"""Vanilla scoring pipeline for a vLLM/OpenAI-compatible backend.

Each video uses one model call per scoring dimension; strict per-dim
JSON outputs are aggregated into the final ``scoring`` block. This entry
talks to an OpenAI-compatible ``/v1/chat/completions`` endpoint.
"""
from __future__ import annotations

import argparse
import os

from _core import (
    DEFAULT_DATA_PATH,
    DEFAULT_RESULTS_DIR,
    run_scoring,
)
from tools import VLLMClient, load_pointwise_data

# ---------------------------------------------------------------------------
# Defaults
# ---------------------------------------------------------------------------

DEFAULT_TAG = "infer"
DEFAULT_SCORE_OUTPUT = os.path.join(
    DEFAULT_RESULTS_DIR, f"{DEFAULT_TAG}_scores.json"
)


# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------

def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description=(
            "Vanilla video reward scoring pipeline (per-dimension, "
            "3 dims = 3 calls/video) — local vLLM OpenAI-compatible "
            "backend."
        )
    )
    parser.add_argument("--data", type=str, default=DEFAULT_DATA_PATH)
    parser.add_argument("--score_output", type=str, default=DEFAULT_SCORE_OUTPUT)
    parser.add_argument(
        "--num_samples",
        type=str,
        default="all",
        help="Number of input videos, or 'all'",
    )
    parser.add_argument(
        "--concurrency",
        type=int,
        default=32,
        help="Max concurrent worker threads (videos in flight). Each "
        "video issues one model call per scoring dimension; the per-dim "
        "calls run sequentially within a video.",
    )

    # vLLM connection / generation params
    parser.add_argument(
        "--vllm_base_url",
        type=str,
        default=os.environ.get("VLLM_BASE_URL", "http://127.0.0.1:8000/v1"),
        help="vLLM OpenAI base URL, e.g. http://127.0.0.1:8000/v1",
    )
    parser.add_argument(
        "--model",
        type=str,
        default=os.environ.get("VLLM_MODEL", "Qwen3-VL-8B-Instruct"),
        help="Served model name for vLLM backend",
    )
    parser.add_argument(
        "--api_key",
        type=str,
        default=os.environ.get("VLLM_API_KEY", "EMPTY"),
        help="OpenAI-compatible API key; vLLM usually accepts EMPTY",
    )
    parser.add_argument("--max_tokens", type=int, default=2048)
    parser.add_argument("--temperature", type=float, default=0.0)
    parser.add_argument("--top_p", type=float, default=None)
    parser.add_argument(
        "--request_interval",
        type=float,
        default=0.0,
        help="Sleep seconds between successive videos on the same worker.",
    )
    parser.add_argument("--max_retries", type=int, default=3)
    parser.add_argument("--retry_base_delay", type=float, default=2.0)
    parser.add_argument("--request_timeout", type=int, default=300)
    return parser.parse_args()


def main() -> None:
    args = parse_args()

    print(f"[vllm] base_url: {args.vllm_base_url}")
    print(f"[vllm] model:    {args.model}")
    print(
        f"[vllm] max_tokens={args.max_tokens}, "
        f"temperature={args.temperature}, top_p={args.top_p}"
    )

    _, expanded_data = load_pointwise_data(
        data_path=args.data,
        num_samples=args.num_samples,
    )

    client = VLLMClient(
        base_url=args.vllm_base_url,
        model_name=args.model,
        api_key=args.api_key,
        max_tokens=args.max_tokens,
        temperature=args.temperature,
        top_p=args.top_p,
        request_interval=args.request_interval,
        max_retries=args.max_retries,
        retry_base_delay=args.retry_base_delay,
        request_timeout=args.request_timeout,
    )

    run_scoring(
        client=client,
        expanded_data=expanded_data,
        score_path=args.score_output,
        concurrency=args.concurrency,
    )

    print("\n" + "=" * 72)
    print("DONE")
    print(f"Scores:  {args.score_output}")
    print("=" * 72)


if __name__ == "__main__":
    main()