# hf_backend.py
import time, logging, os, contextlib
from typing import Any, Dict, AsyncIterable, List

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from backends_base import ChatBackend, ImagesBackend
from config import settings

try:
    import spaces
except ImportError:
    spaces = None

logger = logging.getLogger(__name__)

# --- Load model/tokenizer on CPU at import time (ZeroGPU safe) ---
MODEL_ID = settings.LlmHFModelID or "Qwen/Qwen2.5-1.5B-Instruct"
logger.info(f"Loading {MODEL_ID} on CPU at startup (ZeroGPU safe)...")

tokenizer = None
model = None
load_error = None
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True, use_fast=False)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.float32,  # CPU-safe default
        trust_remote_code=True,
    )
    model.eval()
except Exception as e:
    load_error = f"Failed to load model/tokenizer: {e}"
    logger.exception(load_error)


# --- Device helpers ---
def pick_device() -> str:
    forced = os.getenv("FORCE_DEVICE", "").lower().strip()
    if forced in {"cpu", "cuda", "mps"}:
        return forced
    if torch.cuda.is_available():
        return "cuda"
    if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
        return "mps"
    return "cpu"

def pick_dtype(device: str) -> torch.dtype:
    if device == "cuda":
        major, _ = torch.cuda.get_device_capability()
        return torch.bfloat16 if major >= 8 else torch.float16
    if device == "mps":
        return torch.float16
    return torch.float32


# --- Backend class ---
class HFChatBackend(ChatBackend):
    async def stream(self, request: Dict[str, Any]) -> AsyncIterable[Dict[str, Any]]:
        if load_error:
            raise RuntimeError(load_error)

        messages = request.get("messages", [])
        prompt = messages[-1]["content"] if messages else "(empty)"
        temperature = float(request.get("temperature", settings.LlmTemp or 0.7))
        max_tokens = int(request.get("max_tokens", settings.LlmOpenAICtxSize or 512))

        rid = f"chatcmpl-hf-{int(time.time())}"
        now = int(time.time())

        if spaces:
            @spaces.GPU(duration=120)  # allow longer run
            def run_once(prompt: str) -> str:
                device = pick_device()
                dtype = pick_dtype(device)

                # Move model to GPU if needed
                model.to(device=device, dtype=dtype).eval()

                inputs = tokenizer(prompt, return_tensors="pt").to(device)
                with torch.inference_mode(), torch.autocast(device_type=device, dtype=dtype):
                    outputs = model.generate(
                        **inputs,
                        max_new_tokens=max_tokens,
                        temperature=temperature,
                        do_sample=True,
                    )
                return tokenizer.decode(outputs[0], skip_special_tokens=True)
        else:
            def run_once(prompt: str) -> str:
                inputs = tokenizer(prompt, return_tensors="pt")
                with torch.inference_mode():
                    outputs = model.generate(
                        **inputs,
                        max_new_tokens=max_tokens,
                        temperature=temperature,
                        do_sample=True,
                    )
                return tokenizer.decode(outputs[0], skip_special_tokens=True)

        try:
            text = run_once(prompt)
            yield {
                "id": rid,
                "object": "chat.completion.chunk",
                "created": now,
                "model": MODEL_ID,
                "choices": [
                    {"index": 0, "delta": {"content": text}, "finish_reason": "stop"}
                ],
            }
        except Exception:
            logger.exception("HF inference failed")
            raise


class StubImagesBackend(ImagesBackend):
    async def generate_b64(self, request: Dict[str, Any]) -> str:
        logger.warning("Image generation not supported in HF backend.")
        return (
            "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR4nGP4BwQACfsD/etCJH0AAAAASUVORK5CYII="
        )