"""
BC-250 gfx1010 Comprehensive Monkey-Patch v6
1. Replaces torch.softmax with manual implementation (VGPR overflow fix)
2. Replaces SDPA with manual implementation
3. Patches GGUF cast_bias_weight to dequant on CPU (avoids GPU page-fault hangs)
4. Pre-clones mmap'd tensor data before GPU transfer (XNACK workaround)
5. Pre-warms GPU context and caching allocator
6. Forces text encoder to CPU (memory constraint)
7. Forces VAE decode on CPU (prevents GPU page-fault hang on safetensors mmap)

v6 changes: Removed NO_VRAM (made sampling impossibly slow).
Instead, VAE is forced to decode on CPU. UNet uses normal lowvram path.
Previous LOWVRAM run: 4/4 steps in 21s (5.5s/step). NO_VRAM: stuck at 0/4 for 10+ min.

BC-250 APU / gfx1010: XNACK disabled, no GPU page fault handling.
GPU copy shader hangs on non-resident pages (mmap'd or swapped).
Place in ComfyUI root and import as first line of main.py.
"""
import torch
import torch.nn.functional as F
import os
import sys
import gc
import logging

logger = logging.getLogger(__name__)

SAFE_SOFTMAX_THRESHOLD = int(os.environ.get("BC250_SOFTMAX_THRESHOLD", "512"))

_original_softmax = torch.nn.functional.softmax
_original_tensor_softmax = torch.Tensor.softmax
_original_sdpa = torch.nn.functional.scaled_dot_product_attention

# === MMAP PRE-CLONE PATCH ===
_original_module_apply = torch.nn.Module._apply

def _bc250_safe_apply(self, fn, recurse=True):
    """Pre-clone mmap'd CPU tensor data before GPU transfer to avoid XNACK hangs."""
    for key, param in self._parameters.items():
        if param is not None and param.device.type == 'cpu':
            param.data = param.data.clone()
    for key, buf in self._buffers.items():
        if buf is not None and buf.device.type == 'cpu':
            self._buffers[key] = buf.clone()
    return _original_module_apply(self, fn, recurse)

# === SOFTMAX PATCH ===

def _safe_softmax_impl(input, dim=-1):
    x_max = input.max(dim=dim, keepdim=True).values
    exp_x = torch.exp(input - x_max)
    return exp_x / exp_x.sum(dim=dim, keepdim=True)

def patched_softmax(input, dim=None, _stacklevel=3, dtype=None):
    if dim is None:
        dim = -1
    if dtype is not None:
        input = input.to(dtype)
    if input.is_cuda and input.shape[dim] > SAFE_SOFTMAX_THRESHOLD:
        return _safe_softmax_impl(input, dim)
    return _original_softmax(input, dim=dim)

def patched_tensor_softmax(self, dim=-1, dtype=None):
    if dtype is not None:
        self = self.to(dtype)
    if self.is_cuda and self.shape[dim] > SAFE_SOFTMAX_THRESHOLD:
        return _safe_softmax_impl(self, dim)
    return _original_tensor_softmax(self, dim=dim)

def _safe_sdpa(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
    L, S = query.size(-2), key.size(-2)
    if scale is None:
        scale = query.size(-1) ** -0.5
    attn_weight = torch.matmul(query, key.transpose(-2, -1)) * scale
    if is_causal:
        causal_mask = torch.triu(torch.ones(L, S, dtype=torch.bool, device=query.device), diagonal=1)
        attn_weight = attn_weight.masked_fill(causal_mask, float('-inf'))
    if attn_mask is not None:
        if attn_mask.dtype == torch.bool:
            attn_weight = attn_weight.masked_fill(~attn_mask, float('-inf'))
        else:
            attn_weight = attn_weight + attn_mask
    attn_weight = _safe_softmax_impl(attn_weight, dim=-1)
    if dropout_p > 0.0:
        attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p)
    return torch.matmul(attn_weight, value)

def patched_sdpa(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
    S = key.size(-2)
    if query.is_cuda and S > SAFE_SOFTMAX_THRESHOLD:
        return _safe_sdpa(query, key, value, attn_mask=attn_mask,
                         dropout_p=dropout_p, is_causal=is_causal, scale=scale)
    return _original_sdpa(query, key, value, attn_mask=attn_mask,
                         dropout_p=dropout_p, is_causal=is_causal, scale=scale)

# === GGUF CPU-DEQUANT PATCH (cast_bias_weight override) ===
_gguf_patched = False

def _try_patch_gguf():
    """Patch GGMLLayer.cast_bias_weight to dequant on CPU, send floats to GPU."""
    global _gguf_patched
    if _gguf_patched:
        return True

    ops_mod = None
    dequant_mod = None
    for name, mod in sys.modules.items():
        if mod is None:
            continue
        if name.endswith('.ops') and 'GGUF' in name:
            ops_mod = mod
        if name.endswith('.dequant') and 'GGUF' in name:
            dequant_mod = mod

    if ops_mod is None or dequant_mod is None:
        return False

    GGMLLayer = getattr(ops_mod, 'GGMLLayer', None)
    is_quantized_fn = getattr(dequant_mod, 'is_quantized', None)
    if GGMLLayer is None or is_quantized_fn is None:
        return False

    def _bc250_cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
        """Dequant on CPU, only send float results to GPU."""
        import comfy.model_management
        import comfy.ops

        if input is not None:
            if dtype is None:
                dtype = getattr(input, "dtype", torch.float32)
            if bias_dtype is None:
                bias_dtype = dtype
            if device is None:
                device = input.device

        non_blocking = comfy.model_management.device_supports_non_blocking(device)

        bias = None
        if s.bias is not None:
            if is_quantized_fn(s.bias):
                bias = s.get_weight(s.bias, bias_dtype)
            else:
                bias = s.get_weight(s.bias.to(device), bias_dtype)
            bias = comfy.ops.cast_to(bias, bias_dtype, device, non_blocking=non_blocking, copy=False)

        if is_quantized_fn(s.weight):
            weight = s.get_weight(s.weight, dtype)
        else:
            weight = s.get_weight(s.weight.to(device), dtype)
        weight = comfy.ops.cast_to(weight, dtype, device, non_blocking=non_blocking, copy=False)
        return weight, bias

    GGMLLayer.cast_bias_weight = _bc250_cast_bias_weight

    _gguf_patched = True
    logger.warning("[BC-250] GGUF cast_bias_weight patched (CPU dequant, float-only GPU transfer)")
    return True

# === IMPORT HOOK for deferred GGUF patching ===

class _GGUFImportWatcher:
    def __init__(self):
        self.done = False

    def find_module(self, fullname, path=None):
        if self.done:
            return None
        if 'GGUF' in fullname and ('dequant' in fullname or 'ops' in fullname):
            return self
        return None

    def load_module(self, fullname):
        if self in sys.meta_path:
            sys.meta_path.remove(self)
        try:
            import importlib
            mod = importlib.import_module(fullname)
        finally:
            if self not in sys.meta_path:
                sys.meta_path.insert(0, self)

        if _try_patch_gguf():
            self.done = True
        return mod

# === TEXT ENCODER CPU PATCH ===
_te_patched = False

def _try_patch_text_encoder_device():
    global _te_patched
    if _te_patched:
        return True
    mm = sys.modules.get('comfy.model_management')
    if mm is None:
        return False
    mm.text_encoder_device = lambda: torch.device("cpu")
    mm.text_encoder_offload_device = lambda: torch.device("cpu")

    _te_patched = True
    logger.warning("[BC-250] Text encoder forced to CPU (memory constraint)")
    return True

# === VAE CPU-ONLY PATCH ===
# Force VAE to decode on CPU. VAE is only 320MB — fast enough on CPU for small images.
# Avoids GPU page-fault hangs from safetensors mmap'd weights on BC-250 (XNACK disabled).

_vae_patched = False

def _try_patch_vae_cpu():
    """Patch comfy.sd.VAE to decode and encode on CPU only."""
    global _vae_patched
    if _vae_patched:
        return True

    sd_mod = sys.modules.get('comfy.sd')
    if sd_mod is None:
        return False

    VAE = getattr(sd_mod, 'VAE', None)
    if VAE is None:
        return False

    _original_vae_decode = VAE.decode
    _original_vae_encode = getattr(VAE, 'encode', None)

    def _bc250_vae_decode(self, samples_in, vae_options={}):
        """Force VAE decode on CPU — bypass load_models_gpu entirely.
        
        Root cause: load_models_gpu tries to unload UNet (5032MB in GPU managed memory)
        before loading VAE. Unloading reads GPU pages that may be swapped → XNACK hang.
        Solution: skip load_models_gpu, run VAE inference directly on CPU.
        """
        import comfy.model_management as mm

        logger.warning("[BC-250] VAE decode: CPU-only bypass (skipping load_models_gpu)")
        torch.cuda.empty_cache()
        gc.collect()

        # Temporarily no-op load_models_gpu to prevent UNet unload hang
        _orig_lmg = mm.load_models_gpu
        mm.load_models_gpu = lambda *a, **kw: None

        # Save and override device to CPU
        orig_device = getattr(self, 'device', None)
        orig_output_device = getattr(self, 'output_device', None)
        self.device = torch.device("cpu")
        self.output_device = torch.device("cpu")

        try:
            # Ensure VAE model weights are on CPU
            if hasattr(self, 'first_stage_model'):
                self.first_stage_model.to(torch.device("cpu"))
                self.first_stage_model.eval()

            # Run the original decode (which now skips load_models_gpu)
            result = _original_vae_decode(self, samples_in, vae_options)
            if isinstance(result, torch.Tensor):
                result = result.to(device=torch.device("cpu"))
            return result
        finally:
            # Restore everything
            mm.load_models_gpu = _orig_lmg
            if orig_device is not None:
                self.device = orig_device
            if orig_output_device is not None:
                self.output_device = orig_output_device

    VAE.decode = _bc250_vae_decode

    if _original_vae_encode is not None:
        def _bc250_vae_encode(self, pixel_samples):
            """Force VAE encode on CPU — same bypass as decode."""
            import comfy.model_management as mm
            logger.warning("[BC-250] VAE encode: CPU-only bypass")
            torch.cuda.empty_cache()
            gc.collect()
            _orig_lmg = mm.load_models_gpu
            mm.load_models_gpu = lambda *a, **kw: None
            orig_device = getattr(self, 'device', None)
            orig_output_device = getattr(self, 'output_device', None)
            self.device = torch.device("cpu")
            self.output_device = torch.device("cpu")
            try:
                if hasattr(self, 'first_stage_model'):
                    self.first_stage_model.to(torch.device("cpu"))
                    self.first_stage_model.eval()
                pixel_samples = pixel_samples.to(device=torch.device("cpu"), dtype=torch.float32)
                result = _original_vae_encode(self, pixel_samples)
                if isinstance(result, torch.Tensor):
                    result = result.to(device=torch.device("cpu"))
                return result
            finally:
                mm.load_models_gpu = _orig_lmg
                if orig_device is not None:
                    self.device = orig_device
                if orig_output_device is not None:
                    self.output_device = orig_output_device

        VAE.encode = _bc250_vae_encode

    _vae_patched = True
    logger.warning("[BC-250] VAE forced to CPU decode/encode (prevents mmap GPU hangs)")
    return True

class _SDModuleWatcher:
    """Patches comfy.sd.VAE after it's imported."""
    def __init__(self):
        self.done = False
    def find_module(self, fullname, path=None):
        if self.done:
            return None
        if fullname == 'comfy.sd':
            return self
        return None
    def load_module(self, fullname):
        if self in sys.meta_path:
            sys.meta_path.remove(self)
        try:
            import importlib
            mod = importlib.import_module(fullname)
        finally:
            if self not in sys.meta_path:
                sys.meta_path.insert(0, self)
        if _try_patch_vae_cpu():
            self.done = True
        return mod

class _ModelMgmtWatcher:
    def __init__(self):
        self.done = False
    def find_module(self, fullname, path=None):
        if self.done:
            return None
        if fullname == 'comfy.model_management':
            return self
        return None
    def load_module(self, fullname):
        if self in sys.meta_path:
            sys.meta_path.remove(self)
        try:
            import importlib
            mod = importlib.import_module(fullname)
        finally:
            if self not in sys.meta_path:
                sys.meta_path.insert(0, self)
        if _try_patch_text_encoder_device():
            self.done = True
        return mod

# === GPU MEMORY CLEANUP HOOK ===
# Patch model_management.load_models_gpu to clean up before loading

_load_patched = False

def _try_patch_load_models():
    """Add GPU memory cleanup before model loading."""
    global _load_patched
    if _load_patched:
        return True

    mm = sys.modules.get('comfy.model_management')
    if mm is None:
        return False

    _original_load = getattr(mm, 'load_models_gpu', None)
    if _original_load is None:
        return False

    def _bc250_load_models_gpu(models, *args, **kwargs):
        """Clean GPU cache before loading models to prevent memory pressure hangs."""
        gc.collect()
        torch.cuda.empty_cache()
        return _original_load(models, *args, **kwargs)

    mm.load_models_gpu = _bc250_load_models_gpu
    _load_patched = True
    logger.warning("[BC-250] GPU memory cleanup hook installed (load_models_gpu)")
    return True

# === INSTALL ===

def _prewarm_gpu():
    try:
        if not torch.cuda.is_available():
            return
        dummy = torch.zeros(1, device='cuda')
        _ = dummy + 1
        torch.cuda.synchronize()
        del dummy
        torch.cuda.empty_cache()
        logger.warning("[BC-250] GPU pre-warmed (context + allocator ready)")
    except Exception as e:
        logger.warning(f"[BC-250] GPU pre-warm failed: {e}")


def install():
    # Mmap pre-clone patch
    torch.nn.Module._apply = _bc250_safe_apply
    logger.warning("[BC-250] Mmap pre-clone patch installed (XNACK workaround)")

    # Softmax patches
    torch.nn.functional.softmax = patched_softmax
    torch.Tensor.softmax = patched_tensor_softmax
    torch.nn.functional.scaled_dot_product_attention = patched_sdpa
    logger.warning(f"[BC-250] Softmax monkey-patch installed (threshold={SAFE_SOFTMAX_THRESHOLD})")

    # GGUF deferred cast_bias_weight patch
    sys.meta_path.insert(0, _GGUFImportWatcher())
    logger.warning("[BC-250] GGUF CPU-dequant hook registered (cast_bias_weight)")

    # Text encoder CPU patch
    sys.meta_path.insert(0, _ModelMgmtWatcher())

    # VAE CPU-only patch
    sys.meta_path.insert(0, _SDModuleWatcher())

    # Try immediate patches if modules already loaded
    _try_patch_gguf()
    _try_patch_text_encoder_device()
    _try_patch_vae_cpu()
    _try_patch_load_models()

    # Pre-warm GPU
    _prewarm_gpu()

install()