"""远程评估入口 — 在算力节点的训练容器里执行。"""
import json
import os
from pathlib import Path

# 禁用 FlashAttention，启用 MPS
os.environ["PYTORCH_NO_FLASH"] = "1"
os.environ["MACA_MPS_MODE"] = "1"
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"

_DATA_DIR = Path(os.environ.get("COMPUTE_NODE_REMOTE_DATA_DIR", "/root/Fine-tuning/backend/data"))
_ADAPTERS_DIR = _DATA_DIR / "adapters"


async def run_remote_eval(job_id: str) -> dict:
    """在远程容器里加载 adapter，计算 perplexity。"""
    adapter_path = _ADAPTERS_DIR / job_id
    if not adapter_path.exists():
        return {"error": f"Adapter not found: {adapter_path}"}

    import torch
    from transformers import AutoModelForCausalLM, AutoTokenizer

    # 加载 adapter（CUDA_VISIBLE_DEVICES=2,3 已将物理 GPU 2,3 映射为逻辑 GPU 0,1）
    device_map = {"": 0}

    model = AutoModelForCausalLM.from_pretrained(
        adapter_path, torch_dtype=torch.float16, device_map=device_map
    )
    tokenizer = AutoTokenizer.from_pretrained(adapter_path, trust_remote_code=True)
    model.eval()

    sample_texts = [
        "The quick brown fox jumps over the lazy dog.",
        "Hello, how are you doing today?",
    ]

    losses = []
    with torch.no_grad():
        for text in sample_texts:
            inputs = tokenizer(text, return_tensors="pt").to(model.device)
            outputs = model(**inputs, labels=inputs["input_ids"])
            losses.append(outputs.loss.item())

    avg_loss = sum(losses) / len(losses) if losses else 0
    perplexity = torch.exp(torch.tensor(avg_loss)).item() if avg_loss > 0 else 0

    return {
        "metrics": {
            "eval_loss": round(avg_loss, 4),
            "perplexity": round(perplexity, 2),
            "num_samples": len(sample_texts),
        }
    }