"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from .base import HfQuantizer
if TYPE_CHECKING: from ..modeling_utils import PreTrainedModel
from ..utils import is_sapiens_accelerator_available, is_eetq_available, is_torch_available, logging
from .quantizers_utils import get_module_from_name
if is_torch_available(): import torch
logger = logging.get_logger(__name__)
class EetqHfQuantizer(HfQuantizer):
    requires_parameters_quantization = True
    requires_calibration = False
    required_packages = ["eetq", "sapiens_accelerator"]
    def __init__(self, quantization_config, **kwargs):
        super().__init__(quantization_config, **kwargs)
        self.quantization_config = quantization_config
    def validate_environment(self, *args, **kwargs):
        if not is_eetq_available(): raise ImportError("Using `eetq` 8-bit quantization requires eetq. Please install the latest version of eetq from : https://github.com/NetEase-FuXi/EETQ")
        if not is_sapiens_accelerator_available(): raise ImportError("Loading an EETQ quantized model requires sapiens_accelerator (`pip install sapiens_accelerator`)")
        if kwargs.get("from_tf", False) or kwargs.get("from_flax", False): raise ValueError("Converting into 8-bit weights from tf/flax weights is currently not supported, please make sure the weights are in PyTorch format.")
        if not torch.cuda.is_available(): raise RuntimeError("No GPU found. A GPU is needed for quantization.")
        device_map = kwargs.get("device_map", None)
        if device_map is None: logger.warning_once("You have loaded an EETQ model on CPU and have a CUDA device available, make sure to set your model on a GPU device in order to run your model.")
        elif device_map is not None:
            if isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()): raise ValueError("You are attempting to load an EETQ model with a device_map that contains a CPU or disk device. This is not supported. Please remove the CPU or disk device from the device_map.")
    def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
        if torch_dtype is None:
            torch_dtype = torch.float16
            logger.info("Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to requirements of `eetq` to enable model loading in 8-bit. Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass torch_dtype=torch.float16 to remove this warning.", torch_dtype)
        elif torch_dtype != torch.float16: logger.info("We suggest you to set `torch_dtype=torch.float16` for better efficiency with EETQ.")
        return torch_dtype
    def check_quantized_param(self, model: "PreTrainedModel", param_value: "torch.Tensor", param_name: str, state_dict: Dict[str, Any], **kwargs):
        from eetq import EetqLinear
        module, tensor_name = get_module_from_name(model, param_name)
        if isinstance(module, EetqLinear):
            if self.pre_quantized or tensor_name == "bias":
                if tensor_name == "weight" and param_value.dtype != torch.int8: raise ValueError("Expect quantized weights but got an unquantized weight")
                return False
            else:
                if tensor_name == "weight_scale": raise ValueError("Expect unquantized weights but got a quantized weight_scale")
                return True
        return False
    def create_quantized_param(self, model: "PreTrainedModel", param_value: "torch.Tensor", param_name: str, target_device: "torch.device", state_dict: Dict[str, Any], unexpected_keys: Optional[List[str]] = None):
        from eetq import quantize_and_preprocess_weights
        module, tensor_name = get_module_from_name(model, param_name)
        new_value, weight_scale = quantize_and_preprocess_weights(param_value)
        module._buffers[tensor_name] = new_value.to(target_device)
        module.register("weight_scales", weight_scale.to(target_device))
    def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs): return model
    def _process_model_before_weight_loading(self, model: "PreTrainedModel", device_map, keep_in_fp32_modules: List[str] = [], **kwargs):
        from ..integrations import get_keys_to_not_convert, replace_with_eetq_linear
        self.modules_to_not_convert = get_keys_to_not_convert(model)
        if self.quantization_config.modules_to_not_convert is not None: self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
        model = replace_with_eetq_linear(model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config, pre_quantized=self.pre_quantized)
        model.config.quantization_config = self.quantization_config
    @property
    def is_serializable(self): return True
    @property
    def is_trainable(self) -> bool: return True
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
