import json
import uuid
import numpy as np
import pandas as pd

from config.settings import BASE_DIR
from forecasts.clickhouse.db import get_session
from .replenishment_formulas_for_result import FormulasCalculation
from replen_optimizer.clickhouse.replen_optimizer_results.services.replen_result_creator import ReplenishmentResultCreator
from replen_optimizer.clickhouse.replen_optimizer_results.services.replen_result_creator import ReplenishmentOptimizerResultCreator
from replen_optimizer.clickhouse.replen_optimizer_results.services.db_repository import ReplenishmentOptimizerResultDataClickhouse


class ReplenishmentOutput:

    def __init__(self, *args, **kwargs) -> None:
        self.scenario = kwargs.get("scenario")

    def add_result_optimizer_data(self):

        result_data_frame = f'{BASE_DIR}/storage/replen_optimizer/result/result.csv'
        result_data_frame = pd.read_csv(result_data_frame)
        result_data_frame.columns = [
            'artcode_nm',
            'hub_nm',
            'region_nm',
            'transfer',
            'region_target',
            'region_stock',
            'priority',
        ]
        result_data_frame = result_data_frame.rename(columns={
            "artcode_nm": "art_code_nm",
        })

        result_data_frame['scenario_id'] = self.scenario.pk
        result_data_frame['id'] = [uuid.uuid4() for _ in range(len(result_data_frame.index))]

        with get_session() as session:
            data_creator = ReplenishmentOptimizerResultCreator(
                session=session,
                df=result_data_frame,
                result_id=self.scenario.id,
            )
            data_creator.execute()


    def calculate_result(self) -> pd.DataFrame:
        target_stock_hub = f'{BASE_DIR}/storage/replen_optimizer/inputs/in_target_hub.csv' # TODO: bump
        target_stock_hub = pd.read_csv(target_stock_hub)
        target_stock_hub.columns = [
            'hub_nm',
            'artcode_nm',
            'target_stock_hub',
            'min_target_stock',
        ]

        target_stock_reg = f'{BASE_DIR}/storage/replen_optimizer/inputs/in_target_reg.csv' # TODO: bump
        target_stock_reg = pd.read_csv(target_stock_reg)
        target_stock_reg.columns = [
            'region_nm',
            'artcode_nm',
            'target_stock_reg',
        ]


        with get_session() as session:
            replenishment_optimizer_result_data = ReplenishmentOptimizerResultDataClickhouse(
                session=session,
            )
            source_stock = replenishment_optimizer_result_data.get_source_stock(
                result_id=self.scenario.id,
            )
            destination_stock = replenishment_optimizer_result_data.get_destination_stock(
                result_id=self.scenario.id,
            )
            forecasts_hub = replenishment_optimizer_result_data.get_forecasts_hub(
                result_id=self.scenario.id,
            )
            forecasts_region = replenishment_optimizer_result_data.get_forecasts_region(
                result_id=self.scenario.id,
            )
            result_data_frame = replenishment_optimizer_result_data.get_result_data_optimizer(
                result_id=self.scenario.id,
            )
            calculated_data_frame = pd.DataFrame()
            calculated_data_frame['id'] = [uuid.uuid4() for _ in range(len(result_data_frame.index))]
            calculated_data_frame['scenario_id'] = self.scenario.pk
            calculated_data_frame['art_code_nm'] = result_data_frame['art_code_nm']
            calculated_data_frame['hub_nm'] = result_data_frame['hub_nm']
            calculated_data_frame['region_nm'] = result_data_frame['region_nm']

            # HUBS Forecast
            calculated_data_frame['source_stock'] = result_data_frame.apply(lambda row: FormulasCalculation._found_source_stock(row, source_stock), axis=1)
            calculated_data_frame['manual_correction'] = 0
            calculated_data_frame['calculated_transfer'] = result_data_frame["transfer"]
            calculated_data_frame['final_transfer'] = calculated_data_frame['calculated_transfer'] + calculated_data_frame['manual_correction']
            calculated_data_frame['fc_1_hub'] = result_data_frame.apply(lambda row: FormulasCalculation._found_forecasts_hub_sum_fc_1(row, forecasts_hub), axis=1)
            calculated_data_frame['cover_source'] = calculated_data_frame['source_stock'] / (calculated_data_frame['fc_1_hub'] * 30)
            calculated_data_frame['final_source_stock'] = calculated_data_frame['source_stock'] - result_data_frame.apply(lambda row: FormulasCalculation._found_sum_result_data_hub(row, result_data_frame), axis=1)
            calculated_data_frame['final_cover_source'] = calculated_data_frame['final_source_stock'] / (calculated_data_frame['fc_1_hub'] * 30)
            calculated_data_frame['target_cover_source'] = result_data_frame.apply(lambda row: FormulasCalculation._found_target_stock_hub(row, target_stock_hub), axis=1) / (calculated_data_frame['fc_1_hub'] * 30)

            # REGION Forecast
            calculated_data_frame['fc_1_region'] = result_data_frame.apply(lambda row: FormulasCalculation._found_forecasts_region_sum_fc_1(row, forecasts_region), axis=1)
            calculated_data_frame['destination_stock'] = result_data_frame.apply(lambda row: FormulasCalculation._found_destination_stock(row, destination_stock), axis=1)
            calculated_data_frame['final_destination_stock'] = calculated_data_frame['destination_stock'] + result_data_frame.apply(lambda row: FormulasCalculation._found_sum_result_data_region(row, result_data_frame), axis=1)
            calculated_data_frame['cover_destination'] = calculated_data_frame['destination_stock'] / (calculated_data_frame['fc_1_region'] * 30)
            calculated_data_frame['final_cover_destination'] = calculated_data_frame['final_destination_stock'] / (calculated_data_frame['fc_1_region'] * 30)
            calculated_data_frame['target_cover_destination'] = result_data_frame.apply(lambda row: FormulasCalculation._found_target_stock_reg(row, target_stock_reg), axis=1) / (calculated_data_frame['fc_1_region'] * 30)

            calculated_data_frame.fillna(0, inplace=True)
            calculated_data_frame = calculated_data_frame.drop('fc_1_hub', axis=1)
            calculated_data_frame = calculated_data_frame.drop('fc_1_region', axis=1)
            calculated_data_frame.replace([np.inf, -np.inf], 0, inplace=True)

            calculated_data_frame["cover_source"] = calculated_data_frame["cover_source"].astype(np.int32)
            calculated_data_frame["final_cover_source"] = calculated_data_frame["final_cover_source"].astype(np.int32)
            calculated_data_frame["target_cover_source"] = calculated_data_frame["target_cover_source"].astype(np.int32)
            calculated_data_frame["cover_destination"] = calculated_data_frame["cover_destination"].astype(np.int32)
            calculated_data_frame["final_cover_destination"] = calculated_data_frame["final_cover_destination"].astype(np.int32)
            calculated_data_frame["target_cover_destination"] = calculated_data_frame["target_cover_destination"].astype(np.int32)

            data_creator = ReplenishmentResultCreator(
                session=session,
                df=calculated_data_frame,
                result_id=self.scenario.id,
            )
            data_creator.execute()

    def get_queryset(self, query: dict) -> json:
        with get_session() as session:
            replenishment_optimizer_result_data = ReplenishmentOptimizerResultDataClickhouse(
                session=session,
            )
            result_data = replenishment_optimizer_result_data.get_data_aggregations(
                result_id=self.scenario.id,
                query_params=query,
            )

            return result_data.to_dict("records")

    def get_queryset_filter(self, query: dict) -> json:

        with get_session() as session:
            replenishment_optimizer_result_data = ReplenishmentOptimizerResultDataClickhouse(
                session=session,
            )
            result_data = replenishment_optimizer_result_data.get_queryset_filter_data(
                result_id=self.scenario.id,
                query_params=query,
            )
            return result_data.to_dict("records")