"""
Airflow DAG: bench_collect

Runs the tip-generation benchmark (model × prompt evaluation). Triggered
on-demand from the admin UI or manually, collects candidates per cell,
exports for Claude Code judgment, and generates a leaderboard.

Mirrors the manual flow:

  1. collect.py  → generates candidates, logs to MLflow with judge_pending=true
  2. (human: judge_cli.py --export, Claude Code scores, judge_cli.py --apply)
  3. compare.py  → leaderboard

For now, steps 2 is manual. Future: add a webhook to trigger the human
judge from the admin UI or set up an async task queue.

Required conf keys (passed via dag_run.conf):
  models           str   — comma-separated model tags (e.g. "qwen2.5:0.5b,qwen2.5:1.5b")
  prompts          str   — comma-separated prompt versions (default: "v1,v2-mentor,v3-few-shot")
  n_tips           int   — candidates to generate per scenario (default: 5)
  n_scenarios      int   — cap scenario count; 0 = all (default: 0)
  temperature      float — LLM generation temperature (default: 0.7)
  experiment       str   — MLflow experiment name (default: "tip-bench-auto")
  max_model_b      float — reject models larger than this (default: 4.0)
  ollama_url       str   — Ollama endpoint (default: http://localhost:11434)
  mlflow_url       str   — MLflow tracking URI (env MLFLOW_TRACKING_URI or http://localhost:5000)
"""

from __future__ import annotations

import json
import os
import sys
from datetime import datetime, timedelta
from pathlib import Path

from airflow import DAG
from airflow.operators.python import PythonOperator


def _collect(**context: object) -> dict:
    """Run collect.py with the provided config."""
    conf: dict = context["dag_run"].conf or {}

    models = str(conf.get("models", "qwen2.5:0.5b,qwen2.5:1.5b,gemma3:1b,llama3.2:3b"))
    prompts = str(conf.get("prompts", "v1,v2-mentor,v3-few-shot"))
    n_tips = int(conf.get("n_tips", 5))
    n_scenarios = int(conf.get("n_scenarios", 0))
    temperature = float(conf.get("temperature", 0.7))
    experiment = str(conf.get("experiment", "tip-bench-auto"))
    max_model_b = float(conf.get("max_model_b", 4.0))
    ollama_url = str(conf.get("ollama_url", os.environ.get("OLLAMA_URL", "http://localhost:11434")))
    mlflow_url = str(conf.get("mlflow_url", os.environ.get("MLFLOW_TRACKING_URI", "http://localhost:5000")))

    sys.path.insert(0, "/opt/airflow/ml/experiments/bench")
    from collect import main as collect_main  # type: ignore

    # Build args for collect.py
    args = [
        "--models", models,
        "--prompts", prompts,
        "--experiment", experiment,
        "--n-tips", str(n_tips),
        "--temperature", str(temperature),
        "--max-model-b", str(max_model_b),
        "--ollama-url", ollama_url,
        "--mlflow-url", mlflow_url,
    ]
    if n_scenarios > 0:
        args.extend(["--n-scenarios", str(n_scenarios)])

    # Inject args into sys.argv so argparse picks them up
    old_argv = sys.argv
    try:
        sys.argv = ["collect.py"] + args
        result = collect_main()
        return {
            "status": "success" if result == 0 else "failed",
            "exit_code": result,
            "experiment": experiment,
        }
    finally:
        sys.argv = old_argv


def _compare(**context: object) -> dict:
    """Run compare.py to generate the leaderboard."""
    conf: dict = context["dag_run"].conf or {}
    experiment = str(conf.get("experiment", "tip-bench-auto"))
    mlflow_url = str(conf.get("mlflow_url", os.environ.get("MLFLOW_TRACKING_URI", "http://localhost:5000")))

    sys.path.insert(0, "/opt/airflow/ml/experiments/bench")
    from compare import main as compare_main  # type: ignore

    old_argv = sys.argv
    try:
        sys.argv = [
            "compare.py",
            "--experiment", experiment,
            "--mlflow-url", mlflow_url,
        ]
        result = compare_main()
        return {
            "status": "success" if result == 0 else "failed",
            "exit_code": result,
            "experiment": experiment,
        }
    finally:
        sys.argv = old_argv


def _export_for_judge(**context: object) -> str:
    """Export pending runs for Claude Code judgment."""
    conf: dict = context["dag_run"].conf or {}
    experiment = str(conf.get("experiment", "tip-bench-auto"))
    mlflow_url = str(conf.get("mlflow_url", os.environ.get("MLFLOW_TRACKING_URI", "http://localhost:5000")))

    export_path = f"/tmp/oo-bench-{experiment}-{int(context['ti'].start_date.timestamp())}.json"

    sys.path.insert(0, "/opt/airflow/ml/experiments/bench")
    from judge_cli import export  # type: ignore
    from mlflow_client import MLflowClient  # type: ignore

    client = MLflowClient(
        tracking_uri=mlflow_url,
        username=os.environ.get("MLFLOW_TRACKING_USERNAME") or "admin",
        password=os.environ.get("MLFLOW_TRACKING_PASSWORD") or "password",
    )
    result = export(client, experiment, export_path)

    # XCom: push path so next task can find it
    context["ti"].xcom_push(key="export_path", value=export_path)

    return export_path


with DAG(
    dag_id="bench_collect",
    description="Tip-generation benchmark: model & prompt evaluation via MLflow",
    schedule_interval=None,
    start_date=datetime(2025, 1, 1),
    catchup=False,
    tags=["bench", "ml", "evaluation"],
    default_args={
        "retries": 1,
        "retry_delay": timedelta(minutes=5),
    },
) as dag:

    collect = PythonOperator(
        task_id="collect",
        python_callable=_collect,
        provide_context=True,
    )

    export_judge = PythonOperator(
        task_id="export_for_judge",
        python_callable=_export_for_judge,
        provide_context=True,
    )

    compare = PythonOperator(
        task_id="compare",
        python_callable=_compare,
        provide_context=True,
    )

    collect >> export_judge >> compare