sbt-idp/cope2n-api/fwd_api/utils/accuracy.py

import re
from datetime import datetime

import copy
from typing import Any
from .ocr_utils.ocr_metrics import eval_ocr_metric
from .ocr_utils.sbt_report import post_processing_str
from fwd_api.constant.common import FileCategory
from fwd_api.models import SubscriptionRequest, SubscriptionRequestFile, ReportFile
from ..celery_worker.client_connector import c_connector
from ..utils.file import dict2xlsx, save_workbook_file, save_report_to_S3
from ..utils.subsidiary import map_subsidiary_short_to_long
from ..utils.processing_time import backend_cost
from django.db.models import Q
from django.utils import timezone
from fwd import settings
from ..models import SubscriptionRequest, Report, ReportFile
from typing import Union, List, Dict
import logging

logger = logging.getLogger(__name__)

VALID_KEYS = ["retailername", "sold_to_party", "invoice_no", "purchase_date", "imei_number"]
OPTIONAL_KEYS = ['invoice_no']

class ReportAccumulateByRequest:
    def __init__(self, sub):
        # self.redis_client = redis.Redis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, decode_responses=True)
        self.sub = sub
        self.current_time = None
        self.data = {} # {"month": [total, {"day": day_data}]}
        self.month_format = {
                    'subs': "+",
                    'extraction_date': "Subtotal ()",
                    'num_imei': 0,
                    'num_invoice': 0,
                    'total_images': 0,
                    'images_quality': {
                        'successful': 0,
                        'successful_percent': 0,
                        'bad': 0,
                        'bad_percent': 0
                    },
                    'average_accuracy_rate': {
                        'imei_number': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailername': IterAvg(),
                        'sold_to_party': IterAvg(),
                        'invoice_no': IterAvg()
                    },
                    'average_processing_time': {
                        'imei': IterAvg(),
                        'invoice': IterAvg(),
                        'avg': IterAvg(),
                    },
                    'file_average_processing_time': {
                        'imei': IterAvg(),
                        'invoice': IterAvg(),
                        'avg': IterAvg(),
                    },
                    'usage': {
                        'imei':0,
                        'invoice': 0,
                        'total_images': 0,
                        'request': 0
                        },
                    'feedback_accuracy': {
                        'imei_number': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailername': IterAvg(),
                        'sold_to_party': IterAvg(),
                        'invoice_no': IterAvg()
                    },
                    'reviewed_accuracy': {
                        'imei_number': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailername': IterAvg(),
                        'sold_to_party': IterAvg(),
                        'invoice_no': IterAvg()
                    },
                    'num_request': 0,
                    "review_progress": []
                }
        self.day_format =  {
                    'subs': sub,
                    'extraction_date': "",
                    'num_imei': 0,
                    'num_invoice': 0,
                    'total_images': 0,
                    'images_quality': {
                        'successful': 0,
                        'successful_percent': 0,
                        'bad': 0,
                        'bad_percent': 0
                    },
                    'average_accuracy_rate': {
                        'imei_number': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailername': IterAvg(),
                        'sold_to_party': IterAvg(),
                        'invoice_no': IterAvg()
                    },
                    'average_processing_time': {
                        'imei': IterAvg(),
                        'invoice': IterAvg(),
                        'avg': IterAvg(),
                    },
                    'file_average_processing_time': {
                        'imei': IterAvg(),
                        'invoice': IterAvg(),
                        'avg': IterAvg(),
                    },
                    'usage': {
                        'imei': 0,
                        'invoice': 0,
                        'total_images': 0,
                        'request': 0
                    },
                    'feedback_accuracy': {
                        'imei_number': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailername': IterAvg(),
                        'sold_to_party': IterAvg(),
                        'invoice_no': IterAvg()
                    },
                    'reviewed_accuracy': {
                        'imei_number': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailername': IterAvg(),
                        'sold_to_party': IterAvg(),
                        'invoice_no': IterAvg()
                    },
                    "report_files": [],
                    "num_request": 0,
                    "review_progress": []
                },
        self.report = copy.deepcopy(self.month_format)
        self.report["average_accuracy_rate"]["avg"] = IterAvg()
    
    @staticmethod
    def update_total(total, report_file, image_avg_cost):
        # Update total = update total
        if report_file.bad_image_reason not in settings.ACC_EXCLUDE_RESEASONS:
            total["images_quality"]["successful"] += 1 if not report_file.is_bad_image else 0
            total["images_quality"]["bad"] += 1 if report_file.is_bad_image else 0
            total["total_images"] += 1
            doc_type = "imei"
            if report_file.doc_type in ["imei", "invoice", "all"]:
                doc_type = report_file.doc_type
            else:
                logger.warning(f"Weird doc type {report_file.doc_type} if request id: {report_file.correspond_request_id}")
            total["num_imei"] += 1 if doc_type == "imei" else 0
            total["num_invoice"] += 1 if doc_type == "invoice" else 0

            for key in settings.FIELD:
                if sum([len(report_file.reviewed_accuracy[x]) for x in report_file.reviewed_accuracy.keys() if "_count" not in x]) > 0 :
                    total["average_accuracy_rate"][key].add(report_file.reviewed_accuracy.get(key, []))
                    total["average_accuracy_rate"]['avg'].add(report_file.reviewed_accuracy.get(key, []))
                elif sum([len(report_file.feedback_accuracy[x]) for x in report_file.feedback_accuracy.keys() if "_count" not in x]) > 0:
                    total["average_accuracy_rate"][key].add(report_file.feedback_accuracy.get(key, []))
                    total["average_accuracy_rate"]['avg'].add(report_file.feedback_accuracy.get(key, []))
                total["feedback_accuracy"][key].add(report_file.feedback_accuracy.get(key, []))
                total["reviewed_accuracy"][key].add(report_file.reviewed_accuracy.get(key, []))

            if not total["average_processing_time"].get(report_file.doc_type, None):
                logger.warning(f"Weird doctype: {report_file.doc_type}")
                total["average_processing_time"][report_file.doc_type] = IterAvg()
            total["average_processing_time"][report_file.doc_type].add_avg(report_file.time_cost, 1) if report_file.time_cost else 0
            total["average_processing_time"]["avg"].add_avg(report_file.time_cost, 1) if report_file.time_cost else 0 

            if not total["file_average_processing_time"].get(report_file.doc_type, None):
                logger.warning(f"Weird doctype: {report_file.doc_type}")
                total["file_average_processing_time"][report_file.doc_type] = IterAvg()
            total["file_average_processing_time"][report_file.doc_type].add_avg(image_avg_cost, 1) if image_avg_cost else 0
            total["file_average_processing_time"]["avg"].add_avg(image_avg_cost, 1) if image_avg_cost else 0

            doc_type = "imei"
            if report_file.doc_type in ["imei", "invoice", "all"]:
                doc_type = report_file.doc_type
            else:
                logger.warning(f"Weird doc type {report_file.doc_type} if request id: {report_file.correspond_request_id}")
            total["usage"]["imei"] += 1 if doc_type == "imei" else 0
            total["usage"]["invoice"] += 1 if doc_type == "invoice" else 0
            total["usage"]["total_images"] += 1
            total["review_progress"].append(report_file.review_status)
        return total

    @staticmethod
    def update_month(month, report_file, image_avg_cost):
        # Update month = update month
        if report_file.bad_image_reason not in settings.ACC_EXCLUDE_RESEASONS:
            month["images_quality"]["successful"] += 1 if not report_file.is_bad_image else 0
            month["images_quality"]["bad"] += 1 if report_file.is_bad_image else 0
            month["total_images"] += 1
            doc_type = "imei"
            if report_file.doc_type in ["imei", "invoice", "all"]:
                doc_type = report_file.doc_type
            else:
                logger.warning(f"Weird doc type {report_file.doc_type} if request id: {report_file.correspond_request_id}")
            month["num_imei"] += 1 if doc_type == "imei" else 0
            month["num_invoice"] += 1 if doc_type == "invoice" else 0

            for key in settings.FIELD:
                if sum([len(report_file.reviewed_accuracy[x]) for x in report_file.reviewed_accuracy.keys() if "_count" not in x]) > 0 :
                    month["average_accuracy_rate"][key].add(report_file.reviewed_accuracy.get(key, []))
                elif sum([len(report_file.feedback_accuracy[x]) for x in report_file.feedback_accuracy.keys() if "_count" not in x]) > 0:
                    month["average_accuracy_rate"][key].add(report_file.feedback_accuracy.get(key, []))
                month["feedback_accuracy"][key].add(report_file.feedback_accuracy.get(key, []))
                month["reviewed_accuracy"][key].add(report_file.reviewed_accuracy.get(key, []))

            if not month["average_processing_time"].get(report_file.doc_type, None):
                logger.warning(f"Weird doctype: {report_file.doc_type}")
                month["average_processing_time"][report_file.doc_type] = IterAvg()
            month["average_processing_time"][report_file.doc_type].add_avg(report_file.time_cost, 1) if report_file.time_cost else 0
            month["average_processing_time"]["avg"].add_avg(report_file.time_cost, 1) if report_file.time_cost else 0 

            if not month["file_average_processing_time"].get(report_file.doc_type, None):
                logger.warning(f"Weird doctype: {report_file.doc_type}")
                month["file_average_processing_time"][report_file.doc_type] = IterAvg()
            month["file_average_processing_time"][report_file.doc_type].add_avg(image_avg_cost, 1) if image_avg_cost else 0
            month["file_average_processing_time"]["avg"].add_avg(image_avg_cost, 1) if image_avg_cost else 0 

            doc_type = "imei"
            if report_file.doc_type in ["imei", "invoice", "all"]:
                doc_type = report_file.doc_type
            else:
                logger.warning(f"Weird doc type {report_file.doc_type} if request id: {report_file.correspond_request_id}")
            month["usage"]["imei"] += 1 if doc_type == "imei" else 0
            month["usage"]["invoice"] += 1 if doc_type == "invoice" else 0
            month["usage"]["total_images"] += 1
            month["review_progress"].append(report_file.review_status)
        return month

    @staticmethod
    def update_day(day_data, report_file, image_avg_cost):
        if report_file.bad_image_reason not in settings.ACC_EXCLUDE_RESEASONS:
            day_data["images_quality"]["successful"] += 1 if not report_file.is_bad_image else 0
            day_data["images_quality"]["bad"] += 1 if report_file.is_bad_image else 0
            day_data["total_images"] += 1
            doc_type = "imei"
            if report_file.doc_type in ["imei", "invoice", "all"]:
                doc_type = report_file.doc_type
            else:
                logger.warning(f"Weird doc type {report_file.doc_type} if request id: {report_file.correspond_request_id}")
            day_data["num_imei"] += 1 if doc_type == "imei" else 0
            day_data["num_invoice"] += 1 if doc_type == "invoice" else 0
            day_data["report_files"].append(report_file)
            
            for key in settings.FIELD:
                if sum([len(report_file.reviewed_accuracy[x]) for x in report_file.reviewed_accuracy.keys() if "_count" not in x]) > 0:
                    day_data["average_accuracy_rate"][key].add(report_file.reviewed_accuracy.get(key, []))
                elif sum([len(report_file.feedback_accuracy[x]) for x in report_file.feedback_accuracy.keys() if "_count" not in x]) > 0:
                    day_data["average_accuracy_rate"][key].add(report_file.feedback_accuracy.get(key, []))
                day_data["feedback_accuracy"][key].add(report_file.feedback_accuracy.get(key, []))
                day_data["reviewed_accuracy"][key].add(report_file.reviewed_accuracy.get(key, []))

            if not day_data["average_processing_time"].get(report_file.doc_type, None):
                logger.warning(f"Weird doctype: {report_file.doc_type}")
                day_data["average_processing_time"][report_file.doc_type] = IterAvg()
            day_data["average_processing_time"][report_file.doc_type].add_avg(report_file.time_cost, 1) if report_file.time_cost else 0
            day_data["average_processing_time"]['avg'].add_avg(report_file.time_cost, 1) if report_file.time_cost else 0 

            if not day_data["file_average_processing_time"].get(report_file.doc_type, None):
                logger.warning(f"Weird doctype: {report_file.doc_type}")
                day_data["file_average_processing_time"][report_file.doc_type] = IterAvg()
            day_data["file_average_processing_time"][report_file.doc_type].add_avg(image_avg_cost, 1) if image_avg_cost else 0
            day_data["file_average_processing_time"]['avg'].add_avg(image_avg_cost, 1) if image_avg_cost else 0 

            day_data["review_progress"].append(report_file.review_status)
        return day_data
    
    def add(self, request, report_files, report):
        this_month = timezone.localtime(request.created_at).strftime("%Y%m")
        this_day = timezone.localtime(request.created_at).strftime("%Y%m%d")
        if not self.data.get(this_month, None):
            self.data[this_month] = [copy.deepcopy(self.month_format), {}]
            self.data[this_month][0]["extraction_date"] = "Subtotal (" + timezone.localtime(request.created_at).strftime("%Y-%m") + ")"
        if not self.data[this_month][1].get(this_day, None):
            logger.info(f" Adding a new day: {this_day} for report: {report.id} ...")
            self.data[this_month][1][this_day] = copy.deepcopy(self.day_format)[0]
            self.data[this_month][1][this_day]['extraction_date'] = timezone.localtime(request.created_at).strftime("%Y-%m-%d")
            usage = self.count_transactions_within_day(this_day)
            self.data[this_month][1][this_day]["usage"]["imei"] = usage.get("imei", 0)
            self.data[this_month][1][this_day]["usage"]["invoice"] = usage.get("invoice", 0)
            self.data[this_month][1][this_day]["usage"]["request"] = usage.get("request", 0)
            self.data[this_month][1][this_day]["usage"]["total_images"] = usage.get("imei", 0) + usage.get("invoice", 0)

        self.data[this_month][1][this_day]['num_request'] += 1
        self.data[this_month][0]['num_request'] += 1
        _number_of_file = request.pages
        _be_cost = backend_cost(request.created_at, request.ai_inference_start_time)
        _ai_cost = request.ai_inference_time
        processing_time_by_averaging_request_cost = (_be_cost + _ai_cost)/_number_of_file if _number_of_file > 0 else 0
        for report_file in report_files:
            # report_file.time_cost = processing_time_by_averaging_request_cost
            _report_file = copy.deepcopy(report_file)
            if _report_file.is_bad_image or _report_file.bad_image_reason in settings.ACC_EXCLUDE_RESEASONS:
                _report_file.acc = None
                for t in _report_file.feedback_accuracy.keys():
                    _report_file.feedback_accuracy[t] = []
                for t in _report_file.reviewed_accuracy.keys():
                    _report_file.reviewed_accuracy[t] = []

            self.report = self.update_total(self.report, _report_file, processing_time_by_averaging_request_cost)
            self.data[this_month][0] = self.update_month(self.data[this_month][0], _report_file, processing_time_by_averaging_request_cost) # Update the subtotal within the month
            self.data[this_month][1][this_day] = self.update_day(self.data[this_month][1][this_day], _report_file, processing_time_by_averaging_request_cost) # Update the subtotal of the day

    def count_transactions_within_day(self, date_string):
        start_date = datetime.strptime(date_string, "%Y%m%d")
        start_date_with_timezone = timezone.make_aware(start_date)
        end_date_with_timezone = start_date_with_timezone + timezone.timedelta(days=1)
        return count_transactions(start_date_with_timezone, end_date_with_timezone, self.sub)

    def save(self, root_report_id, is_daily_report=False, include_test=False):
        report_data, overall_report = self.get()
        fine_data = []
        save_data = {"file": {"overview": f"{root_report_id}/{root_report_id}.xlsx"},
                    "data": fine_data, # {"sub_report_id": "S3 location", "data": fine_data}
                    "report": overall_report}
        # extract data
        month_keys = list(report_data.keys())
        month_keys.sort(reverse=True)
        for month in month_keys:
            fine_data.append(report_data[month][0])
            day_keys = list(report_data[month][1].keys())
            day_keys.sort(reverse = True)
            for day in day_keys:
                report_data[month][1][day]['subs'] = map_subsidiary_short_to_long(report_data[month][1][day]['subs'])
                fine_data.append(report_data[month][1][day])
                # save daily reports
                report_id = root_report_id + "_" + day
                start_date = datetime.strptime(day, "%Y%m%d")
                start_date_with_timezone = timezone.make_aware(start_date)
                end_date_with_timezone = start_date_with_timezone + timezone.timedelta(days=1)
                _average_OCR_time = {"invoice": self.data[month][1][day]["average_processing_time"]["invoice"](), "imei": self.data[month][1][day]["average_processing_time"]["imei"](),
                                   "invoice_count": self.data[month][1][day]["average_processing_time"]["invoice"].count, "imei_count": self.data[month][1][day]["average_processing_time"]["imei"].count}
                _average_OCR_time["invoice"] = 0 if _average_OCR_time["invoice"] is None else _average_OCR_time["invoice"]
                _average_OCR_time["imei"] = 0 if _average_OCR_time["imei"] is None else _average_OCR_time["imei"]
                if "avg" not in _average_OCR_time:
                    _average_OCR_time["avg"] = (_average_OCR_time["invoice"]*_average_OCR_time["invoice_count"] + _average_OCR_time["imei"]*_average_OCR_time["imei_count"])/(_average_OCR_time["imei_count"] + _average_OCR_time["invoice_count"]) if (_average_OCR_time["imei_count"] + _average_OCR_time["invoice_count"]) > 0 else None
                
                _file_average_OCR_time = {"invoice": self.data[month][1][day]["file_average_processing_time"]["invoice"](), "imei": self.data[month][1][day]["file_average_processing_time"]["imei"](),
                                   "invoice_count": self.data[month][1][day]["file_average_processing_time"]["invoice"].count, "imei_count": self.data[month][1][day]["file_average_processing_time"]["imei"].count}
                _file_average_OCR_time["invoice"] = 0 if _file_average_OCR_time["invoice"] is None else _file_average_OCR_time["invoice"]
                _file_average_OCR_time["imei"] = 0 if _file_average_OCR_time["imei"] is None else _file_average_OCR_time["imei"]
                if "avg" not in _file_average_OCR_time:
                    _file_average_OCR_time["avg"] = (_file_average_OCR_time["invoice"]*_file_average_OCR_time["invoice_count"] + _file_average_OCR_time["imei"]*_file_average_OCR_time["imei_count"])/(_file_average_OCR_time["imei_count"] + _file_average_OCR_time["invoice_count"]) if (_file_average_OCR_time["imei_count"] + _file_average_OCR_time["invoice_count"]) > 0 else None

                acumulated_acc = {"feedback_accuracy": {},
                    "reviewed_accuracy": {}}
                for acc_type in ["feedback_accuracy", "reviewed_accuracy"]:
                    avg_acc = IterAvg()
                    for key in settings.FIELD:
                        acumulated_acc[acc_type][key] = self.data[month][1][day][acc_type][key]()
                        acumulated_acc[acc_type][key+"_count"] = self.data[month][1][day][acc_type][key].count
                        avg_acc.add_avg(acumulated_acc[acc_type][key], acumulated_acc[acc_type][key+"_count"])
                    acumulated_acc[acc_type]["avg"] = avg_acc()
                    acumulated_acc[acc_type]["avg_count"] = avg_acc.count
                new_report: Report = Report(
                        report_id=report_id,
                        is_daily_report=is_daily_report,
                        subsidiary=self.sub.lower().replace(" ", ""),
                        include_test=include_test,
                        start_at=start_date_with_timezone,
                        end_at=end_date_with_timezone,
                        status="Ready",
                        number_request=report_data[month][1][day]["num_request"],
                        number_images=report_data[month][1][day]["total_images"],
                        number_imei=report_data[month][1][day]["num_imei"],
                        number_invoice=report_data[month][1][day]["num_invoice"],
                        number_bad_images=report_data[month][1][day]["images_quality"]["bad"],
                        average_OCR_time=_file_average_OCR_time,
                        number_imei_transaction=report_data[month][1][day]["usage"]["imei"],
                        number_invoice_transaction=report_data[month][1][day]["usage"]["invoice"],
                        feedback_accuracy=acumulated_acc["feedback_accuracy"],
                        reviewed_accuracy=acumulated_acc["reviewed_accuracy"],
                    )
                if is_daily_report:
                    new_report.save()
                    data = extract_report_detail_list(self.data[month][1][day]["report_files"], lower=True)
                    data_workbook = dict2xlsx(data, _type='report_detail')
                    local_workbook = save_workbook_file(report_id + ".xlsx", new_report, data_workbook)
                    s3_key=save_report_to_S3(report_id, local_workbook)
        return fine_data, save_data
    
    def get(self) -> Any:
        # FIXME: This looks like a junk
        _data = copy.deepcopy(self.data)
        _report = copy.deepcopy(self.report)
        # export report data
        for key in _report["average_processing_time"].keys():
            _report["average_processing_time"][key] = _report["average_processing_time"][key]()
        _ks = list(_report["file_average_processing_time"].keys())
        for key in _ks:
            _report["file_average_processing_time"][key+"_count"] = _report["file_average_processing_time"][key].count
            _report["file_average_processing_time"][key] = _report["file_average_processing_time"][key]()

    
        # avg_acc = 0
        # count_acc = 0
        for key in settings.FIELD:
            _report["average_accuracy_rate"][key] = _report["average_accuracy_rate"][key]()
            for accuracy_type in ["feedback_accuracy", "reviewed_accuracy"]:
            #     if (_report[accuracy_type][key].count + count_acc) > 0:
            #         avg_acc = (avg_acc*count_acc + _report[accuracy_type][key].avg*_report[accuracy_type][key].count) / (_report[accuracy_type][key].count + count_acc)
            #         count_acc += _report[accuracy_type][key].count
                
                _report[accuracy_type][key] = _report[accuracy_type][key]()
        _report["average_accuracy_rate"]["avg"] = _report["average_accuracy_rate"]["avg"]()

        _report["review_progress"] = _report["review_progress"].count(1)/(_report["review_progress"].count(0)+ _report["review_progress"].count(1)) if (_report["review_progress"].count(0)+ _report["review_progress"].count(1)) >0 else 1.0
        _report["images_quality"]["successful_percent"] = _report["images_quality"]["successful"]/_report["total_images"]  if _report["total_images"] > 0 else 0
        _report["images_quality"]["bad_percent"] = _report["images_quality"]["bad"]/_report["total_images"] if _report["total_images"] > 0 else 0
        # export data for dashboard
        for month in _data.keys():
            _data[month][0]["images_quality"]["successful_percent"] = _data[month][0]["images_quality"]["successful"]/_data[month][0]["total_images"] if _data[month][0]["total_images"] > 0 else 0
            _data[month][0]["images_quality"]["bad_percent"] = _data[month][0]["images_quality"]["bad"]/_data[month][0]["total_images"] if _data[month][0]["total_images"] > 0 else 0
            num_transaction_imei = 0
            num_transaction_invoice = 0
            for day in _data[month][1].keys():
                num_transaction_imei += _data[month][1][day]["usage"].get("imei", 0)
                num_transaction_invoice += _data[month][1][day]["usage"].get("invoice", 0)
                for key in _data[month][1][day]["average_processing_time"].keys():
                    _data[month][1][day]["average_processing_time"][key] = _data[month][1][day]["average_processing_time"][key]()
                for key in _data[month][1][day]["file_average_processing_time"].keys():
                    _data[month][1][day]["file_average_processing_time"][key] = _data[month][1][day]["file_average_processing_time"][key]()
            
                for key in settings.FIELD:
                    _data[month][1][day]["average_accuracy_rate"][key] = _data[month][1][day]["average_accuracy_rate"][key]()
                    for accuracy_type in ["feedback_accuracy", "reviewed_accuracy"]:
                        _data[month][1][day][accuracy_type][key] = _data[month][1][day][accuracy_type][key]()
                _data[month][1][day]["review_progress"] = _data[month][1][day]["review_progress"].count(1)/(_data[month][1][day]["review_progress"].count(0)+ _data[month][1][day]["review_progress"].count(1)) if (_data[month][1][day]["review_progress"].count(0)+ _data[month][1][day]["review_progress"].count(1)) >0 else 0
                _data[month][1][day].pop("report_files")

                _data[month][1][day]["images_quality"]["successful_percent"] = _data[month][1][day]["images_quality"]["successful"]/_data[month][1][day]["total_images"]  if _data[month][1][day]["total_images"] > 0 else 0
                _data[month][1][day]["images_quality"]["bad_percent"] = _data[month][1][day]["images_quality"]["bad"]/_data[month][1][day]["total_images"] if _data[month][1][day]["total_images"] > 0 else 0

            _data[month][0]["usage"]["imei"] = num_transaction_imei
            _data[month][0]["usage"]["invoice"] = num_transaction_invoice
            _data[month][0]["usage"]["total_images"] = num_transaction_invoice + num_transaction_imei
            for key in _data[month][0]["average_processing_time"].keys():
                _data[month][0]["average_processing_time"][key] = _data[month][0]["average_processing_time"][key]()
            for key in _data[month][0]["file_average_processing_time"].keys():
                _data[month][0]["file_average_processing_time"][key] = _data[month][0]["file_average_processing_time"][key]()
            for key in settings.FIELD:
                _data[month][0]["average_accuracy_rate"][key] = _data[month][0]["average_accuracy_rate"][key]()
                for accuracy_type in ["feedback_accuracy", "reviewed_accuracy"]:
                    _data[month][0][accuracy_type][key] = _data[month][0][accuracy_type][key]()
            _data[month][0]["review_progress"] = _data[month][0]["review_progress"].count(1)/(_data[month][0]["review_progress"].count(0)+ _data[month][0]["review_progress"].count(1)) if (_data[month][0]["review_progress"].count(0)+ _data[month][0]["review_progress"].count(1)) >0 else 0
        return _data, _report
    
class MonthReportAccumulate:
    def __init__(self):
        self.month = None
        self.total = {
                    'subs': "+",
                    'extraction_date': "Subtotal ()",
                    'total_images': 0,
                    'images_quality': {
                        'successful': 0,
                        'successful_percent': 0,
                        'bad': 0,
                        'bad_percent': 0
                    },
                    'average_accuracy_rate': {
                        'imei': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailer_name': IterAvg(),
                        'invoice_no': IterAvg()
                    },
                    'average_processing_time': {
                        'imei': IterAvg(),
                        'invoice': IterAvg()
                    },
                    'usage': {
                        'imei':0,
                        'invoice': 0
                        }
                    }
        self.data = []
        self.data_format =  {
                    'subs': "",
                    'extraction_date': "",
                    'num_imei': 0,
                    'num_invoice': 0,
                    'total_images': 0,
                    'images_quality': {
                        'successful': 0,
                        'successful_percent': 0,
                        'bad': 0,
                        'bad_percent': 0
                    },
                    'average_accuracy_rate': {
                        'imei': 0,
                        'purchase_date': 0,
                        'retailer_name': 0,
                        'invoice_no': 0
                    },
                    'average_processing_time': {
                        'imei': 0,
                        'invoice': 0
                    },
                    'usage': {
                        'imei':0,
                        'invoice': 0
                        }
                },

    def accumulate(self, report):
        self.total["total_images"] += report.number_images
        self.total["images_quality"]["successful"] += report.number_images - report.number_bad_images
        self.total["images_quality"]["bad"] += report.number_bad_images
        

        if sum([report.reviewed_accuracy[x] for x in report.reviewed_accuracy.keys() if "_count" not in x]) > 0 :
            self.total["average_accuracy_rate"]["imei"].add_avg(report.reviewed_accuracy.get("imei_number", 0), report.reviewed_accuracy.get("imei_number_count", 0))
            self.total["average_accuracy_rate"]["purchase_date"].add_avg(report.reviewed_accuracy.get("purchase_date", 0), report.reviewed_accuracy.get("purchase_date_count", 0))
            self.total["average_accuracy_rate"]["retailer_name"].add_avg(report.reviewed_accuracy.get("retailername", 0), report.reviewed_accuracy.get("retailername_count", 0))
            self.total["average_accuracy_rate"]["invoice_no"].add_avg(report.reviewed_accuracy.get("invoice_no", 0), report.reviewed_accuracy.get("invoice_no_count", 0))
        elif sum([ report.feedback_accuracy[x] for x in report.feedback_accuracy.keys() if "_count" not in x]) > 0:
            self.total["average_accuracy_rate"]["imei"].add_avg(report.feedback_accuracy.get("imei_number", 0), report.feedback_accuracy.get("imei_number_count", 0))
            self.total["average_accuracy_rate"]["purchase_date"].add_avg(report.feedback_accuracy.get("purchase_date", 0), report.feedback_accuracy.get("purchase_date_count", 0))
            self.total["average_accuracy_rate"]["retailer_name"].add_avg(report.feedback_accuracy.get("retailername", 0), report.feedback_accuracy.get("retailername_count", 0))
            self.total["average_accuracy_rate"]["invoice_no"].add_avg(report.feedback_accuracy.get("invoice_no", 0), report.feedback_accuracy.get("invoice_no_count", 0))

        self.total["average_processing_time"]["imei"].add_avg(report.average_OCR_time.get("imei", 0), report.average_OCR_time.get("imei_count", 0)) if report.average_OCR_time else 0
        self.total["average_processing_time"]["invoice"].add_avg(report.average_OCR_time.get("invoice", 0), report.average_OCR_time.get("invoice_count", 0))  if report.average_OCR_time else 0
        self.total["usage"]["imei"] += report.number_imei_transaction
        self.total["usage"]["invoice"] += report.number_invoice_transaction

    def add(self, report):
        report_month = report.start_at.month

        if self.month is None:
            self.month = report_month
            self.total["extraction_date"] = f"Subtotal ({self.month})"

        elif self.month != report_month:
            self.total["images_quality"]["successful_percent"] += self.total["images_quality"]["successful"]/self.total["total_images"]
            self.total["images_quality"]["bad_percent"] += self.total["images_quality"]["bad"]/self.total["total_images"]
            return False  # Reports from a different month, stop accumulating
        # accumulate fields
        new_data = copy.deepcopy(self.data_format)[0]
        new_data["num_imei"] = report.number_imei
        new_data["subs"] = report.subsidiary
        new_data["extraction_date"] = report.start_at
        new_data["num_invoice"] = report.number_invoice
        new_data["total_images"] = report.number_images
        new_data["images_quality"]["successful"] = report.number_images - report.number_bad_images
        new_data["images_quality"]["bad"] = report.number_bad_images

        report.reviewed_accuracy = {} if report.reviewed_accuracy is None else report.reviewed_accuracy
        report.feedback_accuracy = {} if report.feedback_accuracy is None else report.feedback_accuracy
        if sum([ report.reviewed_accuracy[x] for x in report.reviewed_accuracy.keys() if "_count" not in x]):
            new_data["average_accuracy_rate"]["imei"] = report.reviewed_accuracy.get("imei_number", None)
            new_data["average_accuracy_rate"]["purchase_date"] = report.reviewed_accuracy.get("purchase_date", None)
            new_data["average_accuracy_rate"]["retailer_name"] = report.reviewed_accuracy.get("retailername", None)
            new_data["average_accuracy_rate"]["invoice_no"] = report.reviewed_accuracy.get("invoice_no", None)
        elif sum([ report.feedback_accuracy[x] for x in report.feedback_accuracy.keys() if "_count" not in x]):
            new_data["average_accuracy_rate"]["imei"] = report.feedback_accuracy.get("imei_number", None)
            new_data["average_accuracy_rate"]["purchase_date"] = report.feedback_accuracy.get("purchase_date", None)
            new_data["average_accuracy_rate"]["retailer_name"] = report.feedback_accuracy.get("retailername", None)
            new_data["average_accuracy_rate"]["invoice_no"] = report.feedback_accuracy.get("invoice_no", None)
        
        new_data["average_processing_time"]["imei"] = report.average_OCR_time.get("imei", 0) if report.average_OCR_time else 0
        new_data["average_processing_time"]["invoice"] = report.average_OCR_time.get("invoice", 0) if report.average_OCR_time else 0
        new_data["usage"]["imei"] = report.number_imei_transaction
        new_data["usage"]["invoice"] = report.number_invoice_transaction

        new_data["images_quality"]["successful_percent"] += new_data["images_quality"]["successful"]/new_data["total_images"] if new_data["total_images"] else 0
        new_data["images_quality"]["bad_percent"] += new_data["images_quality"]["bad"]/new_data["total_images"] if new_data["total_images"] else 0
        self.data.append(new_data)
        self.accumulate(report)
        return True

    def clear(self):
        self.month = None
        self.total = {
                    'subs': "+",
                    'extraction_date': "Subtotal ()",
                    'total_images': 0,
                    'images_quality': {
                        'successful': 0,
                        'successful_percent': 0,
                        'bad': 0,
                        'bad_percent': 0
                    },
                    'average_accuracy_rate': {
                        'imei': IterAvg(),
                        'purchase_date': IterAvg(),
                        'retailer_name': IterAvg()
                    },
                    'average_processing_time': {
                        'imei': IterAvg(),
                        'invoice': IterAvg()
                    },
                    'usage': {
                        'imei':0,
                        'invoice': 0
                        }
                    }
        self.data = []

    def __call__(self):
        total = copy.deepcopy(self.total)
        total["images_quality"]["successful_percent"] = total["images_quality"]["successful"]/total["total_images"] if total["total_images"] else 0
        total["images_quality"]["bad_percent"] = total["images_quality"]["bad"]/total["total_images"] if total["total_images"] else 0
        total["average_accuracy_rate"]["imei"] = total["average_accuracy_rate"]["imei"]()
        total["average_accuracy_rate"]["purchase_date"] = total["average_accuracy_rate"]["purchase_date"]()
        total["average_accuracy_rate"]["retailer_name"] = total["average_accuracy_rate"]["retailer_name"]()
        total["average_processing_time"]["imei"] = total["average_processing_time"]["imei"]()
        total["average_processing_time"]["invoice"] = total["average_processing_time"]["invoice"]()
        return self.month, self.data, total

class IterAvg:
    def __init__(self, name="default"):
        self.name = name
        self.avg = 0
        self.count = 0
    
    def add(self, values):
        """
        Args: 
            values (list[float]):
        """
        values = [x for x in values if x is not None]
        if len(values) == 0:
            return 
        self.avg = (self.avg*self.count + sum(values))/(self.count+len(values))
        self.count += len(values)
    
    def add_avg(self, avg, count):
        if avg is None or count is None or count == 0:
            return
        self.count += count
        self.avg = (self.avg*(self.count-count) + avg*count)/(self.count)
    
    def __call__(self):
        if self.count == 0:
            return None
        return self.avg

def validate_feedback_file(feedback, predict):
    if feedback:
        imei_feedback = feedback.get("imei_number", [])
        imei_feedback = [x for x in imei_feedback if x != ""]
        num_imei_feedback = len(imei_feedback)
        num_imei_predict = len(predict.get("imei_number", []))
        if num_imei_feedback != num_imei_predict:
            return False
        feedback["imei_number"] = imei_feedback
    return True

def first_of_list(the_list):
    if not the_list:
        return None
    return the_list[0]

def _feedback_invoice_no_exist(feedback_result):
    if feedback_result is None:
        return True
    if not isinstance(feedback_result, dict):
        return True
    invoice_no = feedback_result.get("invoice_no", None)
    if invoice_no in ["", [], None]:
        return False
    else:
        return True

def extract_report_detail_list(report_detail_list, lower=False, in_percent=True):
    data = []
    for report_file in report_detail_list:
        data.append({
            "Subs": report_file.subsidiary,
            "Request ID": report_file.correspond_request_id,
            "OCR Extraction Date": format_datetime_for_report_detail(report_file.correspond_request_created_at),
            "Redemption Number": report_file.correspond_redemption_id,
            "Image type": report_file.doc_type,
            "IMEI_user submitted": first_of_list(report_file.feedback_result.get("imei_number", [None])) if report_file.feedback_result else None,
            "IMEI_OCR retrieved": first_of_list(report_file.predict_result.get("imei_number", [None])),
            "IMEI Revised": first_of_list(report_file.reviewed_result.get("imei_number", [None])) if report_file.reviewed_result else None,
            "IMEI1 Accuracy": first_of_list(report_file.feedback_accuracy.get("imei_number", [None])),
            "Invoice_Number_User": report_file.feedback_result.get("invoice_no", None) if report_file.feedback_result else None,
            "Invoice_Number_OCR": report_file.predict_result.get("invoice_no", None),
            "Invoice_Number Revised": report_file.reviewed_result.get("invoice_no", None) if report_file.reviewed_result else None,
            "Invoice_Number_Accuracy": first_of_list(report_file.feedback_accuracy.get("invoice_no", [None])) if _feedback_invoice_no_exist(report_file.feedback_result) else None,
            "Invoice_Purchase Date_Consumer": report_file.feedback_result.get("purchase_date", None) if report_file.feedback_result else None,
            "Invoice_Purchase Date_OCR": format_purchase_date_ocr_for_report(report_file.predict_result.get("purchase_date", [])),
            "Invoice_Purchase Date Revised": report_file.reviewed_result.get("purchase_date", None) if report_file.reviewed_result else None,
            "Invoice_Purchase Date Accuracy": first_of_list(report_file.feedback_accuracy.get("purchase_date", [None])),
            "Invoice_Retailer_Consumer": report_file.feedback_result.get("retailername", None)  if report_file.feedback_result else None,
            "Invoice_Retailer_OCR": report_file.predict_result.get("retailername", None),
            "Invoice_Retailer Revised": report_file.reviewed_result.get("retailername", None) if report_file.reviewed_result else None,
            "Invoice_Retailer Accuracy": first_of_list(report_file.feedback_accuracy.get("retailername", [None])),
            "OCR Image Accuracy": report_file.acc,
            "OCR Image Speed (seconds)": report_file.time_cost,
            "Is Reviewed": report_file.is_reviewed,
            "Bad Image Reasons": report_file.bad_image_reason,
            "Countermeasures": report_file.counter_measures,
            "IMEI_Revised Accuracy": first_of_list(report_file.reviewed_accuracy.get("imei_number", [None])),
            "Purchase Date_Revised Accuracy": first_of_list(report_file.reviewed_accuracy.get("purchase_date", [None])),
            "Retailer_Revised Accuracy": first_of_list(report_file.reviewed_accuracy.get("retailername", [None])),
            "Invoice_Number_Revised Accuracy": first_of_list(report_file.reviewed_accuracy.get("invoice_no", [None]))
        })
    if lower:
        for i, dat in enumerate(data):
            keys = list(dat.keys())
            for old_key in keys:
                data[i][old_key.lower().replace(" ", "_")] = data[i].pop(old_key)
    if in_percent:
        for i, dat in enumerate(data):
            keys = [x for x in list(dat.keys()) if "accuracy" in x.lower()]
            for key in keys:
                if data[i][key]:
                    data[i][key] = data[i][key]*100
    return data

def format_datetime_for_report_detail(ocr_extraction_date):
    naived_time = timezone.make_naive(ocr_extraction_date)
    ocr_extraction_date = timezone.make_aware(value=naived_time, timezone=timezone.get_current_timezone())
    format_to_date = '%Y-%m-%d'
    ocr_extraction_date = ocr_extraction_date.strftime(format_to_date)
    return ocr_extraction_date

def format_purchase_date_ocr_for_report(ocr_result):
    return ", ".join(ocr_result)

def count_transactions(start_date, end_date, subsidiary="all"):
    base_query = Q(created_at__range=(start_date, end_date))
    base_query &= Q(is_test_request=False)
    if subsidiary and subsidiary.lower().replace(" ", "") not in settings.SUB_FOR_BILLING:
        base_query &= Q(redemption_id__startswith=subsidiary)
    transaction_att = {}

    total_transaction_requests = SubscriptionRequest.objects.filter(base_query).order_by('created_at')
    for request in total_transaction_requests:
        if not request.doc_type:
            continue
        doc_types = request.doc_type.split(",")
        for doc_type in doc_types:
            if transaction_att.get(doc_type, None) == None:
                transaction_att[doc_type] = 1
            else:
                transaction_att[doc_type] += 1
        if not transaction_att.get("request", None):
            transaction_att["request"] = 1
        else:
            transaction_att["request"] += 1
    return transaction_att

def convert_datetime_format(date_string: str, is_gt=False) -> str:
    # pattern_date_string = "2023-02-28"
    input_format = "%Y-%m-%d"
    output_format = "%d/%m/%Y"
    # Validate the input date string format
    pattern = r"\d{4}-\d{2}-\d{2}"
    if re.match(pattern, date_string):
        # Convert the date string to a datetime object
        date_object = datetime.strptime(date_string, input_format)
        # Convert the datetime object to the desired output format
        formatted_date = date_object.strftime(output_format)
        return formatted_date
    return date_string

def predict_result_to_ready(result):
    dict_result = {"retailername": "",
                   "sold_to_party": "",
                   "invoice_no": "",
                   "purchase_date": [],
                   "imei_number": [],}
    if not result:
        return dict_result
    dict_result["retailername"] = result.get("content", {}).get("document", [{}])[0].get("content", [{}])[0].get("value", None)
    dict_result["sold_to_party"] = result.get("content", {}).get("document", [{}])[0].get("content", [{}, {}])[1].get("value", None)
    dict_result["purchase_date"] = result.get("content", {}).get("document", [{}])[0].get("content", [{}, {}, {}])[2].get("value", [])
    dict_result["imei_number"] = result.get("content", {}).get("document", [{}])[0].get("content", [{}, {}, {}, {}])[3].get("value", [])
    dict_result["invoice_no"] = result.get("content", {}).get("document", [{}])[0].get("content", [{}, {}, {}, {}, {}])[4].get("value", None)
    return dict_result

def update_temp_accuracy(accuracy, acc, keys):
    for key in keys:
        accuracy[key].add(acc[key])
    return accuracy

def _accuracy_calculate_formatter(inference, target):
    """_summary_
        format type of inference, and target from str/None to List of str/None.
        Make both list inference and target to be the same length.
    """
    if not isinstance(inference, list):
        # inference = [] if inference is None else [inference]
        inference = [inference]
    if not isinstance(target, list):
        # target = [] if target is None else [target]
        target = [target]
    
    length = max(len(target), len(inference))
    target = target + (length - len(target))*[target[0]] if len(target) > 0 else target + (length - len(target))*[None]
    inference = inference + (length - len(inference))*[None]

    return inference, target

def _acc_will_be_ignored(key_name, _target):
    is_optional_key = key_name in OPTIONAL_KEYS
    is_empty_target = _target in [[], None, '']
    if is_optional_key and is_empty_target:
        return True
    else:
        return False

def calculate_accuracy(key_name: str, inference: Dict[str, Union[str, List]], target: Dict[str, Union[str, List]], type: str, sub: str):
    """_summary_
    NOTE: This has been changed to return accuracy = None if 
    Args:
        key_name (string): key to calculate accuracy on, ex: retailername
        inference (dict): result from ocr, refined to align with the target down below
        target (dict): result of type
        is_optional_keyname: default is set to False (which mean this is not an optional keyname)
                             currently we have invoice_no is an optional keyname.
    """
    acc = []
    data = []
    if not target or not inference:
        return acc, data
    
    _inference = inference[key_name]
    _target = target[key_name]
    # _will_acc_be_ignored = _acc_will_be_ignored(key_name, _target) 
    _inference, _target = _accuracy_calculate_formatter(_inference, _target)

    for i, v in enumerate(_inference):
        # TODO: target[i] is None, ""
        x = post_processing_str(key_name, _inference[i], is_gt=False, sub=sub)
        y = post_processing_str(key_name, _target[i], is_gt=True, sub=sub)

        score = eval_ocr_metric(
            [x], 
            [y], 
            metric=[
                "one_minus_ned",
                # "line_acc_ignore_case_symbol",
                # "line_acc",
                # "one_minus_ned_word",
            ])
        acc.append(list(score.values())[0])
        data.append([x, y])
    return acc, data

def calculate_avg_accuracy(acc, type, keys=[]):
    acc_list = []
    for key in keys:
        this_acc = acc.get(type, {}).get(key, [])
        if len(this_acc) > 0:
            this_acc = [max(this_acc)]
        acc_list += this_acc
    
    acc_list = [x for x in acc_list if x is not None]
    return sum(acc_list)/len(acc_list) if len(acc_list) > 0 else None


def acc_maximize_list_values(acc):
    pos = {}
    for k in acc.keys():
        pos[k] = 0
        if isinstance(acc[k], list) and len(acc[k]) > 0:
            acc[k] = [max(acc[k])] 
            pos[k] = acc[k].index(acc[k][0])
    return acc, pos


def create_billing_data(subscription_requests):
    billing_data = []
    for request in subscription_requests:
        if request.status != 200:
            continue
        images = SubscriptionRequestFile.objects.filter(request=request, file_category=FileCategory.Origin.value)
        for image in images:
            if not image.doc_type:
                _doc_type = image.file_name.split("_")[1]
                if _doc_type in ["imei", "invoice"]:
                    image.doc_type = _doc_type
                    image.save()
            else:
                _doc_type = image.doc_type

            doc_type = "SN/IMEI" if _doc_type == "imei" else "Invoice"

            _sub = ""
            redemption_id = ""
            if request.redemption_id:
                _sub = map_subsidiary_short_to_long(request.redemption_id[:2])
                redemption_id = request.redemption_id

            format_to_time = '%Y-%m-%d %H:%M'
            format_to_date = '%Y-%m-%d'
            format_to_month = '%B %Y'
            naive_created_time = timezone.make_naive(request.created_at)
            rq_created_at = timezone.make_aware(value=naive_created_time, timezone=timezone.get_current_timezone())
            rq_month = rq_created_at.strftime(format_to_month)
            rq_date = rq_created_at.strftime(format_to_date)
            rq_time = rq_created_at.strftime(format_to_time)

            billing_data.append({
                "request_month": rq_month,
                "subsidiary": _sub,
                "image_type": doc_type,
                "redemption_number": redemption_id,
                "request_id": request.request_id,
                "request_date": rq_date,
                "request_time_(utc)": rq_time
            })
    return billing_data


def avg_dict(data):
    values = []
    for k, v in data.items():
        if isinstance(v, list):
            values += v
    return sum(values)/len(values) if len(values) > 0 else -1

def calculate_a_request(report, request):
    def review_status_map(input):
        review_status = {-1: "Not Required",
                            0: "No",
                            1: "Yes"}
        return review_status.get(input, "N/A")
    atts = []
    request_att = {"acc": {"feedback": {"imei_number": [],
                                        "purchase_date": [],
                                        "retailername": [],
                                        "sold_to_party": [],
                                        "invoice_no": [],
                                        },
                            "reviewed": {"imei_number": [],
                                        "purchase_date": [],
                                        "retailername": [],
                                        "sold_to_party": [],
                                        "invoice_no": [],
                                        },
                            "acumulated":{"imei_number": [],
                                        "purchase_date": [],
                                        "retailername": [],
                                        "sold_to_party": [],
                                        "invoice_no": [],
                                        }},
                    "err": [],
                    "time_cost": {"imei": [],
                                  "invoice": []},
                    "total_images": 0,
                    "bad_images": 0,
                    "bad_image_list": [],
                    "is_reviewed": [], # -1: No need to review, 0: Not reviewed, 1: Reviewed
                    }
    images = SubscriptionRequestFile.objects.filter(request=request, file_category=FileCategory.Origin.value)
    report_files = []
    for image in images:
        status, att = calculate_subcription_file(image, request.subsidiary)
        att["acc"]["feedback"], fb_max_indexes = acc_maximize_list_values(att["acc"]["feedback"])
        att["acc"]["reviewed"], rv_max_indexes = acc_maximize_list_values(att["acc"]["reviewed"])

        _att = copy.deepcopy(att) # deep copy right here to advoid removing acc for bad images in the next steps

        fb_avg_acc = avg_dict(att["acc"]["feedback"])
        rv_avg_acc = avg_dict(att["acc"]["reviewed"])

        image.is_required = fb_avg_acc < settings.NEED_REVIEW

        if image.processing_time < 0:
            continue
        if status != 200:
            continue

        atts.append(att)
        image.feedback_accuracy = att["acc"]["feedback"] # dict {key: [values]}
        image.is_bad_image_quality = att["is_bad_image"] # is_bad_image=avg_acc<threshold (avg_acc=feedback_acc)
        
        if att["is_reviewed"]==1: # Image is already reviewed
            image.reviewed_accuracy = att["acc"]["reviewed"] # dict {key: [values]}
        
        if not image.doc_type:
            _doc_type = image.file_name.split("_")[1]
            if _doc_type in ["imei", "invoice"]:
                image.doc_type = _doc_type
        image.save()
        
        _sub = "NA"
        if request.redemption_id:
            _sub = map_subsidiary_short_to_long(request.redemption_id[:2])
        else:
            logger.warning(f"empty redemption_id, check request: {request.request_id}")

        # Little trick to replace purchase date to normalized
        if len(att["normalized_data"]["feedback"].get("purchase_date", [])) > 0:
            image.predict_result["purchase_date"] = [value_pair[0] for value_pair in att["normalized_data"]["feedback"]["purchase_date"]]
            image.feedback_result["purchase_date"] = att["normalized_data"]["feedback"]["purchase_date"][fb_max_indexes["purchase_date"]][1]
        if len(att["normalized_data"]["reviewed"].get("purchase_date", [])) > 0:
            image.predict_result["purchase_date"] = [value_pair[0] for value_pair in att["normalized_data"]["reviewed"]["purchase_date"]]
            image.reviewed_result["purchase_date"] = att["normalized_data"]["reviewed"]["purchase_date"][rv_max_indexes["purchase_date"]][1]
        request_att["is_reviewed"].append(att["is_reviewed"])

        if att["is_reviewed"] !=1:
            att["acc"]["reviewed"] = {}
            reviewed_result = {}
            reason = None
            counter_measure = None
        else:
            reviewed_result = image.reviewed_result
            reason = image.reason
            counter_measure = image.counter_measures

        if att["is_bad_image"] or image.reason in settings.ACC_EXCLUDE_RESEASONS:
            if image.reason in settings.ACC_EXCLUDE_RESEASONS:
                _att["avg_acc"] = None
                for t in _att["acc"].keys():
                    for k in _att["acc"][t].keys():
                        _att["acc"][t][k] = []
        else:
            if request_att["time_cost"].get(image.doc_type, None):
                request_att["time_cost"][image.doc_type].append(image.processing_time)
            else:
                request_att["time_cost"][image.doc_type] = [image.processing_time]

        new_report_file = ReportFile(report=report,
                                    subsidiary=_sub,
                                    correspond_request_id=request.request_id,
                                    correspond_request_created_at=request.created_at,
                                    correspond_redemption_id=request.redemption_id,
                                    doc_type=image.doc_type,
                                    predict_result=image.predict_result,
                                    feedback_result=image.feedback_result,
                                    reviewed_result=reviewed_result,
                                    feedback_accuracy=_att["acc"]["feedback"],
                                    reviewed_accuracy=_att["acc"]["reviewed"],
                                    acc=_att["avg_acc"],
                                    is_bad_image=att["is_bad_image"],
                                    is_reviewed= review_status_map(att["is_reviewed"]),
                                    time_cost=image.processing_time,
                                    bad_image_reason=reason,
                                    counter_measures=counter_measure,
                                    error="|".join(att["err"]),
                                    review_status=att["is_reviewed"],
                                    )
        report_files.append(new_report_file)
        if att["is_bad_image"]:
            request_att["bad_image_list"].append(image.file_name)
            _att["avg_acc"] = None
            for t in _att["acc"].keys():
                for k in _att["acc"][t].keys():
                    _att["acc"][t][k] = []

        try:
            request_att["acc"]["feedback"]["imei_number"] += _att["acc"]["feedback"]["imei_number"]
            request_att["acc"]["feedback"]["purchase_date"] += _att["acc"]["feedback"]["purchase_date"]
            request_att["acc"]["feedback"]["retailername"] += _att["acc"]["feedback"]["retailername"]
            request_att["acc"]["feedback"]["sold_to_party"] += _att["acc"]["feedback"]["sold_to_party"]
            request_att["acc"]["feedback"]["invoice_no"] += _att["acc"]["feedback"]["invoice_no"]
            
            request_att["acc"]["reviewed"]["imei_number"] += _att["acc"]["reviewed"]["imei_number"] if _att["is_reviewed"]==1 else []
            request_att["acc"]["reviewed"]["purchase_date"] += _att["acc"]["reviewed"]["purchase_date"] if _att["is_reviewed"]==1 else []
            request_att["acc"]["reviewed"]["retailername"] += _att["acc"]["reviewed"]["retailername"] if _att["is_reviewed"]==1 else []
            request_att["acc"]["reviewed"]["sold_to_party"] += _att["acc"]["reviewed"]["sold_to_party"] if _att["is_reviewed"]==1 else []
            request_att["acc"]["reviewed"]["invoice_no"] += _att["acc"]["reviewed"]["invoice_no"] if _att["is_reviewed"]==1 else []

            request_att["acc"]["acumulated"]["imei_number"] += _att["acc"]["reviewed"]["imei_number"] if _att["acc"]["reviewed"]["imei_number"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["imei_number"]
            request_att["acc"]["acumulated"]["purchase_date"] += _att["acc"]["reviewed"]["purchase_date"] if _att["acc"]["reviewed"]["purchase_date"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["purchase_date"]
            request_att["acc"]["acumulated"]["retailername"] += _att["acc"]["reviewed"]["retailername"] if _att["acc"]["reviewed"]["retailername"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["retailername"]
            request_att["acc"]["acumulated"]["sold_to_party"] += _att["acc"]["reviewed"]["sold_to_party"]  if _att["acc"]["reviewed"]["sold_to_party"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["sold_to_party"]
            request_att["acc"]["acumulated"]["invoice_no"] += _att["acc"]["reviewed"]["invoice_no"]  if _att["acc"]["reviewed"]["invoice_no"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["invoice_no"]

            if image.reason not in settings.ACC_EXCLUDE_RESEASONS:
                request_att["bad_images"] += int(_att["is_bad_image"])
                request_att["total_images"] += 1
            request_att["err"] += _att["err"]
        except Exception as e:
            logger.error(f"Failed to calculate request: {request.request_id} - request_file: {image.file_name} because of {e}")
            continue
 
    return request_att, report_files, atts

def calculate_subcription_file(subcription_request_file, subsidiary):
    att = {"acc": {"feedback": {},
                    "reviewed": {}},
            "normalized_data": {"feedback": {},
                                "reviewed": {}},
             "err": [],
             "is_bad_image": False,
             "avg_acc": None,
             "is_reviewed": -1,  # -1: No need to review, 0: Not reviewed, 1: Reviewed
            }
    if not subcription_request_file.predict_result:
        return 400, att

    inference_result = copy.deepcopy(subcription_request_file.predict_result)
    feedback_result = copy.deepcopy(subcription_request_file.feedback_result)
    reviewed_result = copy.deepcopy(subcription_request_file.reviewed_result)

    accuracy_keys_for_this_image = settings.FIELDS_BY_SUB.get(subsidiary, settings.FIELDS_BY_SUB["default"]).get(subcription_request_file.doc_type, [])

    for key_name in VALID_KEYS:
        att["acc"]["feedback"][key_name] = []
        att["normalized_data"]["feedback"][key_name] = []
        att["acc"]["reviewed"][key_name] = []
        att["normalized_data"]["reviewed"][key_name] = []

    for key_name in accuracy_keys_for_this_image:
        try:
            att["acc"]["feedback"][key_name], att["normalized_data"]["feedback"][key_name] = calculate_accuracy(key_name, inference_result, feedback_result, "feedback", sub=subcription_request_file.request.subsidiary)
            att["acc"]["reviewed"][key_name], att["normalized_data"]["reviewed"][key_name] = calculate_accuracy(key_name, inference_result, reviewed_result, "reviewed", sub=subcription_request_file.request.subsidiary)
        except Exception as e:
            att["err"].append(str(e))
    subcription_request_file.feedback_accuracy = att["acc"]["feedback"]
    subcription_request_file.reviewed_accuracy = att["acc"]["reviewed"]
    
    avg_reviewed = calculate_avg_accuracy(att["acc"], "reviewed", VALID_KEYS)
    avg_feedback = calculate_avg_accuracy(att["acc"], "feedback", VALID_KEYS)

    if avg_feedback is not None or avg_reviewed is not None:
        avg_acc = 0
        if avg_feedback is not None:
            avg_acc = avg_feedback
            if avg_feedback < settings.NEED_REVIEW:
                att["is_reviewed"] = 0
            else:
                att["is_reviewed"] = -1
        if avg_reviewed is not None and att["is_reviewed"]!=-1:
            avg_acc = avg_reviewed
            att["is_reviewed"] = 1

        # Little trick to overcome issue caused by misleading manually review process
        if (subcription_request_file.reason or subcription_request_file.counter_measures) and att["is_reviewed"]!=-1:
            att["is_reviewed"] = 1

        att["avg_acc"] = avg_acc
        if avg_acc < settings.BAD_THRESHOLD:
            att["is_bad_image"] = True
    return 200, att

def mean_list(l):
    l = [x for x in l if x is not None]
    if len(l) == 0:
        return 0
    return sum(l)/len(l)

def shadow_report(report_id, query):
    c_connector.make_a_report_2((report_id, query))