import os
import re
import sys
import json
import time
import diffusers
from installer import install
from modules.logger import log


ao = None
bnb = None
optimum_quanto = None
trt = None
quant_last_model_name = None
quant_last_model_device = None
debug = os.environ.get('SD_QUANT_DEBUG', None) is not None


def get_quant_type(args):
    if args is not None and "quantization_config" in args:
        return args['quantization_config'].__class__.__name__
    return None


def get_quant(name):
    if "qint8" in name.lower():
        return 'qint8'
    if "qint4" in name.lower():
        return 'qint4'
    if "fp8" in name.lower():
        return 'fp8'
    if "fp4" in name.lower():
        return 'fp4'
    if "nf4" in name.lower():
        return 'nf4'
    if name.endswith('.gguf'):
        return 'gguf'
    return 'none'


def dont_quant():
    from modules import shared
    models_list = re.split(r'[ ,]+', shared.opts.models_not_to_quant)
    models_list = [m.lower().strip() for m in models_list]
    if shared.sd_model_type.lower() in models_list:
        log.debug(f'Quantization: model={shared.sd_model_type} skip')
        return True
    return False


def create_trt_config(kwargs = None, allow: bool = True, module: str = 'Model', modules_to_not_convert: list | None = None):
    from modules import shared
    if allow and (module == 'any' or module in shared.opts.trt_quantization):
        load_trt()
        if trt is None:
            return kwargs
        trt_config_data = {
            "int8": {"quant_type": "INT8", "quant_method": "modelopt", "modules_to_not_convert": []},
            "int4": {"quant_type": "INT4", "quant_method": "modelopt", "block_quantize": 128, "channel_quantize": -1, "modules_to_not_convert": ["conv", "patch_embed"]},
            "fp8": {"quant_type": "FP8", "quant_method": "modelopt", "modules_to_not_convert": []},
            "nf4": {"quant_type": "NF4", "quant_method": "modelopt", "block_quantize": 128, "channel_quantize": -1, "scale_block_quantize": 8, "scale_channel_quantize": -1, "modules_to_not_convert": ["conv"]},
            "nvfp4": {"quant_type": "NVFP4", "quant_method": "modelopt", "block_quantize": 128, "channel_quantize": -1, "modules_to_not_convert": ["conv"]},
        }
        trt_quant_config = trt_config_data[shared.opts.trt_quantization_type].copy()
        if modules_to_not_convert is not None:
            for m in modules_to_not_convert:
                if m not in trt_quant_config['modules_to_not_convert']:
                    trt_quant_config['modules_to_not_convert'].append(m)
        trt_config = diffusers.quantizers.quantization_config.NVIDIAModelOptConfig(**trt_quant_config)
        log.debug(f'Quantization: module={module} type=tensorrt dtype={shared.opts.trt_quantization_type}')
        if kwargs is None:
            return trt_config
        else:
            kwargs['quantization_config'] = trt_config
            return kwargs
    return kwargs


def get_sdnq_devices(mode="pre"):
    from modules import devices, shared
    if shared.opts.device_map == "gpu":
        quantization_device = devices.device
        return_device = devices.device
    elif shared.opts.device_map == "cpu":
        quantization_device = devices.cpu
        return_device = devices.cpu
    elif shared.opts.diffusers_offload_mode in {"none", "model"} or (mode == "post" and shared.opts.sdnq_quantize_shuffle_weights):
        quantization_device = devices.device if shared.opts.sdnq_quantize_with_gpu else devices.cpu
        return_device = devices.device
    elif shared.opts.sdnq_quantize_with_gpu:
        quantization_device = devices.device
        return_device = devices.device if shared.opts.diffusers_to_gpu else devices.cpu
    else:
        quantization_device = None
        return_device = None
    return quantization_device, return_device


def create_sdnq_config(kwargs = None, allow: bool = True, module: str = 'Model', weights_dtype: str | None = None, quantized_matmul_dtype: str | None = None, modules_to_not_convert: list | None = None, modules_dtype_dict: dict | None = None):
    from modules import shared
    if allow and (shared.opts.sdnq_quantize_mode in {'pre', 'auto'}) and (module == 'any' or module in shared.opts.sdnq_quantize_weights):
        from modules.sdnq import SDNQConfig
        from modules.sdnq.common import use_torch_compile as sdnq_use_torch_compile

        if shared.opts.sdnq_use_quantized_matmul and not sdnq_use_torch_compile:
            log.warning('SDNQ Quantized MatMul requires a working Triton install. Disabling Quantized MatMul.')
            shared.opts.sdnq_use_quantized_matmul = False

        if weights_dtype is None:
            if module in {"TE", "LLM"} and shared.opts.sdnq_quantize_weights_mode_te not in {"Same as model", "default"}:
                weights_dtype = shared.opts.sdnq_quantize_weights_mode_te
            else:
                weights_dtype = shared.opts.sdnq_quantize_weights_mode
        if weights_dtype is None or weights_dtype == 'none':
            return kwargs

        if quantized_matmul_dtype is None:
            if module in {"TE", "LLM"} and shared.opts.sdnq_quantize_matmul_mode_te not in {"Same as model", "default"}:
                quantized_matmul_dtype = shared.opts.sdnq_quantize_matmul_mode_te
            else:
                quantized_matmul_dtype = shared.opts.sdnq_quantize_matmul_mode
        if quantized_matmul_dtype == "auto":
            quantized_matmul_dtype = None

        if modules_to_not_convert is None:
            modules_to_not_convert = []
        if modules_dtype_dict is None:
            modules_dtype_dict = {}

        sdnq_modules_to_not_convert = [m.strip() for m in re.split(';|,| ', shared.opts.sdnq_modules_to_not_convert) if len(m.strip()) > 1]
        if len(sdnq_modules_to_not_convert) > 0:
            modules_to_not_convert.extend(sdnq_modules_to_not_convert)

        try:
            if len(shared.opts.sdnq_modules_dtype_dict) > 2:
                sdnq_modules_dtype_dict = shared.opts.sdnq_modules_dtype_dict
                if "{" not in sdnq_modules_dtype_dict:
                    sdnq_modules_dtype_dict = "{" + sdnq_modules_dtype_dict + "}"
                sdnq_modules_dtype_dict = json.loads(bytes(sdnq_modules_dtype_dict, 'utf-8'))
                for key, value in sdnq_modules_dtype_dict.items():
                    if isinstance(value, str):
                        value = [m.strip() for m in re.split(';|,| ', value) if len(m.strip()) > 1]
                    if key not in modules_dtype_dict.keys():
                        modules_dtype_dict[key] = value
                    else:
                        modules_dtype_dict[key].extend(value)
        except Exception as e:
            log.warning(f'Quantization: SDNQ failed to parse sdnq_modules_dtype_dict: {e}')

        quantization_device, return_device = get_sdnq_devices(mode="pre")

        sdnq_config = SDNQConfig(
            weights_dtype=weights_dtype,
            quantized_matmul_dtype=quantized_matmul_dtype,
            group_size=shared.opts.sdnq_quantize_weights_group_size,
            svd_rank=shared.opts.sdnq_svd_rank,
            svd_steps=shared.opts.sdnq_svd_steps,
            dynamic_loss_threshold=shared.opts.sdnq_dynamic_loss_threshold,
            use_svd=shared.opts.sdnq_use_svd,
            quant_conv=shared.opts.sdnq_quantize_conv_layers,
            use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
            use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
            use_dynamic_quantization=shared.opts.sdnq_use_dynamic_quantization,
            dequantize_fp32=shared.opts.sdnq_dequantize_fp32,
            non_blocking=shared.opts.diffusers_offload_nonblocking,
            quantization_device=quantization_device,
            return_device=return_device,
            modules_to_not_convert=modules_to_not_convert,
            modules_dtype_dict=modules_dtype_dict.copy(),
        )
        if quantized_matmul_dtype is None:
            quantized_matmul_dtype = "auto" # set for logging
        svd = f'{shared.opts.sdnq_use_svd} rank={shared.opts.sdnq_svd_rank} steps={shared.opts.sdnq_svd_steps}' if shared.opts.sdnq_use_svd else f'{shared.opts.sdnq_use_svd}'
        log.debug(f'Quantization: module="{module}" type=sdnq mode=pre dtype={weights_dtype} svd={svd} dynamic={shared.opts.sdnq_use_dynamic_quantization} group={shared.opts.sdnq_quantize_weights_group_size} loss={shared.opts.sdnq_dynamic_loss_threshold} matmul_dtype={quantized_matmul_dtype} matmul_quant={shared.opts.sdnq_use_quantized_matmul} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv}  quant_conv={shared.opts.sdnq_quantize_conv_layers} fp32={shared.opts.sdnq_dequantize_fp32} device={quantization_device} return={return_device} use_gpu={shared.opts.sdnq_quantize_with_gpu} map={shared.opts.device_map} offload={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} skip_modules={modules_to_not_convert} dict={modules_dtype_dict}')
        if kwargs is None:
            return sdnq_config
        else:
            kwargs['quantization_config'] = sdnq_config
            return kwargs
    return kwargs


def check_quant(module: str = ''):
    from modules import shared
    if module in shared.opts.sdnq_quantize_weights:
        return True
    return False


def check_nunchaku(module: str = ''):
    from modules import shared
    if 'nunchaku' not in shared.opts.sd_model_checkpoint.lower():
        return False
    base_path = shared.opts.sd_model_checkpoint.split('+')[0]
    for v in shared.reference_models.values():
        if v.get('path', '') != base_path:
            continue
        nunchaku_modules = v.get('nunchaku', None)
        if nunchaku_modules is None:
            continue
        if isinstance(nunchaku_modules, bool) and nunchaku_modules:
            nunchaku_modules = ['Model', 'TE']
        if not isinstance(nunchaku_modules, list):
            continue
        if module in nunchaku_modules:
            from modules import mit_nunchaku
            mit_nunchaku.install_nunchaku()
            return mit_nunchaku.ok
    return False


def create_config(kwargs = None, allow: bool = True, module: str = 'Model', modules_to_not_convert: list | None = None, modules_dtype_dict: dict | None = None):
    if kwargs is None:
        kwargs = {}
    if module == 'Model' and dont_quant():
        return kwargs
    kwargs = create_sdnq_config(kwargs, allow=allow, module=module, modules_to_not_convert=modules_to_not_convert, modules_dtype_dict=modules_dtype_dict)
    if kwargs is not None and 'quantization_config' in kwargs:
        if debug:
            log.trace(f'Quantization: type=sdnq config={kwargs.get("quantization_config", None)}')
        return kwargs
    kwargs = create_trt_config(kwargs, allow=allow, module=module, modules_to_not_convert=modules_to_not_convert)
    if kwargs is not None and 'quantization_config' in kwargs:
        if debug:
            log.trace(f'Quantization: type=tensorrt config={kwargs.get("quantization_config", None)}')
        return kwargs
    return kwargs


def load_trt(msg='', silent=False):
    global trt # pylint: disable=global-statement
    if trt is not None:
        return trt
    try:
        install('nvidia-modelopt')
        import pydantic
        if pydantic.__version__.startswith('1'):
            log.error('Quantization: type=tensorrt pydantic==2 required')
            return None
        import modelopt
        trt = modelopt
        fn = f'{sys._getframe(3).f_code.co_name}:{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
        log.debug(f'Quantization: type=tensorrt version={trt.__version__} fn={fn}') # pylint: disable=protected-access
        return trt
    except Exception as e:
        if len(msg) > 0:
            log.error(f"{msg} failed to import tensorrt: {e}")
        trt = None
        if not silent:
            raise
    return None


def upcast_non_layerwise_modules(model, dtype): # pylint: disable=unused-argument
    from diffusers.hooks.layerwise_casting import _GO_LC_SUPPORTED_PYTORCH_LAYERS
    model_children = list(model.children())
    if not model_children:
        if not isinstance(model, _GO_LC_SUPPORTED_PYTORCH_LAYERS):
            model = model.to(dtype)
        return model
    for module in model_children:
        has_children = list(module.children())
        if not has_children:
            if not isinstance(module, _GO_LC_SUPPORTED_PYTORCH_LAYERS):
                module = module.to(dtype)
        else:
            module = upcast_non_layerwise_modules(module, dtype)
    return model


def load_fp8_model_layerwise(checkpoint_info, load_model_func, diffusers_load_config):
    model = None
    if isinstance(checkpoint_info, str):
        repo_path = checkpoint_info
    else:
        repo_path = checkpoint_info.path
    try:
        import torch
        from modules import devices, shared
        from diffusers.quantizers import quantization_config
        if not hasattr(quantization_config.QuantizationMethod, 'LAYERWISE'):
            setattr(quantization_config.QuantizationMethod, 'LAYERWISE', 'layerwise') # noqa: B010
        if "e5m2" in repo_path.lower():
            storage_dtype = torch.float8_e5m2
        else:
            storage_dtype= torch.float8_e4m3fn
        load_args = diffusers_load_config.copy()
        load_args["torch_dtype"] = storage_dtype
        model = load_model_func(repo_path, **load_args)
        model = upcast_non_layerwise_modules(model, devices.dtype)
        model._skip_layerwise_casting_patterns = None # pylint: disable=protected-access
        model.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=shared.opts.diffusers_offload_nonblocking, skip_modules_pattern=[])
        model.layerwise_storage_dtype = storage_dtype
        model.quantization_method = 'LayerWise'
    except Exception as e:
        log.error(f"Load model: Failed to load FP8 model: {e}")
        model = None
    return model


def apply_layerwise(sd_model, quiet:bool=False):
    import torch
    from diffusers.quantizers import quantization_config
    from modules import shared, devices, sd_models
    if shared.opts.layerwise_quantization_storage == 'float8_e4m3fn' and hasattr(torch, 'float8_e4m3fn'):
        storage_dtype = torch.float8_e4m3fn
    elif shared.opts.layerwise_quantization_storage == 'float8_e5m2' and hasattr(torch, 'float8_e5m2'):
        storage_dtype = torch.float8_e5m2
    else:
        storage_dtype = None
        log.warning(f'Quantization: type=layerwise storage={shared.opts.layerwise_quantization_storage} not supported')
        return
    if not hasattr(quantization_config.QuantizationMethod, 'LAYERWISE'):
        setattr(quantization_config.QuantizationMethod, 'LAYERWISE', 'layerwise') # noqa: B010
    for module in sd_models.get_signature(sd_model).keys():
        if not hasattr(sd_model, module):
            continue
        try:
            cls = getattr(sd_model, module).__class__.__name__
            m = getattr(sd_model, module)
            if getattr(m, "quantization_method", None) in {'LayerWise', quantization_config.QuantizationMethod.LAYERWISE}: # pylint: disable=no-member
                storage_dtype = getattr(m, "layerwise_storage_dtype", storage_dtype)
                m.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=shared.opts.diffusers_offload_nonblocking)
            elif module.startswith('unet') and ('Model' in shared.opts.layerwise_quantization):
                if hasattr(m, 'enable_layerwise_casting'):
                    m.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=shared.opts.diffusers_offload_nonblocking)
                    m.layerwise_storage_dtype = storage_dtype
                    m.quantization_method = 'LayerWise'
                    log.quiet(quiet, f'Quantization: type=layerwise module={module} cls={cls} storage={storage_dtype} compute={devices.dtype} blocking={not shared.opts.diffusers_offload_nonblocking}')
            elif module.startswith('transformer') and ('Model' in shared.opts.layerwise_quantization):
                if hasattr(m, 'enable_layerwise_casting'):
                    m.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=shared.opts.diffusers_offload_nonblocking)
                    m.layerwise_storage_dtype = storage_dtype
                    m.quantization_method = 'LayerWise'
                    log.quiet(quiet, f'Quantization: type=layerwise module={module} cls={cls} storage={storage_dtype} compute={devices.dtype} blocking={not shared.opts.diffusers_offload_nonblocking}')
            elif module.startswith('text_encoder') and ('TE' in shared.opts.layerwise_quantization) and ('clip' not in cls.lower()):
                if hasattr(m, 'enable_layerwise_casting'):
                    m.enable_layerwise_casting(compute_dtype=devices.dtype, storage_dtype=storage_dtype, non_blocking=shared.opts.diffusers_offload_nonblocking)
                    m.layerwise_storage_dtype = storage_dtype
                    m.quantization_method = quantization_config.QuantizationMethod.LAYERWISE # pylint: disable=no-member
                    log.quiet(quiet, f'Quantization: type=layerwise module={module} cls={cls} storage={storage_dtype} compute={devices.dtype} blocking={not shared.opts.diffusers_offload_nonblocking}')
        except Exception as e:
            if 'Hook with name' not in str(e):
                log.error(f'Quantization: type=layerwise {e}')


def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weights_dtype: str | None = None, quantized_matmul_dtype: str | None = None, modules_to_not_convert: list | None = None, modules_dtype_dict: dict | None = None):
    global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
    from modules import devices, shared, timer
    from modules.sdnq import sdnq_post_load_quant
    from modules.sdnq.common import use_torch_compile as sdnq_use_torch_compile

    if shared.opts.sdnq_use_quantized_matmul and not sdnq_use_torch_compile:
        log.warning('SDNQ Quantized MatMul requires a working Triton install. Disabling Quantized MatMul.')
        shared.opts.sdnq_use_quantized_matmul = False

    if weights_dtype is None:
        if (op is not None) and ("text_encoder" in op or op in {"TE", "LLM"}) and (shared.opts.sdnq_quantize_weights_mode_te not in {"Same as model", "default"}):
            weights_dtype = shared.opts.sdnq_quantize_weights_mode_te
        else:
            weights_dtype = shared.opts.sdnq_quantize_weights_mode
    if weights_dtype is None or weights_dtype == 'none':
        return model

    if quantized_matmul_dtype is None:
        if (op is not None) and ("text_encoder" in op or op in {"TE", "LLM"}) and (shared.opts.sdnq_quantize_matmul_mode_te not in {"Same as model", "default"}):
            quantized_matmul_dtype = shared.opts.sdnq_quantize_matmul_mode_te
        else:
            quantized_matmul_dtype = shared.opts.sdnq_quantize_matmul_mode
    if quantized_matmul_dtype == "auto":
        quantized_matmul_dtype = None

    quantization_device, return_device = get_sdnq_devices(mode="post")

    if modules_to_not_convert is None:
        modules_to_not_convert = []
    if modules_dtype_dict is None:
        modules_dtype_dict = {}

    sdnq_modules_to_not_convert = [m.strip() for m in re.split(';|,| ', shared.opts.sdnq_modules_to_not_convert) if len(m.strip()) > 1]
    if len(sdnq_modules_to_not_convert) > 0:
        modules_to_not_convert.extend(sdnq_modules_to_not_convert)

    try:
        if len(shared.opts.sdnq_modules_dtype_dict) > 2:
            sdnq_modules_dtype_dict = shared.opts.sdnq_modules_dtype_dict
            if "{" not in sdnq_modules_dtype_dict:
                sdnq_modules_dtype_dict = "{" + sdnq_modules_dtype_dict + "}"
            sdnq_modules_dtype_dict = json.loads(bytes(sdnq_modules_dtype_dict, 'utf-8'))
            for key, value in sdnq_modules_dtype_dict.items():
                if isinstance(value, str):
                    value = [m.strip() for m in re.split(';|,| ', value) if len(m.strip()) > 1]
                if key not in modules_dtype_dict.keys():
                    modules_dtype_dict[key] = value
                else:
                    modules_dtype_dict[key].extend(value)
    except Exception as e:
        log.warning(f'Quantization: SDNQ failed to parse sdnq_modules_dtype_dict: {e}')

    t0 = time.time()

    model = sdnq_post_load_quant(
        model,
        weights_dtype=weights_dtype,
        quantized_matmul_dtype=quantized_matmul_dtype,
        torch_dtype=devices.dtype,
        group_size=shared.opts.sdnq_quantize_weights_group_size,
        svd_rank=shared.opts.sdnq_svd_rank,
        svd_steps=shared.opts.sdnq_svd_steps,
        dynamic_loss_threshold=shared.opts.sdnq_dynamic_loss_threshold,
        use_svd=shared.opts.sdnq_use_svd,
        quant_conv=shared.opts.sdnq_quantize_conv_layers,
        use_quantized_matmul=shared.opts.sdnq_use_quantized_matmul,
        use_quantized_matmul_conv=shared.opts.sdnq_use_quantized_matmul_conv,
        use_dynamic_quantization=shared.opts.sdnq_use_dynamic_quantization,
        dequantize_fp32=shared.opts.sdnq_dequantize_fp32,
        non_blocking=shared.opts.diffusers_offload_nonblocking,
        quantization_device=quantization_device,
        return_device=return_device,
        modules_to_not_convert=modules_to_not_convert,
        modules_dtype_dict=modules_dtype_dict.copy(),
    )

    t1 = time.time()
    timer.load.add('sdnq', t1 - t0)

    if op is not None and shared.opts.sdnq_quantize_shuffle_weights:
        if quant_last_model_name is not None:
            if "." in quant_last_model_name:
                last_model_names = quant_last_model_name.split(".")
                getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
            else:
                getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
            if do_gc:
                devices.torch_gc(force=True, reason='sdnq')
        if shared.cmd_opts.medvram or shared.cmd_opts.lowvram or shared.opts.diffusers_offload_mode != "none":
            quant_last_model_name = op
            quant_last_model_device = model.device
        else:
            quant_last_model_name = None
            quant_last_model_device = None
        model.to(devices.device)
    elif (shared.opts.diffusers_offload_mode != "none") and (not shared.opts.diffusers_to_gpu):
        model = model.to(devices.cpu)
    if do_gc:
        devices.torch_gc(force=True, reason='sdnq')

    if quantized_matmul_dtype is None:
        quantized_matmul_dtype = "auto" # set for logging
    log.debug(f'Quantization: module="{op if op is not None else model.__class__}" type=sdnq mode=post dtype={weights_dtype} matmul_dtype={quantized_matmul_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} svd={shared.opts.sdnq_use_svd} dynamic={shared.opts.sdnq_use_dynamic_quantization}:group={shared.opts.sdnq_quantize_weights_group_size}:rank={shared.opts.sdnq_svd_rank}:steps={shared.opts.sdnq_svd_steps}:loss={shared.opts.sdnq_dynamic_loss_threshold} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} fp32={shared.opts.sdnq_dequantize_fp32} gpu={shared.opts.sdnq_quantize_with_gpu} device={quantization_device} return={return_device} map={shared.opts.device_map} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_skip={modules_to_not_convert} modules_dtype={modules_dtype_dict}')
    return model


def sdnq_quantize_weights(sd_model):
    try:
        t0 = time.time()
        from modules import shared, devices, sd_models
        log.debug(f"Quantization: type=SDNQ modules={shared.opts.sdnq_quantize_weights} dtype={shared.opts.sdnq_quantize_weights_mode} dtype_te={shared.opts.sdnq_quantize_weights_mode_te} offload={shared.opts.diffusers_offload_mode} pre_forward={shared.opts.diffusers_offload_pre}")
        global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement

        sd_model = sd_models.apply_function_to_model(sd_model, sdnq_quantize_model, shared.opts.sdnq_quantize_weights, op="sdnq")
        if quant_last_model_name is not None:
            if "." in quant_last_model_name:
                last_model_names = quant_last_model_name.split(".")
                getattr(getattr(sd_model, last_model_names[0]), last_model_names[1]).to(quant_last_model_device)
            else:
                getattr(sd_model, quant_last_model_name).to(quant_last_model_device)
            devices.torch_gc(force=True, reason='sdnq')
        quant_last_model_name = None
        quant_last_model_device = None

        t1 = time.time()
        log.info(f"Quantization: type=SDNQ time={t1-t0:.2f}")
    except Exception as e:
        log.warning(f"Quantization: type=SDNQ {e}")
        from modules import errors
        errors.display(e, 'Quantization')
    return sd_model


def get_dit_args(load_config: dict | None = None, module: str | None = None, device_map: bool = False, allow_quant: bool = True, modules_to_not_convert: list | None = None, modules_dtype_dict: dict | None = None):
    from modules import shared, devices
    config = {} if load_config is None else load_config.copy()
    if 'torch_dtype' not in config:
        config['torch_dtype'] = devices.dtype
    if 'low_cpu_mem_usage' in config:
        del config['low_cpu_mem_usage']
    if 'load_connected_pipeline' in config:
        del config['load_connected_pipeline']
    if 'safety_checker' in config:
        del config['safety_checker']
    if 'requires_safety_checker' in config:
        del config['requires_safety_checker']
    # if 'variant' in config:
    #     del config['variant']
    if device_map:
        if devices.backend == 'ipex' and os.environ.get('UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS', '0') != '1' and module in {'TE', 'LLM'}:
            config['device_map'] = 'cpu' # alchemist gpus hits the 4GB allocation limit with transformers, UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS emulates above 4GB allocations
        elif shared.opts.device_map == 'cpu':
            config['device_map'] = 'cpu'
        elif shared.opts.device_map == 'gpu':
            config['device_map'] = devices.device
    if allow_quant:
        quant_args = create_config(module=module, modules_to_not_convert=modules_to_not_convert, modules_dtype_dict=modules_dtype_dict)
    else:
        quant_args = {}
    return config, quant_args


def do_post_load_quant(sd_model, allow=True):
    from modules import shared
    if dont_quant():
        return sd_model
    if shared.opts.sdnq_quantize_weights and (shared.opts.sdnq_quantize_mode == 'post' or (allow and shared.opts.sdnq_quantize_mode == 'auto')):
        log.debug('Load model: post_quant=sdnq')
        sd_model = sdnq_quantize_weights(sd_model)
    if shared.opts.layerwise_quantization:
        log.debug('Load model: post_quant=layerwise')
        apply_layerwise(sd_model)
    return sd_model