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

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

logger = logging.getLogger(__name__)

try:
    import spaces
    from spaces.zero import client as zero_client
except ImportError:
    spaces, zero_client = None, None

# --- Model setup ---
MODEL_ID = settings.LlmHFModelID or "Qwen/Qwen2.5-1.5B-Instruct"
logger.info(f"Preloading tokenizer for {MODEL_ID} on CPU...")

tokenizer, load_error = None, None
try:
    tokenizer = AutoTokenizer.from_pretrained(
        MODEL_ID,
        trust_remote_code=True,
        use_fast=False,
    )
except Exception as e:
    load_error = f"Failed to load tokenizer: {e}"
    logger.exception(load_error)


# ---------------- helpers ----------------
def _pick_cpu_dtype() -> torch.dtype:
    if hasattr(torch, "cpu") and hasattr(torch.cpu, "is_bf16_supported"):
        try:
            if torch.cpu.is_bf16_supported():
                logger.info("CPU BF16 supported, will attempt torch.bfloat16")
                return torch.bfloat16
        except Exception:
            pass
    logger.info("Falling back to torch.float32 on CPU")
    return torch.float32


# ---------------- global cache ----------------
_MODEL_CACHE: dict[tuple[str, torch.dtype], AutoModelForCausalLM] = {}


def _get_model(device: str, dtype: torch.dtype):
    key = (device, dtype)
    if key in _MODEL_CACHE:
        return _MODEL_CACHE[key]

    cfg = AutoConfig.from_pretrained(MODEL_ID, trust_remote_code=True)
    if hasattr(cfg, "quantization_config"):
        logger.warning("Removing quantization_config from model config")
        delattr(cfg, "quantization_config")

    try:
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_ID,
            config=cfg,
            torch_dtype=dtype,
            trust_remote_code=True,
            device_map="auto" if device != "cpu" else {"": "cpu"},
        )
    except Exception as e:
        if device == "cpu" and dtype == torch.bfloat16:
            logger.warning(f"BF16 load failed on CPU: {e}. Retrying with FP32.")
            model = AutoModelForCausalLM.from_pretrained(
                MODEL_ID,
                config=cfg,
                torch_dtype=torch.float32,
                trust_remote_code=True,
                device_map={"": "cpu"},
            )
            dtype = torch.float32
        else:
            raise

    model.eval()
    _MODEL_CACHE[(device, dtype)] = model
    return model


# ---------------- Chat Backend ----------------
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", [])
        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())

        # --- Inject X-IP-Token into global headers if ZeroGPU is used ---
        x_ip_token = request.get("x_ip_token")
        if x_ip_token and zero_client:
            zero_client.HEADERS["X-IP-Token"] = x_ip_token
            logger.debug("Injected X-IP-Token into ZeroGPU headers")

        # Build prompt using chat template if available
        if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template:
            try:
                prompt = tokenizer.apply_chat_template(
                    messages,
                    tokenize=False,
                    add_generation_prompt=True,
                )
                logger.debug("Applied chat template for prompt")
            except Exception as e:
                logger.warning(f"Failed to apply chat template: {e}, using fallback")
                prompt = messages[-1]["content"] if messages else "(empty)"
        else:
            prompt = messages[-1]["content"] if messages else "(empty)"

        def _run_once(prompt: str, device: str, dtype: torch.dtype) -> str:
            model = _get_model(device, dtype)
            inputs = tokenizer(prompt, return_tensors="pt").to(device)

            with torch.inference_mode():
                if device != "cpu":
                    autocast_ctx = torch.autocast(device_type=device, dtype=dtype)
                else:
                    autocast_ctx = torch.cpu.amp.autocast(dtype=dtype)

                with autocast_ctx:
                    outputs = model.generate(
                        **inputs,
                        max_new_tokens=max_tokens,
                        temperature=temperature,
                        do_sample=True,
                    )

            return tokenizer.decode(outputs[0], skip_special_tokens=True)

        if spaces:
            # --- GPU path with ZeroGPU ---
            @spaces.GPU(duration=120)
            def run_once(prompt: str) -> str:
                return _run_once(prompt, device="cuda", dtype=torch.float16)

            text = run_once(prompt)
        else:
            # --- CPU-only fallback with auto dtype detection ---
            dtype = _pick_cpu_dtype()
            text = _run_once(prompt, device="cpu", dtype=dtype)

        yield {
            "id": rid,
            "object": "chat.completion.chunk",
            "created": now,
            "model": MODEL_ID,
            "choices": [
                {"index": 0, "delta": {"content": text}, "finish_reason": "stop"}
            ],
        }


# ---------------- Stub Images Backend ----------------
class StubImagesBackend(ImagesBackend):
    """
    Stub backend for images since HFChatBackend is text-only.
    Returns a transparent 1x1 PNG placeholder.
    """
    async def generate_b64(self, request: Dict[str, Any]) -> str:
        logger.warning("Image generation not supported in HF backend.")
        return (
            "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR4nGP4BwQACfsD/etCJH0AAAAASUVORK5CYII="
        )