"""Weight loader for Z-Image-Turbo MLX backend.

Loads safetensors weights from HuggingFace cache and maps them
to the MLX module hierarchy.
"""

from __future__ import annotations

import glob
import logging
from pathlib import Path

import mlx.core as mx

logger = logging.getLogger("zimage-mlx")

# Default HF cache path for Z-Image-Turbo
_DEFAULT_MODEL_ID = "Tongyi-MAI/Z-Image-Turbo"
_HF_CACHE = Path.home() / ".cache" / "huggingface" / "hub"

# Local weights directory (project-local, survives HF cache cleanup)
_LOCAL_WEIGHTS_DIR = Path(__file__).parent / "weights"


def _find_model_path(model_id: str = _DEFAULT_MODEL_ID) -> Path:
    """Find local weight path for a model.

    Priority:
      1. Project-local ``backends/mlx_zimage/weights/`` (if text_encoder/ exists)
      2. HF cache ``~/.cache/huggingface/hub/models--Tongyi-MAI--Z-Image-Turbo/``
    """
    # 1. Local weights directory
    if _LOCAL_WEIGHTS_DIR.is_dir() and (_LOCAL_WEIGHTS_DIR / "text_encoder").is_dir():
        logger.info("[ZImage] Using local weights: %s", _LOCAL_WEIGHTS_DIR)
        return _LOCAL_WEIGHTS_DIR

    # 2. HF cache
    safe_id = model_id.replace("/", "--")
    model_dir = _HF_CACHE / f"models--{safe_id}"
    if not model_dir.exists():
        raise FileNotFoundError(
            f"Model not found. Neither local ({_LOCAL_WEIGHTS_DIR}) "
            f"nor HF cache ({model_dir}) available."
        )
    # Find the latest snapshot
    snapshots = sorted(model_dir.glob("snapshots/*"), key=lambda p: p.stat().st_mtime, reverse=True)
    if not snapshots:
        raise FileNotFoundError(f"No snapshots found in {model_dir}")
    logger.info("[ZImage] Using HF cache: %s", snapshots[0])
    return snapshots[0]


def _log_memory(label: str) -> None:
    """Log Metal memory usage (safe no-op if unavailable)."""
    try:
        active = mx.metal.get_active_memory() / (1024 ** 3)
        peak = mx.metal.get_peak_memory() / (1024 ** 3)
        logger.info("[ZImage] MEM %s: active=%.2f GB, peak=%.2f GB", label, active, peak)
    except Exception:
        pass  # mx.metal not available (e.g. CI / non-Apple)


def _load_safetensors_shards(
    shard_dir: Path,
    pattern: str = "*.safetensors",
    *,
    key_filter: str | None = None,
) -> dict[str, mx.array]:
    """Load safetensors files via mx.load() — zero-copy, preserves bfloat16.

    Args:
        shard_dir: Directory containing safetensors shard files.
        pattern: Glob pattern for shard files.
        key_filter: If set, only load keys starting with this prefix.
    """
    files = sorted(shard_dir.glob(pattern))
    if not files:
        raise FileNotFoundError(f"No safetensors files in {shard_dir}")

    params: dict[str, mx.array] = {}
    for f in files:
        # mx.load() natively reads safetensors → mx.array (preserves bfloat16)
        shard = mx.load(str(f))
        if key_filter:
            shard = {k: v for k, v in shard.items() if k.startswith(key_filter)}
        params.update(shard)
        logger.info("[ZImage] Loaded shard %s (%d keys)", f.name, len(shard))

    logger.info("[ZImage] Total: %d keys from %d files in %s", len(params), len(files), shard_dir.name)
    _log_memory(f"after loading {shard_dir.name}")
    return params


# ── Text Encoder weight mapping ──────────────────────────────────

def load_text_encoder_weights(model_path: Path | None = None) -> dict[str, mx.array]:
    """Load and map Qwen3 text encoder weights for MLX.

    The safetensors keys use the pattern:
        model.embed_tokens.weight
        model.layers.N.input_layernorm.weight
        model.layers.N.self_attn.q_proj.weight
        ...
        model.norm.weight

    Our MLX module uses:
        embed_tokens.weight
        layers.N.input_layernorm.weight
        layers.N.self_attn.q_proj.weight
        ...
        norm.weight

    So we strip the leading "model." prefix.
    """
    if model_path is None:
        model_path = _find_model_path()

    te_dir = model_path / "text_encoder"
    raw = _load_safetensors_shards(te_dir, "model-*.safetensors")

    mapped: dict[str, mx.array] = {}
    for key, tensor in raw.items():
        # Strip "model." prefix
        if key.startswith("model."):
            new_key = key[len("model."):]
        else:
            new_key = key

        mapped[new_key] = tensor

    logger.info("[ZImage] Text encoder: %d parameters mapped", len(mapped))
    return mapped


# ── Transformer weight mapping ───────────────────────────────────

def load_transformer_weights(model_path: Path | None = None) -> dict[str, mx.array]:
    """Load ZImageTransformer2DModel weights."""
    if model_path is None:
        model_path = _find_model_path()

    dit_dir = model_path / "transformer"
    raw = _load_safetensors_shards(dit_dir, "diffusion_pytorch_model-*.safetensors")

    # Keys are already flat (no "model." prefix), use as-is
    logger.info("[ZImage] Transformer: %d parameters loaded", len(raw))
    return raw


# ── VAE weight mapping ───────────────────────────────────────────

def load_vae_weights(model_path: Path | None = None) -> dict[str, mx.array]:
    """Load AutoencoderKL weights."""
    if model_path is None:
        model_path = _find_model_path()

    vae_dir = model_path / "vae"
    raw = _load_safetensors_shards(vae_dir)

    logger.info("[ZImage] VAE: %d parameters loaded", len(raw))
    return raw


def load_vae_decoder_weights(model_path: Path | None = None) -> list[tuple[str, mx.array]]:
    """Load VAE decoder weights, mapped for the MLX Decoder module.

    Only loads keys starting with ``decoder.`` (skips encoder weights
    to avoid wasting memory).  Performs two transformations:
    1. Strips the ``decoder.`` prefix so keys match the Decoder module tree.
    2. Transposes Conv2d weights from PyTorch (O,I,kH,kW) → MLX (O,kH,kW,I).

    Returns a list of (key, array) tuples ready for ``Decoder.load_weights()``.
    """
    if model_path is None:
        model_path = _find_model_path()

    vae_dir = model_path / "vae"
    # Only load decoder.* keys — skip encoder weights entirely
    raw = _load_safetensors_shards(vae_dir, key_filter="decoder.")

    weights: list[tuple[str, mx.array]] = []
    for key, val in raw.items():
        key = key[len("decoder."):]

        # Conv2d weight: (O, I, kH, kW) → (O, kH, kW, I)
        if val.ndim == 4:
            val = val.transpose(0, 2, 3, 1)

        # force_upcast: ensure float32 for numerical stability
        val = val.astype(mx.float32)

        weights.append((key, val))

    logger.info("[ZImage] VAE decoder: %d parameters mapped", len(weights))
    return weights