"""Backend detection — device + dtype + ZeroGPU awareness.

Refer to RESEARCH.md §7 for the per-backend loading recipe rationale.
"""
from __future__ import annotations

import os
from dataclasses import dataclass
from typing import Literal

import torch

Device = Literal["cuda", "mps", "cpu"]


@dataclass(frozen=True)
class Backend:
    device: Device
    dtype: torch.dtype
    vae_dtype: torch.dtype
    is_zerogpu: bool
    zerogpu_size: Literal["large", "xlarge"] | None  # None on MPS/CPU
    supports_quant: bool          # hardware CAN do FP8 (CUDA) — capability, not policy
    supports_aoti: bool           # spaces.aoti_*
    supports_flash_attn_3: bool
    quant: str | None = None      # ACTIVE quantization: None = bf16 (default everywhere
                                  # today). Device-aware seam — only ever set on CUDA;
                                  # MUST stay None on MPS (Metal has no FP8).

    @property
    def label(self) -> str:
        if self.is_zerogpu:
            return f"ZeroGPU ({self.zerogpu_size})"
        if self.device == "mps":
            return "MPS (Apple Silicon)"
        if self.device == "cuda":
            return "CUDA (self-hosted)"
        return "CPU"


def detect() -> Backend:
    is_zerogpu = os.getenv("SPACES_ZERO_GPU") is not None

    if torch.cuda.is_available():
        device: Device = "cuda"
        dtype = torch.bfloat16
        vae_dtype = torch.float32
        zerogpu_size = (
            "xlarge" if os.getenv("WAN_STUDIO_TIER", "large") == "xlarge" else "large"
        ) if is_zerogpu else None
        return Backend(
            device=device,
            dtype=dtype,
            vae_dtype=vae_dtype,
            is_zerogpu=is_zerogpu,
            zerogpu_size=zerogpu_size,
            supports_quant=True,
            supports_aoti=is_zerogpu,
            supports_flash_attn_3=True,
            # bf16 by default (decision: bf16 everywhere, MoE on xlarge). The FP8
            # seam is CUDA-only and opt-in via WAN_STUDIO_QUANT=fp8 — shelved today.
            quant=(os.getenv("WAN_STUDIO_QUANT") or None),
        )

    if torch.backends.mps.is_available():
        return Backend(
            device="mps",
            dtype=torch.bfloat16,      # MPS runs full bf16 (M-series, 128 GB unified)
            vae_dtype=torch.float32,
            is_zerogpu=False,
            zerogpu_size=None,
            supports_quant=False,      # Metal has no FP8 — quant must NEVER apply here
            supports_aoti=False,
            supports_flash_attn_3=False,
            quant=None,                # explicit: MPS is always bf16, never quantized
        )

    return Backend(
        device="cpu",
        dtype=torch.float32,
        vae_dtype=torch.float32,
        is_zerogpu=False,
        zerogpu_size=None,
        supports_quant=False,
        supports_aoti=False,
        supports_flash_attn_3=False,
    )


def spaces_gpu_or_noop():
    """Returns the `spaces.GPU` decorator if running on ZeroGPU, otherwise a no-op.

    `import spaces` is safe outside ZeroGPU (the decorator is effect-free), but this
    helper keeps the decorator-call site terse and avoids the `spaces` dependency
    erroring on environments where it isn't installed.
    """
    try:
        import spaces  # type: ignore
        return spaces.GPU
    except ImportError:
        def _noop(*_args, **_kwargs):
            def deco(fn):
                return fn
            return deco
        return _noop


def apply_quantization(model, backend: "Backend") -> None:
    """Device-aware quantization seam.

    No-op today — we run bf16 everywhere (MoE fits xlarge in bf16, MPS requires
    bf16). The seam exists so CUDA can later opt into FP8 WITHOUT touching the
    MPS path: it only acts when `backend.quant` is set AND the device is CUDA, so
    MPS (and CPU) always pass through untouched. Flip on via WAN_STUDIO_QUANT=fp8.
    """
    if backend.quant is None or backend.device != "cuda":
        return
    if backend.quant == "fp8":
        from torchao.quantization import (  # noqa: PLC0415
            Float8DynamicActivationFloat8WeightConfig,
            quantize_,
        )
        quantize_(model, Float8DynamicActivationFloat8WeightConfig())