#!/usr/bin/env python3
"""Utility script to trigger the training workflow end-to-end for testing.

Steps performed:
1. Upload the CSV test dataset to MinIO using the configured `mc` alias.
2. Insert a new experiment_run record in Postgres and capture the generated ID.
3. Trigger the Temporal `train_model` workflow with the correct payload.

Prerequisites:
- `mc` CLI configured with alias defined in MINIO_ALIAS.
- PostgreSQL accessible with credentials in environment variables or defaults.
- Temporal server reachable without TLS on TEMPORAL_HOST / TEMPORAL_NAMESPACE.
- Python dependencies installed (see requirements.txt / requirements-dev.txt).
"""

from __future__ import annotations

import asyncio
import json
import os
import subprocess
import sys
import uuid
from datetime import datetime, timedelta
from pathlib import Path

from dotenv import load_dotenv
import psycopg2
from psycopg2.extras import Json
from temporalio import client


# Carrega variáveis de ambiente do arquivo .env na raiz do projeto
PROJECT_ROOT = Path(__file__).resolve().parent.parent
ENV_PATH = PROJECT_ROOT / '.env'
if ENV_PATH.exists():
    load_dotenv(dotenv_path=ENV_PATH)


DOCS_PATH = Path('docs/test-model-data.csv')
MINIO_ALIAS = 'suse'
MINIO_BUCKET = 'model-training'

POSTGRES_CONFIG = {
    'host': os.getenv('POSTGRES_HOST'),
    'port': os.getenv('POSTGRES_PORT'),
    'user': os.getenv('POSTGRES_USER'),
    'password': os.getenv('POSTGRES_PASSWORD'),
    'dbname': os.getenv('POSTGRES_DBNAME'),
}

TEMPORAL_HOST = os.getenv('TEMPORAL_HOST')
TEMPORAL_NAMESPACE = os.getenv('TEMPORAL_NAMESPACE')
TRAIN_TASK_QUEUE = os.getenv('TRAIN_TASK_QUEUE')
TEMPORAL_WORKFLOW = 'train_model'

BASE_REQUEST_DATA = {
    'experimentName': 'model-manager-test-01',
    'username': 'bruno.domingues@aignosi.com.br',
    'modelType': 'Linear Regression',
    'targetVariable': '03CV020/CORRENTE_N_M1_PV(Value)',
    'variableColumns': ['303-WIT-200(Value)'],
    'lagTrain': 0,
    'lagVal': 0,
    'remStaticWin': False,
    'lowLim': {},
    'uppLim': {},
    'window': 0,
    'useScaler': False,
    'includeAr': False,
    'trainSize': 80,
    'shuffle': True,
    'lineSeparator': ',',
    'decimalSeparator': '.',
    'removedIntervals': [],
}


def _ensure_source_file(path: Path) -> None:
    if not path.exists():
        raise FileNotFoundError(f'Test dataset not found at {path.resolve()}')


def upload_to_minio(source_path: Path) -> str:
    """Upload the CSV to MinIO using the mc CLI and return the object name."""
    _ensure_source_file(source_path)
    timestamp = datetime.utcnow().strftime('%Y%m%d-%H%M%S')
    object_name = f'test-model-data-{timestamp}.csv'
    target_uri = f'{MINIO_ALIAS}/{MINIO_BUCKET}/{object_name}'

    subprocess.run(  # noqa: S603
        ['mc', 'cp', str(source_path), target_uri],  # noqa: S607
        check=True,
    )
    return object_name


def insert_experiment_run(file_name: str, request_data: dict) -> int:
    """Insert experiment_run record and return the generated ID."""
    now = datetime.utcnow()
    payload = {
        **request_data,
        'fileName': file_name,
        'bucketName': MINIO_BUCKET,
    }

    insert_sql = """
        INSERT INTO experiment_run (
            experiment_name,
            username,
            status,
            created_at,
            updated_at,
            bucket_name,
            file_name,
            request_data
        )
        VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
        RETURNING id;
    """

    with psycopg2.connect(**POSTGRES_CONFIG) as conn:
        with conn.cursor() as cur:
            cur.execute(
                insert_sql,
                (
                    request_data['experimentName'],
                    request_data['username'],
                    'ORCHESTRATOR_REQUEST_SENT',
                    now,
                    now,
                    MINIO_BUCKET,
                    file_name,
                    Json(payload),
                ),
            )
            experiment_run_id = cur.fetchone()[0]

    return experiment_run_id


def build_workflow_payload(
    experiment_run_id: int,
    file_name: str,
    request_data: dict,
) -> dict:
    """Convert camelCase request data to snake_case and enrich with runtime values."""
    return {
        'experiment_run_id': experiment_run_id,
        'experiment_name': request_data['experimentName'],
        'username': request_data['username'],
        'model_type': request_data['modelType'],
        'target_variable': request_data['targetVariable'],
        'variable_columns': request_data['variableColumns'],
        'lag_train': request_data['lagTrain'],
        'lag_val': request_data['lagVal'],
        'rem_static_win': request_data['remStaticWin'],
        'low_lim': request_data['lowLim'],
        'upp_lim': request_data['uppLim'],
        'window': request_data['window'],
        'use_scaler': request_data['useScaler'],
        'include_ar': request_data['includeAr'],
        'train_size': request_data['trainSize'],
        'shuffle': request_data['shuffle'],
        'bucket_name': MINIO_BUCKET,
        'file_name': file_name,
        'line_separator': request_data['lineSeparator'],
        'decimal_separator': request_data['decimalSeparator'],
        'removed_intervals': request_data['removedIntervals'],
    }


async def trigger_temporal_workflow(workflow_input: dict) -> str:
    """Connect to Temporal and trigger the training workflow."""
    temporal_client = await client.Client.connect(
        target_host=TEMPORAL_HOST,
        namespace=TEMPORAL_NAMESPACE,
        tls=os.getenv('TEMPORAL_USE_TLS', False),
    )

    workflow_id = f'train-model-test-{uuid.uuid4()}'
    await temporal_client.execute_workflow(
        TEMPORAL_WORKFLOW,
        workflow_input,
        id=workflow_id,
        task_queue=TRAIN_TASK_QUEUE,
        execution_timeout=timedelta(minutes=5),
        run_timeout=timedelta(minutes=5),
        task_timeout=timedelta(minutes=5),
    )
    return workflow_id


def main() -> None:
    try:
        uploaded_file_name = upload_to_minio(DOCS_PATH)
    except subprocess.CalledProcessError as exc:
        print(f'Failed to upload file to MinIO: {exc}', file=sys.stderr)
        sys.exit(1)
    except FileNotFoundError as exc:
        print(str(exc), file=sys.stderr)
        sys.exit(1)

    experiment_request = BASE_REQUEST_DATA.copy()

    try:
        experiment_run_id = insert_experiment_run(uploaded_file_name, experiment_request)
    except psycopg2.Error as exc:
        print(f'Database error while inserting experiment_run: {exc}', file=sys.stderr)
        sys.exit(1)

    workflow_payload = build_workflow_payload(
        experiment_run_id=experiment_run_id,
        file_name=uploaded_file_name,
        request_data=experiment_request,
    )

    try:
        workflow_id = asyncio.run(trigger_temporal_workflow(workflow_payload))
    except Exception as exc:  # noqa: BLE001
        print(f'Failed to start Temporal workflow: {exc}', file=sys.stderr)
        sys.exit(1)

    print(
        json.dumps(
            {
                'experiment_run_id': experiment_run_id,
                's3_object_name': uploaded_file_name,
                'workflow_id': workflow_id,
            },
            indent=2,
        )
    )


if __name__ == '__main__':
    main()