"""collection of configured data pipelines, intended to be invoked from C#"""

import time
from datetime import datetime as Datetime
from typing import Final

import sqlalchemy as sql

from delta_barth import databases as db
from delta_barth.analysis import forecast
from delta_barth.constants import DEFAULT_DB_ERR_CODE
from delta_barth.errors import STATUS_HANDLER, wrap_result
from delta_barth.logging import logger_pipelines as logger
from delta_barth.management import SESSION
from delta_barth.types import JsonExportResponse, PipelineMetrics


def _write_performance_metrics(
    pipeline_name: str,
    time_start: int,
    time_end: int,
) -> PipelineMetrics:
    if time_end < time_start:
        raise ValueError("Ending time smaller than starting time")
    execution_duration = (time_end - time_start) / 1e9
    metrics = PipelineMetrics(
        pipeline_name=pipeline_name,
        execution_duration=execution_duration,
    )

    with SESSION.db_engine.begin() as con:
        con.execute(sql.insert(db.perf_meas).values(**metrics))

    return metrics


@wrap_result(code_on_error=DEFAULT_DB_ERR_CODE)
def _write_performance_metrics_wrapped(
    pipeline_name: str,
    time_start: int,
    time_end: int,
) -> PipelineMetrics:
    return _write_performance_metrics(pipeline_name, time_start, time_end)


def pipeline_sales_forecast(
    company_id: int | None,
    start_date: Datetime | None,
) -> JsonExportResponse:
    PIPELINE_NAME: Final[str] = "sales_forecast"
    logger.info("[EXT-CALL PIPELINES] Starting main sales forecast pipeline...")
    t_start = time.perf_counter_ns()
    result = forecast.pipeline_sales_forecast(
        SESSION, company_id=company_id, start_date=start_date
    )
    export = JsonExportResponse(result.model_dump_json())
    t_end = time.perf_counter_ns()
    logger.info("[EXT-CALL PIPELINES] Main sales forecast pipeline successful")
    logger.info("[EXT-CALL PIPELINES] Writing performance metrics...")
    res = _write_performance_metrics_wrapped(
        pipeline_name=PIPELINE_NAME,
        time_start=t_start,
        time_end=t_end,
    )
    if res.status != STATUS_HANDLER.SUCCESS:
        logger.error(
            (
                "[DB-WRITE][METRICS] Pipeline: >%s< - Error on writing "
                "pipeline metrics to database: %s"
            ),
            PIPELINE_NAME,
            res.status,
        )
    else:
        metrics = res.unwrap()
        logger.info(
            "[METRICS] Pipeline: >%s< - Execution time: %.6f",
            PIPELINE_NAME,
            metrics["execution_duration"],
        )

    return export


def pipeline_sales_forecast_dummy(
    company_id: int | None,
    start_date: Datetime | None,
) -> JsonExportResponse:
    PIPELINE_NAME: Final[str] = "sales_forecast_dummy"
    logger.info("[EXT-CALL PIPELINES] Starting dummy sales forecast pipeline...")
    t_start = time.perf_counter_ns()
    result = forecast.pipeline_sales_dummy(
        SESSION,
        company_id=company_id,
        start_date=start_date,
    )
    export = JsonExportResponse(result.model_dump_json())
    t_end = time.perf_counter_ns()
    logger.info("[EXT-CALL PIPELINES] Dummy sales forecast pipeline successful")
    logger.info("[EXT-CALL PIPELINES] Writing performance metrics...")
    res = _write_performance_metrics_wrapped(
        pipeline_name=PIPELINE_NAME,
        time_start=t_start,
        time_end=t_end,
    )
    if res.status != STATUS_HANDLER.SUCCESS:
        logger.error(
            (
                "[DB-WRITE][METRICS] Pipeline: >%s< - Error on writing "
                "pipeline metrics to database: %s"
            ),
            PIPELINE_NAME,
            res.status,
        )
    else:
        metrics = res.unwrap()
        logger.info(
            "[METRICS] Pipeline: >%s< - Execution time: %.6f",
            PIPELINE_NAME,
            metrics["execution_duration"],
        )

    return export