import copy
import logging
import os
from os.path import isdir, join
from typing import Dict, List, Optional, Union

import accelerate
import torch
import torch.nn as nn
import transformers
from accelerate.hooks import remove_hook_from_module
from safetensors import safe_open
from safetensors.torch import load_file as safe_load
from safetensors.torch import save_file as safe_save
from tqdm import tqdm
from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel
from transformers.modeling_utils import no_init_weights
from transformers.utils.generic import ContextManagers
from transformers.utils.hub import (
    CommitOperationAdd,
    PushToHubMixin,
    create_commit,
    create_repo,
)

from ..nn_modules._fused_base import FusedBaseAttentionModule, FusedBaseMLPModule
from ..nn_modules.qlinear import GeneralQuantLinear
from ..quantization import GPTQ, BaseQuantizeConfig
from ..quantization.config import (
    CHECKPOINT_FORMAT,
    CHECKPOINT_FORMAT_FIELD,
    QUANT_METHOD_FIELD,
    QUANTIZE_BLACK_LIST,
)
from ..utils.accelerate_utils import load_checkpoint_in_model
from ..utils.data_utils import collate_data
from ..utils.import_utils import (
    AUTOGPTQ_CUDA_AVAILABLE,
    EXLLAMA_KERNELS_AVAILABLE,
    EXLLAMAV2_KERNELS_AVAILABLE,
    MARLIN_AVAILABLE,
    QIGEN_AVAILABLE,
    TRITON_AVAILABLE,
    dynamically_import_QuantLinear,
)
from ..utils.marlin_utils import (
    _validate_marlin_compatibility,
    _validate_marlin_device_support,
    prepare_model_for_marlin_load,
)
from ._const import CPU, CUDA_0, SUPPORTED_MODELS
from ._utils import (
    autogptq_post_init,
    find_layers,
    get_checkpoints,
    get_device,
    get_module_by_name_prefix,
    get_module_by_name_suffix,
    make_quant,
    make_sure_no_tensor_in_meta_device,
    move_to_device,
    pack_from_tensors,
    pack_model,
    preprocess_checkpoint_qigen,
    simple_dispatch_model,
    unpack_awq,
)


logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(message)s")
handler.setFormatter(formatter)
logger.propagate = False
logger.addHandler(handler)
logger.setLevel(logging.INFO)


def nested_move_to_device(v, device):
    if isinstance(v, torch.Tensor):
        return move_to_device(v, device)
    elif isinstance(v, (list, tuple)):
        return type(v)([nested_move_to_device(e, device) for e in v])
    else:
        return v


class BaseGPTQForCausalLM(nn.Module, PushToHubMixin):
    layer_type: str = None
    layers_block_name: str = None
    outside_layer_modules: List[str] = None
    inside_layer_modules: List[List[str]] = None
    lm_head_name: str = "lm_head"

    fused_attn_module_type: Optional[FusedBaseAttentionModule] = None
    fused_mlp_module_type: Optional[FusedBaseMLPModule] = None

    def __init__(
        self,
        model: PreTrainedModel,
        quantized: bool,
        quantize_config: BaseQuantizeConfig,
        is_triton_backend: bool = False,
        injected_fused_attention: bool = False,
        injected_fused_mlp: bool = False,
        trainable: bool = False,
    ):
        super().__init__()

        self.model = model
        self.model_type = self.model.config.model_type
        self._quantized = quantized
        self.quantize_config = quantize_config
        self.config = self.model.config

        self.is_triton_backend = is_triton_backend
        self.injected_fused_attention = injected_fused_attention
        self.injected_fused_mlp = injected_fused_mlp
        self.trainable = trainable

    @property
    def quantized(self):
        return self._quantized

    @property
    def hf_device_map(self):
        return getattr(self.model, "hf_device_map", None)

    def _prepare_examples_for_quantization(
        self,
        examples: List[Dict[str, Union[List[int], torch.LongTensor]]],
        batch_size: int = 1,
    ):
        def _convert_tensor_to_list(tensor):
            if isinstance(tensor, torch.Tensor):
                if len(tensor.shape) == 1:
                    tensor = tensor.unsqueeze(0)
                tensor = tensor.long()
                return tensor.cpu().numpy().tolist()
            return [tensor]

        new_examples = []
        for example in examples:
            input_ids = _convert_tensor_to_list(example["input_ids"])
            attention_mask = _convert_tensor_to_list(example["attention_mask"])
            if "labels" in example:
                labels = _convert_tensor_to_list(example["labels"])
            elif "label" in example:
                labels = _convert_tensor_to_list(example["label"])
            elif "label_ids" in example:
                labels = _convert_tensor_to_list(example["label_ids"])
            else:
                labels = copy.deepcopy(input_ids)
            new_examples.append(
                {
                    "input_ids": input_ids,
                    "attention_mask": attention_mask,
                    "labels": labels,
                }
            )
        pad_token_id = self.config.pad_token_id
        if not pad_token_id:
            pad_token_id = self.config.eos_token_id

        new_examples = [
            collate_data(new_examples[start : start + batch_size], pad_token_id)
            for start in range(0, len(new_examples), batch_size)
        ]
        for new_example in new_examples:
            del new_example["labels"]

        return new_examples

    @torch.inference_mode()
    def quantize(
        self,
        examples: List[Dict[str, Union[List[int], torch.LongTensor]]],
        in_scales,
        in_zeros,
        in_g_idxes,
        in_bits,
        batch_size: int = 1,
        use_triton: bool = False,
        use_cuda_fp16: bool = True,
        autotune_warmup_after_quantized: bool = False,
        cache_examples_on_gpu: bool = True,
    ):
        if self.quantized:
            raise EnvironmentError("can't execute quantize because the model is quantized.")

        if self.quantize_config.quant_method in QUANTIZE_BLACK_LIST:
            raise ValueError(f"Unsupported quantization operation for quant method: {self.quantize_config.quant_method}")

        # alert users to limit threads so packing performance does not regress by up to ~100x
        thread_warning = """If you have not already done so, please inject the following code at the very top of your 
quantization script so the packing stage is optimized for speed. Using too many cores may reduce packing performance.
----
import os
import math
max_threads = str(min(8, os.cpu_count()))
os.environ['OMP_NUM_THREADS'] = max_threads
os.environ['OPENBLAS_NUM_THREADS'] = max_threads
os.environ['MKL_NUM_THREADS'] = max_threads
os.environ['VECLIB_MAXIMUM_THREADS'] = max_threads
os.environ['NUMEXPR_NUM_THREADS'] = max_threads
os.environ['NUMEXPR_MAX_THREADS'] = max_threads
----
"""
        # logger.warning(thread_warning)

        # if use_triton and not TRITON_AVAILABLE:
        #     logger.warning("triton is not installed, reset use_triton to False")
        #     use_triton = False

        device_map = self.hf_device_map
        if device_map:
            for name, device in device_map.items():
                if device == "cpu":
                    logger.info(f"truly offloading {name} to cpu with hook.")
                    module = get_module_by_name_suffix(self.model, name)
                    remove_hook_from_module(module, recurse=True)
                    accelerate.cpu_offload_with_hook(module, CUDA_0)

        layer_inputs = []
        attention_masks = []
        position_ids = []
        layer_input_kwargs = []
        layer_outputs = []

        examples = self._prepare_examples_for_quantization(examples, batch_size)

        forward_pass_use_cache = self.model.config.use_cache
        self.model.config.use_cache = False

        num_batches = len(examples)
        layers = get_module_by_name_prefix(self.model, self.layers_block_name)

        cur_layer_device = get_device(layers[0])
        data_device = cur_layer_device if cache_examples_on_gpu else CPU
        # def store_input_hook(_, args, kwargs):
        #     # Positional arguments.
        #     layer_input = []
        #     for inp in args:
        #         layer_input.append(move_to_device(inp, data_device))
        #     layer_inputs.append(layer_input)

        #     # Keyword arguments.
        #     if kwargs["attention_mask"] is not None:
        #         attention_masks.append(kwargs["attention_mask"].to(data_device))
        #     else:
        #         attention_masks.append(None)

        #     pos_ids = kwargs.get("position_ids", None)
        #     if pos_ids is not None:
        #         position_ids.append(move_to_device(pos_ids, data_device))
        #     one_kwargs = {}
        #     for (
        #         k,
        #         v,
        #     ) in kwargs.items():  # make sure other arguments also be captured
        #         if k not in ["hidden_states", "attention_mask", "position_ids"]:
        #             one_kwargs[k] = nested_move_to_device(v, data_device)
        #     layer_input_kwargs.append(one_kwargs)
        #     raise ValueError

        force_layer_back_to_cpu = False
        if get_device(layers[0]) == CPU:
            layers[0] = layers[0].to(CUDA_0)
            force_layer_back_to_cpu = True

        # ori_outside_layer_module_devices = {}
        # for module_name in self.outside_layer_modules:
        #     module = get_module_by_name_prefix(self.model, module_name)

        #     if module is None:
        #         continue

        #     ori_outside_layer_module_devices[module_name] = get_device(module)
        #     if module is not None:
        #         move_to_device(module, cur_layer_device)

        # # TODO: make this optional, backporting https://github.com/huggingface/optimum/blob/main/optimum/gptq/quantizer.py
        # handle = layers[0].register_forward_pre_hook(store_input_hook, with_kwargs=True)
        # for example in examples:
        #     for k, v in example.items():
        #         if len(v.shape) == 1:
        #             v = v.unsqueeze(0)
        #         example[k] = move_to_device(v, cur_layer_device)
        #     try:
        #         self.model(**example)
        #     except ValueError:
        #         pass
        # handle.remove()

        # move_to_device(layers[0], CPU if force_layer_back_to_cpu else cur_layer_device)
        # for module_name in self.outside_layer_modules:
        #     module = get_module_by_name_prefix(self.model, module_name)
        #     if module is not None:
        #         move_to_device(module, ori_outside_layer_module_devices[module_name])

        # torch.cuda.empty_cache()

        # inside_layer_modules = self.inside_layer_modules
        # if not self.quantize_config.true_sequential:
        #     inside_layer_modules = [sum(inside_layer_modules, [])]
        # quantizers = {}
        # for i in range(len(layers)):
        #     logger.info(f"Start quantizing layer {i + 1}/{len(layers)}")
        #     layer = layers[i]
        #     force_layer_back_to_cpu = False
        #     if get_device(layer) == CPU:
        #         move_to_device(layer, CUDA_0)
        #         force_layer_back_to_cpu = True
        #     cur_layer_device = get_device(layer)

        #     full = find_layers(layer)
        #     for names in inside_layer_modules:
        #         subset = {n: full[n] for n in names if n in full}
        #         gptq = {}
        #         for name in subset:
        #             gptq[name] = GPTQ(subset[name])
        #             gptq[name].quantizer.configure(
        #                 self.quantize_config.bits,
        #                 perchannel=True,
        #                 sym=self.quantize_config.sym,
        #                 mse=False,
        #             )

        #         def add_batch(name):
        #             def tmp(_, inp, out):
        #                 # gptq is mutable.
        #                 gptq[name].add_batch(inp[0].data, out.data)  # noqa: F821

        #             return tmp

        #         handles = []
        #         for name in subset:
        #             handles.append(subset[name].register_forward_hook(add_batch(name)))
        #         for j in range(num_batches):
        #             layer_input = []
        #             for k, layer_inp in enumerate(layer_inputs[j]):
        #                 layer_input.append(move_to_device(layer_inp, cur_layer_device))

        #             layer_attention_mask = move_to_device(attention_masks[j], cur_layer_device)
        #             additional_layer_inputs = {"attention_mask": layer_attention_mask}
        #             layer_position_ids = (
        #                 None if not position_ids else move_to_device(position_ids[j], cur_layer_device)
        #             )
        #             if layer_position_ids is not None:
        #                 additional_layer_inputs["position_ids"] = layer_position_ids
        #             for k, v in layer_input_kwargs[j].items():
        #                 additional_layer_inputs[k] = nested_move_to_device(v, cur_layer_device)
        #             layer(*layer_input, **additional_layer_inputs)
        #         for h in handles:
        #             h.remove()

        #         for name in subset:
        #             logger.info(f"Quantizing {name} in layer {i + 1}/{len(layers)}...")
        #             scale, zero, g_idx = gptq[name].fasterquant(
        #                 percdamp=self.quantize_config.damp_percent,
        #                 group_size=self.quantize_config.group_size,
        #                 actorder=self.quantize_config.desc_act,
        #                 static_groups=self.quantize_config.static_groups,
        #             )
        #             quantizers[f"{self.layers_block_name}.{i}.{name}"] = (
        #                 gptq[name].quantizer.to(CPU if force_layer_back_to_cpu else cur_layer_device),
        #                 move_to_device(scale, CPU if force_layer_back_to_cpu else cur_layer_device),
        #                 move_to_device(zero, CPU if force_layer_back_to_cpu else cur_layer_device),
        #                 move_to_device(g_idx, CPU if force_layer_back_to_cpu else cur_layer_device),
        #             )
        #             gptq[name].free()

        #     for j in range(num_batches):
        #         layer_input = []
        #         for k, layer_inp in enumerate(layer_inputs[j]):
        #             layer_input.append(move_to_device(layer_inp, cur_layer_device))

        #         layer_attention_mask = move_to_device(attention_masks[j], cur_layer_device)
        #         additional_layer_inputs = {"attention_mask": layer_attention_mask}
        #         layer_position_ids = None if not position_ids else move_to_device(position_ids[j], cur_layer_device)
        #         if layer_position_ids is not None:
        #             additional_layer_inputs["position_ids"] = layer_position_ids
        #         for k, v in layer_input_kwargs[j].items():
        #             additional_layer_inputs[k] = nested_move_to_device(v, cur_layer_device)
        #         layer_output = move_to_device(
        #             layer(*layer_input, **additional_layer_inputs)[0],
        #             cur_layer_device if cache_examples_on_gpu else CPU,
        #         )
        #         layer_outputs.append([layer_output])

        #     layers[i] = move_to_device(layer, CPU if force_layer_back_to_cpu else cur_layer_device)
        #     del layer
        #     del gptq
        #     del layer_inputs
        #     layer_inputs, layer_outputs = layer_outputs, []  # TODO: is it really OK to cache only the first positional argument?
        #     torch.cuda.empty_cache()

        quantizers = {}
        qweight_heights = {}
        inside_layer_modules = self.inside_layer_modules
        if not self.quantize_config.true_sequential:
            inside_layer_modules = [sum(inside_layer_modules, [])]
        for i in range(len(layers)):
            logger.info(f"Start quantizing layer {i + 1}/{len(layers)}")
            layer = layers[i]
            force_layer_back_to_cpu = False
            if get_device(layer) == CPU:
                # move_to_device(layer, CUDA_0)
                force_layer_back_to_cpu = True
            cur_layer_device = get_device(layer)

            full = find_layers(layer)
            subset = {}
            for names in inside_layer_modules:
                subset.update({n: full[n] for n in names if n in full})
            
            for name in subset:
                logger.info(f"Quantizing {name} in layer {i + 1}/{len(layers)}...")
                # scale zero g_idx from outside
                scale = in_scales['model.layers.%d.%s' % (i, name)]
                zero = in_zeros['model.layers.%d.%s' % (i, name)]
                g_idx = in_g_idxes['model.layers.%d.%s' % (i, name)]
                block_bit = in_bits['model.layers.%d.%s' % (i, name)]
                quantizers[f"{self.layers_block_name}.{i}.{name}"] = (
                    None,
                    move_to_device(scale, CPU if force_layer_back_to_cpu else cur_layer_device),
                    move_to_device(zero, CPU if force_layer_back_to_cpu else cur_layer_device),
                    move_to_device(g_idx, CPU if force_layer_back_to_cpu else cur_layer_device),
                    move_to_device(block_bit, CPU if force_layer_back_to_cpu else cur_layer_device),
                )
                qweight_height = 0
                for bit in block_bit.to(CPU):
                    bit = bit.item()
                    if bit == 1:
                        bit = 2
                    qweight_height += self.quantize_config.group_size // 32 * bit
                qweight_heights[f"{self.layers_block_name}.{i}.{name}"] = qweight_height
        self.quantize_config.qweight_heights = qweight_heights

        pack_model(
            model=self.model,
            quantizers=quantizers,
            bits=self.quantize_config.bits,
            group_size=self.quantize_config.group_size,
            qweight_heights=self.quantize_config.qweight_heights,
            use_triton=use_triton,
            use_cuda_fp16=use_cuda_fp16,
            desc_act=self.quantize_config.desc_act,
            warmup_triton=autotune_warmup_after_quantized,
            force_layer_back_to_cpu=force_layer_back_to_cpu,
            use_marlin=self.quantize_config.checkpoint_format == CHECKPOINT_FORMAT.MARLIN,
        )
        if device_map:
            self.model = remove_hook_from_module(self.model, recurse=True)
            self.model = simple_dispatch_model(self.model, device_map)
        self.model.config.use_cache = forward_pass_use_cache

        self._quantized = True

        torch.cuda.empty_cache()

    @property
    def device(self):
        if not self.hf_device_map:
            return self.model.device
        else:
            device = [d for d in self.hf_device_map.values() if d not in {"disk"}][0]
            return torch.device(device)

    def to(self, device: Union[str, torch.device]):
        self.model.to(device)
        return self

    def forward(self, *args, **kwargs):
        return self.model(*args, **kwargs)

    def generate(self, **kwargs):
        """shortcut for model.generate"""
        with torch.inference_mode(), torch.amp.autocast(device_type=self.device.type):
            return self.model.generate(**kwargs)

    def prepare_inputs_for_generation(self, *args, **kwargs):
        """shortcut for model.prepare_inputs_for_generation"""
        return self.model.prepare_inputs_for_generation(*args, **kwargs)

    def push_to_hub(
        self,
        repo_id: str,
        save_dir: Optional[str] = None,
        use_safetensors: Optional[bool] = True,
        safetensors_metadata: Optional[Dict[str, str]] = None,
        commit_message: Optional[str] = "Upload of AutoGPTQ quantized model",
        use_auth_token: Optional[Union[bool, str]] = None,
        private: Optional[bool] = None,
        token: Optional[Union[bool, str]] = None,
        create_pr: Optional[bool] = False,
    ) -> str:
        """
        Upload the model to the Hugging Face Hub.

        Parameters:
            repo_id (`str`):
                The name of the repository you want to push your tool to. It should contain your organization name when
                pushing to a given organization.
            save_dir (`str`, *optional*):
                The name of the local folder to save the model to.
                If the model has already been saved, this parameter can be omitted.
            use_safetensors (`bool`, *optional*):
                Save the model using `safetensors`.
                If the model has already been saved, this parameter can be omitted.
            safetensors_metadata: (`dict`, *optional*, defaults to `None`):
                Pass optional metadata dictionary to be saved in the `safetensors` model file(s).
                Metadata is optional and is purely for informational purposes. It does not affect inference.
                If `None`, no metadata will be saved.
            commit_message (`str`, *optional*, defaults to `"Upload tool"`):
                Message to commit while pushing.
            use_auth_token (`bool` or `str`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`
                is not specified.
            private (`bool`, *optional*):
                Whether or not the repository created should be private.
            token (`bool` or `str`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated
                when running `huggingface-cli login` (stored in `~/.huggingface`).
            create_pr (`bool`, *optional*, defaults to `False`):
                Whether or not to create a PR with the uploaded files or directly commit.
        """
        if (
            self.quantize_config.model_name_or_path is None or not isdir(self.quantize_config.model_name_or_path)
        ) and save_dir is None:
            raise ValueError(
                "Quantized model should be saved first, or you can provide save_dir to make sure model is saved to local disk before uploading."
            )

        if save_dir is not None:
            logger.info(f"Saving model to {save_dir}")
            self.save_quantized(save_dir, use_safetensors, safetensors_metadata)

        repo_url = create_repo(
            repo_id=repo_id,
            token=token,
            private=private,
            exist_ok=True,
            repo_type="model",
        )
        repo_id = repo_url.repo_id

        if self.quantize_config.model_name_or_path is not None:
            work_dir = self.quantize_config.model_name_or_path
            operations = [
                CommitOperationAdd(path_or_fileobj=join(work_dir, f), path_in_repo=f) for f in os.listdir(work_dir)
            ]
            logger.info(f"Uploading the following files to {repo_id}: {','.join(os.listdir(work_dir))}")
            return create_commit(
                repo_id=repo_id,
                operations=operations,
                commit_message=commit_message,
                token=use_auth_token,
                create_pr=create_pr,
                repo_type="model",
            )

    def save_quantized(
        self,
        save_dir: str,
        use_safetensors: bool = True,
        safetensors_metadata: Optional[Dict[str, str]] = None,
    ):
        """save quantized model and configs to local disk"""
        os.makedirs(save_dir, exist_ok=True)

        if not self.quantized:
            raise EnvironmentError("can only save quantized model, please execute .quantize first.")

        self.model.to(CPU)

        model_base_name = (
            self.quantize_config.model_file_base_name
            or f"gptq_model-{self.quantize_config.bits}bit-{self.quantize_config.group_size}g"
        )
        if use_safetensors:
            model_save_name = model_base_name + ".safetensors"
            state_dict = self.model.state_dict()
            state_dict = {k: v.clone().contiguous() for k, v in state_dict.items()}
            if safetensors_metadata is None:
                safetensors_metadata = {}
            elif not isinstance(safetensors_metadata, dict):
                raise TypeError("safetensors_metadata must be a dictionary.")
            else:
                logger.debug(f"Received safetensors_metadata: {safetensors_metadata}")
                new_safetensors_metadata = {}
                converted_keys = False
                for key, value in safetensors_metadata.items():
                    if not isinstance(key, str) or not isinstance(value, str):
                        converted_keys = True
                        try:
                            new_key = str(key)
                            new_value = str(value)
                        except Exception as e:
                            raise TypeError(
                                f"safetensors_metadata: both keys and values must be strings and an error occured when trying to convert them: {e}"
                            )
                        if new_key in new_safetensors_metadata:
                            logger.warning(
                                f"After converting safetensors_metadata keys to strings, the key '{new_key}' is duplicated. Ensure that all your metadata keys are strings to avoid overwriting."
                            )
                        new_safetensors_metadata[new_key] = new_value
                safetensors_metadata = new_safetensors_metadata
                if converted_keys:
                    logger.debug(
                        f"One or more safetensors_metadata keys or values had to be converted to str(). Final safetensors_metadata: {safetensors_metadata}"
                    )

            # Format is required to enable Accelerate to load the metadata
            # otherwise it raises an OSError
            safetensors_metadata["format"] = "pt"

            # Store the quantization configuration as safetensors metadata
            from auto_gptq import __version__

            safetensors_metadata["auto_gptq_version"] = str(__version__)
            safetensors_metadata["gptq_bits"] = str(self.quantize_config.bits)
            safetensors_metadata["gptq_group_size"] = str(self.quantize_config.group_size)
            safetensors_metadata["gptq_desc_act"] = str(self.quantize_config.desc_act)
            safetensors_metadata["gptq_damp_percent"] = str(self.quantize_config.damp_percent)
            safetensors_metadata["gptq_" + CHECKPOINT_FORMAT_FIELD] = self.quantize_config.checkpoint_format
            safetensors_metadata["gptq_" + QUANT_METHOD_FIELD] = self.quantize_config.quant_method

            safe_save(state_dict, join(save_dir, model_save_name), safetensors_metadata)
        else:
            model_save_name = model_base_name + ".bin"
            torch.save(self.model.state_dict(), join(save_dir, model_save_name))

        self.model.config.quantization_config = self.quantize_config.to_dict()
        self.model.config.save_pretrained(save_dir)
        self.quantize_config.save_pretrained(save_dir)
        self.quantize_config.model_name_or_path = save_dir
        self.quantize_config.model_file_base_name = model_base_name

    def save_pretrained(
        self,
        save_dir: str,
        use_safetensors: bool = True,
        safetensors_metadata: Optional[Dict[str, str]] = None,
        **kwargs,
    ):
        """alias of save_quantized"""
        logger.warning("you are using save_pretrained, which will re-direct to save_quantized.")
        self.save_quantized(save_dir, use_safetensors, safetensors_metadata)

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: str,
        quantize_config: BaseQuantizeConfig,
        max_memory: Optional[dict] = None,
        trust_remote_code: bool = False,
        torch_dtype: torch.dtype = torch.float16,
        **model_init_kwargs,
    ):
        """load un-quantized pretrained model to cpu"""

        if not torch.cuda.is_available():
            raise EnvironmentError("Load pretrained model to do quantization requires CUDA available.")

        def skip(*args, **kwargs):
            pass

        torch.nn.init.kaiming_uniform_ = skip
        torch.nn.init.uniform_ = skip
        torch.nn.init.normal_ = skip

        # Parameters related to loading from Hugging Face Hub
        cache_dir = model_init_kwargs.pop("cache_dir", None)
        force_download = model_init_kwargs.pop("force_download", False)
        resume_download = model_init_kwargs.pop("resume_download", False)
        proxies = model_init_kwargs.pop("proxies", None)
        local_files_only = model_init_kwargs.pop("local_files_only", False)
        use_auth_token = model_init_kwargs.pop("use_auth_token", None)
        revision = model_init_kwargs.pop("revision", None)
        subfolder = model_init_kwargs.pop("subfolder", "")
        commit_hash = model_init_kwargs.pop("_commit_hash", None)

        cached_file_kwargs = {
            "cache_dir": cache_dir,
            "force_download": force_download,
            "proxies": proxies,
            "resume_download": resume_download,
            "local_files_only": local_files_only,
            "use_auth_token": use_auth_token,
            "revision": revision,
            "subfolder": subfolder,
            "_commit_hash": commit_hash,
        }

        config = AutoConfig.from_pretrained(
            pretrained_model_name_or_path, trust_remote_code=True, **cached_file_kwargs
        )
        if config.model_type not in SUPPORTED_MODELS:
            raise TypeError(f"{config.model_type} isn't supported yet.")

        # enforce some values despite user specified
        model_init_kwargs["torch_dtype"] = torch_dtype
        model_init_kwargs["trust_remote_code"] = trust_remote_code
        if max_memory:
            if "disk" in max_memory:
                raise NotImplementedError("disk offload not support yet.")
            with accelerate.init_empty_weights():
                model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
            model.tie_weights()

            max_memory = accelerate.utils.get_balanced_memory(
                model,
                max_memory=max_memory,
                no_split_module_classes=[cls.layer_type],
                dtype=model_init_kwargs["torch_dtype"],
                low_zero=False,
            )
            model_init_kwargs["device_map"] = accelerate.infer_auto_device_map(
                model,
                max_memory=max_memory,
                no_split_module_classes=[cls.layer_type],
                dtype=model_init_kwargs["torch_dtype"],
            )
            model_init_kwargs["low_cpu_mem_usage"] = True

            del model
        else:
            model_init_kwargs["device_map"] = None
            model_init_kwargs["low_cpu_mem_usage"] = False

        torch.cuda.empty_cache()

        merged_kwargs = {**model_init_kwargs, **cached_file_kwargs}
        model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, **merged_kwargs)

        model_config = model.config.to_dict()
        seq_len_keys = ["max_position_embeddings", "seq_length", "n_positions"]
        if any(k in model_config for k in seq_len_keys):
            for key in seq_len_keys:
                if key in model_config:
                    model.seqlen = model_config[key]
                    break
        else:
            logger.warning("can't get model's sequence length from model config, will set to 4096.")
            model.seqlen = 4096
        model.eval()

        return cls(model, False, quantize_config)

    @classmethod
    def from_quantized(
        cls,
        model_name_or_path: Optional[str],
        device_map: Optional[Union[str, Dict[str, Union[int, str]]]] = None,
        max_memory: Optional[dict] = None,
        device: Optional[Union[str, int]] = None,
        low_cpu_mem_usage: bool = False,
        use_triton: bool = False,
        use_qigen: bool = False,
        use_marlin: bool = False,
        torch_dtype: Optional[torch.dtype] = None,
        inject_fused_attention: bool = False,
        inject_fused_mlp: bool = False,
        use_cuda_fp16: bool = True,
        quantize_config: Optional[BaseQuantizeConfig] = None,
        model_basename: Optional[str] = None,
        use_safetensors: bool = True,
        trust_remote_code: bool = False,
        warmup_triton: bool = False,
        trainable: bool = False,
        disable_exllama: Optional[bool] = None,
        disable_exllamav2: bool = False,
        use_tritonv2: bool = False,
        checkpoint_format: Optional[str] = None,
        **kwargs,
    ):
        """load quantized model from local disk"""
        # If disable_exllamav2 is True, we want to fall back on the exllama kernel and not the cuda/cuda_old ones.
        if disable_exllama is None:
            if disable_exllamav2:
                disable_exllama = False
            else:
                disable_exllama = True

        # Parameters related to loading from Hugging Face Hub
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", "")
        commit_hash = kwargs.pop("_commit_hash", None)

        cached_file_kwargs = {
            "cache_dir": cache_dir,
            "force_download": force_download,
            "proxies": proxies,
            "resume_download": resume_download,
            "local_files_only": local_files_only,
            "use_auth_token": use_auth_token,
            "revision": revision,
            "subfolder": subfolder,
            "_raise_exceptions_for_missing_entries": False,
            "_commit_hash": commit_hash,
        }
        if use_qigen and not QIGEN_AVAILABLE:
            logger.warning("Qigen is not installed, reset use_qigen to False.")
            use_qigen = False
        if use_triton and use_tritonv2:
            logging.warn(
                "Both use_triton and use_tritonv2 are set to True. Defaulting to use_triton"
            )
            use_tritonv2 = False
        if (use_triton or use_tritonv2) and not TRITON_AVAILABLE:
            logger.warning("Triton is not installed, reset use_triton to False.")
            use_triton = False
            use_tritonv2 = False
        if not disable_exllama and not EXLLAMA_KERNELS_AVAILABLE:
            logger.warning(
                "Exllama kernel is not installed, reset disable_exllama to True. "
                "This may because you installed auto_gptq using a pre-build wheel "
                "on Windows, in which exllama_kernels are not compiled. To use "
                "exllama_kernels to further speedup inference, you can re-install "
                "auto_gptq from source."
            )
            disable_exllama = True
        if not disable_exllamav2 and not EXLLAMAV2_KERNELS_AVAILABLE:
            logger.warning(
                "Exllamav2 kernel is not installed, reset disable_exllamav2 to True. "
                "This may because you installed auto_gptq using a pre-build wheel "
                "on Windows, in which exllama_kernels are not compiled. To use "
                "exllama_kernels to further speedup inference, you can re-install "
                "auto_gptq from source."
            )
            disable_exllamav2 = True
        if not AUTOGPTQ_CUDA_AVAILABLE:
            logger.warning(
                "CUDA kernels for auto_gptq are not installed, this will result in "
                "very slow inference speed. This may because:\n"
                "1. You disabled CUDA extensions compilation by setting BUILD_CUDA_EXT=0 when install auto_gptq from source.\n"
                "2. You are using pytorch without CUDA support.\n"
                "3. CUDA and nvcc are not installed in your device."
            )

        if use_qigen and QIGEN_AVAILABLE:
            logger.warning("QIgen is active. Ignores all settings related to cuda.")
            inject_fused_attention = False
            inject_fused_mlp = False
            use_triton = False
            disable_exllama = True
            disable_exllamav2 = True

        if not disable_exllamav2 and not disable_exllama:
            logger.warning(
                "You have activated both exllama and exllamav2 kernel. Setting disable_exllama to True and keeping disable_exllamav2 to False"
            )
            disable_exllama = True

        # == step1: prepare configs and file names == #
        config = AutoConfig.from_pretrained(
            model_name_or_path,
            trust_remote_code=trust_remote_code,
            **cached_file_kwargs,
        )

        if config.model_type not in SUPPORTED_MODELS:
            raise TypeError(f"{config.model_type} isn't supported yet.")

        if quantize_config is None:
            quantize_config = BaseQuantizeConfig.from_pretrained(model_name_or_path, checkpoint_format=checkpoint_format, **cached_file_kwargs, **kwargs)
        else:
            if not isinstance(quantize_config, BaseQuantizeConfig):
                quantize_config = BaseQuantizeConfig.from_quant_config(quantize_config, checkpoint_format)

        if quantize_config.checkpoint_format == CHECKPOINT_FORMAT.MARLIN:
            # format marlin requires marlin kernel
            use_marlin = True

        marlin_compatible, marlin_optimized = _validate_marlin_device_support()
        if use_marlin and (not MARLIN_AVAILABLE or not marlin_compatible):
            raise TypeError("use_marlin is true but Marlin is not availble due to cuda/device support.")
        elif use_marlin and not marlin_optimized:
            logger.info(
                "use_marlin is true and your gpu device is supported but not optimized for Marlin."
            )

        if not use_marlin and MARLIN_AVAILABLE:
            unsupported_reason = _validate_marlin_compatibility(quantize_config)
            if unsupported_reason is None and marlin_compatible:
                logger.info(
                    "You passed a model that is compatible with the Marlin int4*fp16 GPTQ kernel but use_marlin is False. We recommend using `use_marlin=True` to use the optimized Marlin kernels for inference. Example: `model = AutoGPTQForCausalLM.from_quantized(..., use_marlin=True)`."
                )

        if model_basename is None:
            if quantize_config.model_file_base_name:
                possible_model_basenames = [quantize_config.model_file_base_name]
            else:
                possible_model_basenames = [
                    f"gptq_model-{quantize_config.bits}bit-{quantize_config.group_size}g",
                    "model",
                ]
        else:
            possible_model_basenames = [model_basename]

        quantize_config.model_name_or_path = model_name_or_path

        extensions = []
        if use_safetensors:
            extensions.append(".safetensors")
        else:
            extensions += [".bin", ".pt"]

        model_name_or_path = str(model_name_or_path)

        # Retrieve (and if necessary download) the quantized checkpoint(s).
        is_sharded, resolved_archive_file, true_model_basename = get_checkpoints(model_name_or_path=model_name_or_path, extensions=extensions, possible_model_basenames=possible_model_basenames, **cached_file_kwargs)

        quantize_config.model_file_base_name = true_model_basename

        model_save_name = resolved_archive_file  # In case a model is sharded, this would be `model.safetensors.index.json` which may later break.

        if (not disable_exllama or not disable_exllamav2) and trainable:
            logger.warning(
                "QuantLinear with the exllama backend not does support the trainable mode yet, switching to cuda/cuda_old/triton backend."
            )
            disable_exllama = True
            disable_exllamav2 = True

        elif not (use_triton or use_tritonv2) and trainable:
            logger.warning(
                "QuantLinear with cuda backend not support trainable mode yet, Switch to the pytorch backend."
            )

        # == step2: convert model to gptq-model (replace Linear with QuantLinear) == #
        def skip(*args, **kwargs):
            pass

        if torch_dtype is None:
            if not use_qigen:
                torch_dtype = torch.float16
            else:
                torch_dtype = torch.float32

        if torch_dtype != torch.float16:
            logger.warning("Overriding use_cuda_fp16 to False since torch_dtype is not torch.float16.")
            use_cuda_fp16 = False

        if not use_qigen:
            torch.nn.init.kaiming_uniform_ = skip
            torch.nn.init.uniform_ = skip
            torch.nn.init.normal_ = skip

            transformers.modeling_utils._init_weights = False

            init_contexts = [no_init_weights()]
            if low_cpu_mem_usage:
                init_contexts.append(accelerate.init_empty_weights(include_buffers=False))

            with ContextManagers(init_contexts):
                model = AutoModelForCausalLM.from_config(
                    config, trust_remote_code=trust_remote_code, torch_dtype=torch_dtype
                )

                layers = find_layers(model)
                ignore_layers = [cls.lm_head_name] + cls.outside_layer_modules
                for name in list(layers.keys()):
                    if any(name.startswith(ignore_layer) for ignore_layer in ignore_layers) or all(
                        not name.endswith(ignore_layer)
                        for sublist in cls.inside_layer_modules
                        for ignore_layer in sublist
                    ):
                        logger.info(f"The layer {name} is not quantized.")
                        del layers[name]

                make_quant(
                    model,
                    layers,
                    quantize_config.bits,
                    quantize_config.group_size,
                    quantize_config.qweight_heights,
                    use_triton=use_triton,
                    disable_exllama=disable_exllama,
                    disable_exllamav2=disable_exllamav2,
                    use_cuda_fp16=use_cuda_fp16,
                    desc_act=quantize_config.desc_act,
                    trainable=trainable,
                    use_tritonv2=use_tritonv2,
                )
                model.tie_weights()

            # == step3: load checkpoint and dispatch == #
            if isinstance(device_map, str) and device_map not in [
                "auto",
                "balanced",
                "balanced_low_0",
                "sequential",
            ]:
                raise ValueError(
                    "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
                    "'sequential'."
                )
            if isinstance(device_map, dict):
                max_memory = None
            else:
                if device is None and not device_map and not max_memory:
                    device_map = "auto"
                if device is not None:
                    device = torch.device(device)
                    if not max_memory and not device_map:
                        device_map = {"": device.index if device.type == "cuda" else device.type}
                if not isinstance(device_map, dict) and device_map != "sequential":
                    max_memory = accelerate.utils.get_balanced_memory(
                        model=model,
                        max_memory=max_memory,
                        no_split_module_classes=[cls.layer_type],
                        low_zero=(device_map == "balanced_low_0"),
                    )
            if not isinstance(device_map, dict):
                device_map = accelerate.infer_auto_device_map(
                    model,
                    max_memory=max_memory,
                    no_split_module_classes=[cls.layer_type],
                )

            if low_cpu_mem_usage:
                make_sure_no_tensor_in_meta_device(
                    model,
                    use_triton,
                    quantize_config.desc_act,
                    quantize_config.group_size,
                    bits=quantize_config.bits,
                    disable_exllama=disable_exllama,
                    disable_exllamav2=disable_exllamav2,
                    use_tritonv2=use_tritonv2,
                )

            # TODO: move this logic in an awq_utils.py file.
            if quantize_config.checkpoint_format == CHECKPOINT_FORMAT.AWQ_GEMM:
                if is_sharded:
                    raise ValueError("The loading of sharded checkpoints with AWQ checkpoints is currently not supported. Please raise an issue in AutoGPTQ repository.")

                if use_marlin:
                    raise ValueError(
                        "Tried to load an AWQ model with use_marlin=True. This is currently not supported. Please open an issue in AutoGPTQ repository."
                    )

                model_cache_name, is_cached = quantize_config.get_cache_file_path()

                if is_cached:
                    model_save_name = model_cache_name
                    logger.info(f"Loading an AWQ model, detected a cached repacked weight at {model_save_name}.")
                else:
                    logger.info(
                        "Loading an AWQ model. This requires repacking the weights, and no cached repacked checkpoint was found. Grab a coffee!"
                    )

                    if "safetensors" not in model_save_name:
                        raise NotImplementedError(
                            f"Conversion from AWQ checkpoints is implemented only for safetensors checkpoints, found {model_save_name}"
                        )
                    if quantize_config.bits != 4:
                        raise NotImplementedError(
                            f"Conversion from AWQ checkpoints is supported only for 4 bits models. Found {quantize_config.bits} bits."
                        )
                    gptq_layers = set()
                    non_gptq_params = set()
                    with safe_open(model_save_name, framework="pt") as f:
                        for state_dict_key in f.keys():
                            if (
                                "qweight" not in state_dict_key
                                and "qzeros" not in state_dict_key
                                and "scales" not in state_dict_key
                            ):
                                non_gptq_params.add(state_dict_key)
                                continue

                            # e.g. prefix "model.layers.3.self_attn.k_proj"
                            prefix, _ = state_dict_key.rsplit(".", 1)
                            gptq_layers.add(prefix)

                        new_state_dict = {}

                        for state_dict_key in non_gptq_params:
                            new_state_dict[state_dict_key] = f.get_tensor(state_dict_key)

                        gptq_layers = sorted(gptq_layers)
                        max_layer_name_length = len(max(gptq_layers, key=len))
                        pbar = tqdm(gptq_layers)
                        i = 0
                        for gptq_layer_name in pbar:
                            i += 1
                            desc = f"Unpacking {gptq_layer_name} + '...'"
                            desc = desc + " " * (max_layer_name_length - len(desc))

                            awq_qweight = f.get_tensor(gptq_layer_name + ".qweight")
                            awq_qzeros = f.get_tensor(gptq_layer_name + ".qzeros")
                            awq_scales = f.get_tensor(gptq_layer_name + ".scales")

                            # TODO: add FAST unpacking.
                            unpacked_qweight, unpacked_qzeros = unpack_awq(
                                awq_qweight,
                                awq_qzeros,
                                awq_scales,
                                bits=quantize_config.bits,
                                group_size=quantize_config.group_size,
                            )

                            # TODO: add FAST repacking, this is too slow.
                            desc = f"Repacking {gptq_layer_name}..."
                            desc = desc + " " * (max_layer_name_length + 12 - len(desc))
                            pbar.set_description(desc)
                            gptq_qweight, gptq_qzeros = pack_from_tensors(
                                unpacked_qweight,
                                unpacked_qzeros,
                                awq_scales,
                                bits=quantize_config.bits,
                                group_size=quantize_config.group_size,
                            )

                            new_state_dict[gptq_layer_name + ".qweight"] = gptq_qweight
                            new_state_dict[gptq_layer_name + ".qzeros"] = gptq_qzeros
                            new_state_dict[gptq_layer_name + ".scales"] = awq_scales

                        safe_save(new_state_dict, model_cache_name)
                        model_save_name = model_cache_name

            if use_marlin:
                if is_sharded:
                    raise ValueError("The loading of sharded checkpoints with Marlin is currently not supported. Please raise an issue in AutoGPTQ repository.")
                if torch.version.hip:
                    raise ValueError("Can not use Marlin int4*fp16 kernel with AMD ROCm version of PyTorch as the kernel is not compatible. Please do not use `use_marlin=True` when using ROCm devices.")
                if not _validate_marlin_device_support():
                    raise ValueError(f'Can not use Marlin int4*fp16 kernel with a device of compute capability {torch.cuda.get_device_capability()}, the minimum compute capability is 8.0 for Marlin kernel. Please do not use `use_marlin=True`, or please upgrade your GPU ("The more you buy, the more you save." - Taiwanese proverb).')

                # Validate the model can run in Marlin.
                if torch_dtype != torch.float16:
                    raise ValueError("Marlin kernel requires torch_dtype=torch.float16.")
                unsupported_reason = _validate_marlin_compatibility(quantize_config)
                if unsupported_reason is not None:
                    raise ValueError(
                        f"The model {model_name_or_path} can not be converted to use the Marlin kernel for the following reason: {unsupported_reason}, which is not supported by Marlin kernel."
                    )

                # Load the quant linear type we need.
                # TODO: load directy marlin with the right quantlinear class.
                quant_linear_class = dynamically_import_QuantLinear(
                    use_triton=use_triton,
                    desc_act=quantize_config.desc_act,
                    group_size=quantize_config.group_size,
                    bits=quantize_config.bits,
                    disable_exllama=disable_exllama,
                    disable_exllamav2=disable_exllamav2,
                    use_marlin=False,
                    use_tritonv2=use_tritonv2,  # Get the "original" QuantLienar class
                )

                # Prepare model for marlin load.
                #   If stub is marlin serialzed         --> load from directly
                #   If stub has cached marlin version   --> load from the cached versin
                #   Otherwise                           --> convert to marlin, cache, load from cache
                model, model_save_name = prepare_model_for_marlin_load(
                    model=model,
                    quantize_config=quantize_config,
                    quant_linear_class=quant_linear_class,
                    torch_dtype=torch_dtype,
                    current_model_save_name=model_save_name,
                    device_map=device_map,
                )

                # Disable incompatible optimizations.
                if inject_fused_attention or inject_fused_mlp:
                    # TODO: Validate whether that can be used.
                    logger.info("Disabling fused attention and mlp injection because Marlin kernel is used.")
                    inject_fused_attention = False
                    inject_fused_mlp = False

            load_checkpoint_in_model(
                model,
                dtype=torch_dtype,  # This is very hacky but works due to https://github.com/huggingface/accelerate/blob/bd72a5f1a80d5146554458823f8aeda0a9db5297/src/accelerate/utils/modeling.py#L292
                checkpoint=model_save_name,
                device_map=device_map,
                offload_state_dict=True,
                offload_buffers=True,
            )

            # TODO: Why are we using this custom function and not dispatch_model?
            model = simple_dispatch_model(model, device_map)
        else:
            # Using QiGen.

            if is_sharded:
                raise ValueError("The loading of sharded checkpoints with QiGen is currently not supported. Please raise an issue in AutoGPTQ repository.")

            if quantize_config.desc_act:
                NotImplementedError("desc_act=True is not yet supported with QiGen.")
            model = AutoModelForCausalLM.from_config(
                config, trust_remote_code=trust_remote_code, torch_dtype=torch_dtype
            )

            layers = find_layers(model)
            ignore_layers = [cls.lm_head_name] + cls.outside_layer_modules
            for name in list(layers.keys()):
                if any(name.startswith(ignore_layer) for ignore_layer in ignore_layers):
                    logger.info(f"{name} not been quantized, will be ignored when make_quant.")
                    del layers[name]

            if model_save_name.endswith(".safetensors"):
                checkpoint = safe_load(model_save_name)
            else:
                checkpoint = torch.load(model_save_name)
            make_quant(
                model,
                layers,
                quantize_config.bits,
                quantize_config.group_size,
                use_triton=use_triton,
                disable_exllama=disable_exllama,
                disable_exllamav2=disable_exllamav2,
                use_cuda_fp16=use_cuda_fp16,
                desc_act=quantize_config.desc_act,
                trainable=trainable,
                use_qigen=True,
                use_tritonv2=use_tritonv2,
                use_marlin=quantize_config.checkpoint_format == CHECKPOINT_FORMAT.MARLIN,
            )
            preprocess_checkpoint_qigen(
                model,
                layers,
                quantize_config.bits,
                quantize_config.group_size,
                checkpoint,
            )
            model.load_state_dict(checkpoint)

        # == step4: set seqlen == #
        model_config = model.config.to_dict()
        seq_len_keys = ["max_position_embeddings", "seq_length", "n_positions"]
        if any(k in model_config for k in seq_len_keys):
            for key in seq_len_keys:
                if key in model_config:
                    model.seqlen = model_config[key]
                    break
        else:
            logger.warning("can't get model's sequence length from model config, will set to 4096.")
            model.seqlen = 4096

        # == step5: (optional) inject optimized module == #
        if inject_fused_attention:
            if cls.fused_attn_module_type is None:
                inject_fused_attention = False
                logger.warning(f"{cls.__name__} hasn't fused attention module yet, will skip inject fused attention.")
            else:
                cls.fused_attn_module_type.inject_to_model(
                    model,
                    use_triton=use_triton,
                    group_size=quantize_config.group_size,
                    use_cuda_fp16=use_cuda_fp16,
                    desc_act=quantize_config.desc_act,
                    trainable=trainable,
                    bits=quantize_config.bits,
                    disable_exllama=disable_exllama,
                    disable_exllamav2=disable_exllamav2,
                    use_tritonv2=use_tritonv2,
                )
        if inject_fused_mlp:
            if cls.fused_mlp_module_type is None:
                inject_fused_mlp = False
                logger.warning(f"{cls.__name__} hasn't fused mlp module yet, will skip inject fused mlp.")
            else:
                cls.fused_mlp_module_type.inject_to_model(model, use_triton=use_triton)

        # Any post-initialization that require device information, for example buffers initialization on device.
        model = autogptq_post_init(model, use_act_order=quantize_config.desc_act)

        model.eval()

        # == step6: (optional) warmup triton == #
        if (use_triton or use_tritonv2) and warmup_triton:
            if use_tritonv2:
                from ..nn_modules.qlinear.qlinear_tritonv2 import QuantLinear
            else:
                from ..nn_modules.qlinear.qlinear_triton import QuantLinear

            QuantLinear.warmup(model, seqlen=model.seqlen)

            if inject_fused_mlp and cls.fused_mlp_module_type is not None:
                cls.fused_mlp_module_type.warmup(model, seqlen=model.seqlen)

        # == step7: make model compatible with peft
        # cls.make_sure_compatible_with_peft(
        #     model,
        #     use_triton,
        #     quantize_config.desc_act,
        #     quantize_config.group_size,
        #     bits=quantize_config.bits,
        #     disable_exllama=disable_exllama,
        #     disable_exllamav2=disable_exllamav2,
        #     use_marlin=use_marlin,
        #     use_qigen=use_qigen,
        # )

        return cls(
            model,
            True,
            quantize_config,
            is_triton_backend=use_triton or use_tritonv2,
            injected_fused_attention=inject_fused_attention,
            injected_fused_mlp=inject_fused_mlp and (use_triton or use_tritonv2),
            trainable=trainable,
        )

    def warmup_triton(self, enabled: bool = True):
        if not enabled:
            return
        if not TRITON_AVAILABLE:
            logger.warning("triton is not available, skip warmup stage directly.")
            return

        from ..nn_modules.qlinear.qlinear_triton import QuantLinear

        QuantLinear.warmup(self.model, seqlen=self.model.seqlen)

        if self.fused_mlp_module_type is not None:
            self.fused_mlp_module_type.warmup(self.model, seqlen=self.model.seqlen)

    def enable_trainable_mode(self, enabled: bool = True):
        if not self.is_triton_backend and enabled:
            raise NotImplementedError("For now, trainable mode only supports triton backend.")
        for n, m in self.model.named_modules():
            if hasattr(m, "trainable"):
                setattr(m, "trainable", enabled)

    def disable_trainable_mode(self):
        self.enable_trainable_mode(enabled=False)

    @staticmethod
    def make_sure_compatible_with_peft(
        model: PreTrainedModel,
        use_triton: bool,
        desc_act: bool,
        group_size: int,
        bits: int,
        disable_exllama: bool = True,
        disable_exllamav2: bool = False,
        use_marlin: bool = False,
        use_qigen: bool = False,
        use_tritonv2: bool = False,
    ):
        GeneralQuantLinear.inject_to_model(
            model,
            dynamically_import_QuantLinear(use_triton, desc_act, group_size, bits=bits, disable_exllama=disable_exllama,
                                           disable_exllamav2=disable_exllamav2,
                                           use_marlin=use_marlin, use_qigen=use_qigen),
        )

    def __getattr__(self, item):
        try:
            return super().__getattr__(item)
        except Exception:
            return getattr(self.model, item)


__all__ = ["BaseGPTQForCausalLM", "BaseQuantizeConfig"]
