")
    logger.info(f"share_mapping_data_recall: {share_mapping_data_recall}")
    logger.info(f"share_mapping_data_f1: {share_mapping_data_f1}")
    logger.info(f"share_mapping_data_support: {len(share_mapping_data_gt)}")
    
    final_data_df = pd.DataFrame(final_data)
    # set column order as title, accuracy, f1, precision, recall
    final_data_df = final_data_df[["title", "accuracy", "f1", "precision", "recall", "support"]]
    # output to excel
    final_data_file = (
        r"/data/emea_ar/output/metrics/mapping_data_info_20_new_emea_documents_sample_Accuracy_metrics_v2.xlsx"
    )
    with pd.ExcelWriter(final_data_file) as writer:
        final_data_df.to_excel(
            writer, sheet_name="metrics", index=False
        )
    
        
def calc_typical_doc_metrics_v1():
    from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
    result_file = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_20_new_emea_documents_sample_Accuracy.xlsx"
    sheet_name = "record_level_Results"
    data = pd.read_excel(result_file, sheet_name=sheet_name)
    data.fillna("", inplace=True)
    fund_raw_data_list = data["Raw Mapping"].tolist()
    fund_raw_data_gt = []
    fund_raw_data_pred = []
    for fund_raw_data in fund_raw_data_list:
        if fund_raw_data == "Correct Raw mapping":
            fund_raw_data_gt.append(1)
            fund_raw_data_pred.append(1)
        elif fund_raw_data == "Incorrect Raw mapping":
            fund_raw_data_gt.append(1)
            fund_raw_data_pred.append(0)
        else:
            pass
    fund_raw_data_accuracy = accuracy_score(fund_raw_data_gt, fund_raw_data_pred)
    fund_raw_data_precision = precision_score(fund_raw_data_gt, fund_raw_data_pred)
    fund_raw_data_recall = recall_score(fund_raw_data_gt, fund_raw_data_pred)
    fund_raw_data_f1 = f1_score(fund_raw_data_gt, fund_raw_data_pred)
    
    fund_mapping_data_list = data["Share Mapping"].tolist()
    fund_mapping_data_gt = []
    fund_mapping_data_pred = []
    for fund_mapping_data in fund_mapping_data_list:
        if fund_mapping_data == "Correct share mapping":
            fund_mapping_data_gt.append(1)
            fund_mapping_data_pred.append(1)
        elif fund_mapping_data == "Incorrect share mapping":
            fund_mapping_data_gt.append(1)
            fund_mapping_data_pred.append(0)
        else:
            pass
    fund_mapping_data_accuracy = accuracy_score(fund_mapping_data_gt, fund_mapping_data_pred)
    fund_mapping_data_precision = precision_score(fund_mapping_data_gt, fund_mapping_data_pred)
    fund_mapping_data_recall = recall_score(fund_mapping_data_gt, fund_mapping_data_pred)
    fund_mapping_data_f1 = f1_score(fund_mapping_data_gt, fund_mapping_data_pred)
    
    share_raw_data_gt = []
    share_raw_data_pred = []
    
    share_mapping_data_gt = []
    share_mapping_data_pred = []
    for idx, row in data.iterrows():
        share_raw_data_infer_count = row["Raw Share in Inference"]
        share_raw_data_gt_count = row["Raw Share in Doc"]
        if share_raw_data_gt_count is not None and \
            len(str(share_raw_data_gt_count)) > 0:
                share_raw_data_gt_count = int(share_raw_data_gt_count)
                share_raw_data_infer_count = int(share_raw_data_infer_count)
                
                gt_list = [1 for i in range(share_raw_data_gt_count)]
                if share_raw_data_infer_count > 0:
                    pred_list = [1 for i in range(share_raw_data_infer_count)]
                else:
                    pred_list = [1, 1]
                    gt_list = [0, 0]
                if len(pred_list) < len(gt_list):
                    pred_list.extend([0 for i in range(len(gt_list) - len(pred_list))])
                share_raw_data_gt.extend(gt_list)
                share_raw_data_pred.extend(pred_list)
        
        share_mapping_data_infer_count = row["share in Inferencce"]
        share_mapping_data_gt_count = row["share in DB"]
        if share_mapping_data_gt_count is not None and \
            len(str(share_mapping_data_gt_count)) > 0:
                share_mapping_data_gt_count = int(share_mapping_data_gt_count)
                share_mapping_data_infer_count = int(share_mapping_data_infer_count)
                
                gt_list = [1 for i in range(share_mapping_data_gt_count)]
                if share_mapping_data_infer_count > 0:
                    pred_list = [1 for i in range(share_mapping_data_infer_count)]
                else:
                    pred_list = [1, 1]
                    gt_list = [0, 0]
                if len(pred_list) < len(gt_list):
                    pred_list.extend([0 for i in range(len(gt_list) - len(pred_list))])
                share_mapping_data_gt.extend(gt_list)
                share_mapping_data_pred.extend(pred_list)
    share_raw_data_accuracy = accuracy_score(share_raw_data_gt, share_raw_data_pred)
    share_raw_data_precision = precision_score(share_raw_data_gt, share_raw_data_pred)
    share_raw_data_recall = recall_score(share_raw_data_gt, share_raw_data_pred)
    share_raw_data_f1 = f1_score(share_raw_data_gt, share_raw_data_pred)
    
    share_mapping_data_accuracy = accuracy_score(share_mapping_data_gt, share_mapping_data_pred)
    share_mapping_data_precision = precision_score(share_mapping_data_gt, share_mapping_data_pred)
    share_mapping_data_recall = recall_score(share_mapping_data_gt, share_mapping_data_pred)
    share_mapping_data_f1 = f1_score(share_mapping_data_gt, share_mapping_data_pred)
    
    final_data = []
    
    fund_raw_data_metrics = {"title": "Fund_Raw_Data",
                             "accuracy": fund_raw_data_accuracy,
                             "precision": fund_raw_data_precision,
                             "recall": fund_raw_data_recall,
                             "f1": fund_raw_data_f1,
                             "support": len(fund_raw_data_gt)}
    final_data.append(fund_raw_data_metrics)
    logger.info(f"fund_raw_data_accuracy: {fund_raw_data_accuracy}")
    logger.info(f"fund_raw_data_precision: {fund_raw_data_precision}")
    logger.info(f"fund_raw_data_recall: {fund_raw_data_recall}")
    logger.info(f"fund_raw_data_f1: {fund_raw_data_f1}")
    
    fund_mapping_data_metrics = {"title": "Fund_Mapping_Data",
                                 "accuracy": fund_mapping_data_accuracy,
                                 "precision": fund_mapping_data_precision,
                                 "recall": fund_mapping_data_recall,
                                 "f1": fund_mapping_data_f1,
                                 "support": len(fund_mapping_data_gt)}
    final_data.append(fund_mapping_data_metrics)
    logger.info(f"fund_mapping_data_accuracy: {fund_mapping_data_accuracy}")
    logger.info(f"fund_mapping_data_precision: {fund_mapping_data_precision}")
    logger.info(f"fund_mapping_data_recall: {fund_mapping_data_recall}")
    logger.info(f"fund_mapping_data_f1: {fund_mapping_data_f1}")
    
    share_raw_data_metrics = {"title": "Share_Raw_Data",
                                "accuracy": share_raw_data_accuracy,
                                "precision": share_raw_data_precision,
                                "recall": share_raw_data_recall,
                                "f1": share_raw_data_f1,
                                "support": len(share_raw_data_gt)}
    final_data.append(share_raw_data_metrics)
    logger.info(f"share_raw_data_accuracy: {share_raw_data_accuracy}")
    logger.info(f"share_raw_data_precision: {share_raw_data_precision}")
    logger.info(f"share_raw_data_recall: {share_raw_data_recall}")
    logger.info(f"share_raw_data_f1: {share_raw_data_f1}")
    
    share_mapping_data_metrics = {"title": "Share_Mapping_Data",
                                    "accuracy": share_mapping_data_accuracy,
                                    "precision": share_mapping_data_precision,
                                    "recall": share_mapping_data_recall,
                                    "f1": share_mapping_data_f1,
                                    "support": len(share_mapping_data_gt)}
    final_data.append(share_mapping_data_metrics)
    logger.info(f"share_mapping_data_accuracy: {share_mapping_data_accuracy}")
    logger.info(f"share_mapping_data_precision: {share_mapping_data_precision}")
    logger.info(f"share_mapping_data_recall: {share_mapping_data_recall}")
    logger.info(f"share_mapping_data_f1: {share_mapping_data_f1}")
    
    final_data_df = pd.DataFrame(final_data)
    # set column order as title, accuracy, f1, precision, recall
    final_data_df = final_data_df[["title", "accuracy", "f1", "precision", "recall", "support"]]
    # output to excel
    final_data_file = (
        r"/data/emea_ar/output/metrics/mapping_data_info_20_new_emea_documents_sample_Accuracy_metrics.xlsx"
    )
    with pd.ExcelWriter(final_data_file) as writer:
        final_data_df.to_excel(
            writer, sheet_name="metrics", index=False
        )
        

def merge_aus_document_prospectus_data():
    """
    Merge AUS document and prospectus data.
    """
    aus_document_file = r"/data/aus_prospectus/basic_information/document_mapping.xlsx"
    aus_prospectus_file = r"/data/aus_prospectus/basic_information/aus_prospectus_data.xlsx"
    aus_document_data = pd.read_excel(aus_document_file)
    aus_prospectus_data = pd.read_excel(aus_prospectus_file)
    
    aus_document_data["DocumentId"] = aus_document_data["DocumentId"].astype(str)
    
    aus_document_prospectus_data = pd.merge(
        aus_document_data,
        aus_prospectus_data,
        on=["FundClassId", "EffectiveDate"],
        how="left",
    )
    aus_document_prospectus_file = r"/data/aus_prospectus/aus_document_prospectus.xlsx"
    with pd.ExcelWriter(aus_document_prospectus_file) as writer:
        aus_document_prospectus_data.to_excel(
            writer, sheet_name="aus_document_prospectus", index=False
        )
        


if __name__ == "__main__":
    # merge_aus_document_prospectus_data()
    folder = r"/data/emea_ar/basic_information/English/sample_doc/emea_11_06_case/"
    file_name = "doc_ar_data_for_emea_11_06.xlsx"
    # get_document_with_all_4_data_points(folder, file_name, None)
    # calc_typical_doc_metrics_v1()
    # calc_typical_doc_metrics_v2()
    
    doc_provider_file_path = (
        r"/data/emea_ar/basic_information/English/latest_provider_ar_document.xlsx"
    )
    doc_ar_data_file_path = r"/data/emea_ar/basic_information/English/latest_provider_ar_document_mapping.xlsx"
    provider_mapping_data_file = (
        r"/data/emea_ar/basic_information/English/provider_mapping_data.xlsx"
    )
    doc_mapping_from_top_100_provider_file = (
        r"/data/emea_ar/basic_information/English/lux_english_ar_from_top_100_provider_since_2020.xlsx"
    )
    basic_info_folder = r"/data/emea_ar/basic_information/English/"
    pdf_folder = r"/data/emea_ar/pdf/"
    output_folder = r"/data/emea_ar/output/"
    # get_unique_docids_from_doc_provider_data(doc_provider_file_path)
    # download_pdf(doc_provider_file_path, 'doc_provider_count', pdf_folder)
    # pdf_folder = r"/data/emea_ar/small_pdf/"
    output_folder = r"/data/emea_ar/small_pdf_txt/"
    random_small_document_data_file = (
        r"/data/emea_ar/basic_information/English/lux_english_ar_top_100_provider_random_small_document.xlsx"
    )
    
    doc_provider_file_path = r"/data/emea_ar/basic_information/English/sample_doc/emea_final_typical_case/Final list of EMEA documents.xlsx"
    pdf_folder = r"/data/emea_ar/pdf/"
    # download_pdf(
    #     doc_provider_file_path=doc_provider_file_path,
    #     sheet_name="Sheet1",
    #     doc_id_column="Document Id",
    #     pdf_path=pdf_folder)
    
    pdf_folder = r"/data/aus_prospectus/pdf/"
    output_folder = r"/data/aus_prospectus/pdf_txt/"
    output_pdf_page_text(pdf_folder, output_folder)
    
    # extract_pdf_table(pdf_folder, output_folder)
    # analyze_json_error()

    latest_top_100_provider_ar_data_file = r"/data/emea_ar/basic_information/English/top_100_provider_latest_document_most_mapping/lux_english_ar_from_top_100_provider_latest_document_with_most_mappings.xlsx"
    # download_pdf(latest_top_100_provider_ar_data_file, 
    #              'latest_ar_document_most_mapping', 
    #              pdf_folder)
    
    
    doc_ar_data_file_path = r"/data/emea_ar/basic_information/sample_documents/sample_doc/sample_documents_12_11/doc_ar_data_12_11.xlsx"
    doc_mapping_data_file_path = r"/data/emea_ar/basic_information/sample_documents/sample_doc/sample_documents_12_11/doc_mapping_12_11.xlsx"
    output_data_folder = r"/data/emea_ar/basic_information/sample_documents/sample_doc/sample_documents_12_11/"
    output_file="doc_ar_data_sample_documents_12_11_statistics.xlsx"
    
    pdf_folder = r"/data/illume/japan_prospectus/pdf/"
    doc_ar_data_file_path = None
    doc_mapping_data_file_path = r"/data/illume/japan_prospectus/materials/document_mapping.xlsx"
    output_data_folder = r"/data/illume/japan_prospectus/materials/"
    output_file = "japan_prospectus_statistics.xlsx"
    # statistics_document(pdf_folder=pdf_folder,
    #                     doc_mapping_file_path=doc_mapping_data_file_path,
    #                     doc_ar_data_file_path=doc_ar_data_file_path, 
    #                     mapping_sheet_name="Sheet1",
    #                     ar_data_sheet_name="doc_ar_data_in_db",
    #                     output_folder=output_data_folder,
    #                     output_file=output_file)
    # get_document_extracted_share_diff_by_db()
    # statistics_provider_mapping(
    #     provider_mapping_data_file=provider_mapping_data_file,
    #     output_folder=basic_info_folder,
    # )
    # statistics_document_fund_share_count(doc_mapping_from_top_100_provider_file)
    # pickup_document_from_top_100_providers()
    # compare_records_count_by_document_id()
    
    # document_mapping_folder = r"/data/emea_ar/output/mapping/document/"
    # all_data_file = r"/data/emea_ar/output/mapping/all_document_mapping.xlsx"
    # concat_mapping(document_mapping_folder, all_data_file)
    
    # provider_mapping_folder = r"/data/emea_ar/output/mapping/provider/"
    # all_data_file = r"/data/emea_ar/output/mapping/all_provider_mapping.xlsx"
    # concat_mapping(provider_mapping_folder, all_data_file)