#!/usr/bin/env python3
"""
Test checkpoint + replay flow end-to-end.

1. Runs the pipeline with checkpointing enabled on a test video
2. Lists available checkpoints
3. Replays from run_ocr with different config
4. Compares detection counts

Usage:
    MPR_CHECKPOINT=1 INFERENCE_URL=http://mcrndeb:8000 python tests/detect/manual/test_replay.py [--job JOB_ID]

Requires: inference server running, MinIO/S3 running, test video available
"""

import argparse
import logging
import os
import sys
from pathlib import Path

# Load ctrl/.env
env_file = Path(__file__).resolve().parents[3] / "ctrl" / ".env"
if env_file.exists():
    for line in env_file.read_text().splitlines():
        line = line.strip()
        if line and not line.startswith("#") and "=" in line:
            key, _, value = line.partition("=")
            os.environ.setdefault(key.strip(), value.strip())

sys.path.insert(0, ".")

logging.basicConfig(level=logging.INFO, format="%(levelname)-7s %(name)s — %(message)s")
logger = logging.getLogger(__name__)

# Force checkpointing on
os.environ["MPR_CHECKPOINT"] = "1"


def main():
    parser = argparse.ArgumentParser()
    import time
    default_job = f"replay-{int(time.time()) % 100000}"
    parser.add_argument("--job", default=default_job)
    parser.add_argument("--port", type=int, default=6382)
    args = parser.parse_args()

    # Override Redis to localhost (ctrl/.env has k8s hostname)
    os.environ["REDIS_URL"] = f"redis://localhost:{args.port}/0"

    from core.detect.graph import get_pipeline, NODES
    from core.detect.checkpoint import list_checkpoints
    from core.detect.checkpoint import replay_from
    from core.detect.state import DetectState

    VIDEO = "media/out/chunks/95043d50-4df6-4ac8-bbd5-2ba873117c6e/chunk_0000.mp4"

    logger.info("Job: %s", args.job)
    logger.info("Checkpoint: enabled")
    logger.info("Video: %s", VIDEO)
    logger.info("Open: http://mpr.local.ar/detection/?job=%s", args.job)
    input("\nPress Enter to run initial pipeline...")

    # --- Initial run ---
    pipeline = get_pipeline(checkpoint=True)
    initial_state = DetectState(
        video_path=VIDEO,
        job_id=args.job,
        profile_name="soccer_broadcast",
    )

    logger.info("Running initial pipeline...")
    result = pipeline.invoke(initial_state)

    detections = result.get("detections", [])
    report = result.get("report")
    logger.info("Initial run: %d detections, %d brands",
                len(detections), len(report.brands) if report else 0)

    # --- List checkpoints ---
    stages = list_checkpoints(args.job)
    logger.info("Available checkpoints: %s", stages)

    if "detect_objects" not in stages:
        logger.error("Expected checkpoint for detect_objects — aborting replay test")
        return

    input("\nPress Enter to replay from run_ocr with different config...")

    # --- Replay with different OCR config ---
    overrides = {"ocr": {"min_confidence": 0.3, "languages": ["en", "es"]}}
    logger.info("Replaying from run_ocr with overrides: %s", overrides)

    replay_result = replay_from(
        job_id=args.job,
        start_stage="run_ocr",
        config_overrides=overrides,
    )

    replay_detections = replay_result.get("detections", [])
    replay_report = replay_result.get("report")
    logger.info("Replay run: %d detections, %d brands",
                len(replay_detections),
                len(replay_report.brands) if replay_report else 0)

    # --- Compare ---
    logger.info("--- Comparison ---")
    logger.info("Initial: %d detections", len(detections))
    logger.info("Replay:  %d detections (min_confidence 0.5 → 0.3)", len(replay_detections))

    diff = len(replay_detections) - len(detections)
    if diff > 0:
        logger.info("Replay found %d more detections with lower threshold", diff)
    elif diff == 0:
        logger.info("Same count — threshold change didn't affect this video")
    else:
        logger.warning("Replay found fewer detections — unexpected")

    logger.info("Done.")


if __name__ == "__main__":
    main()