import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
       import bitsandbytes as bnb
       import torch
       import torch.nn as nn

       from ..pytorch_utils import ConvaD

if is_accelerate_available():
       from accelerate import init_empty_weights
       from accelerate.utils import find_tied_parameters

a_							=      logging.get_logger(__name__)




def       __lowerCAmelCase      (						A_   : str						,	A_   : str						,	A_   : Optional[int]						,	A_   : str=None						,	A_   : str=None					)    ->  str:
  # Recurse if needed
  if "." in tensor_name:
    __UpperCAmelCase						    =       tensor_name.split("."					)
    for split in splits[:-1]:
      __UpperCAmelCase						    =       getattr(A_						,	A_					)
      if new_module is None:
        raise ValueError(F'''{module} has no attribute {split}.'''					)
      __UpperCAmelCase						    =       new_module
    __UpperCAmelCase						    =       splits[-1]

  if tensor_name not in module._parameters and tensor_name not in module._buffers:
    raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.'''					)
  __UpperCAmelCase						    =       tensor_name in module._buffers
  __UpperCAmelCase						    =       getattr(A_						,	A_					)

  if old_value.device == torch.device("meta"					) and device not in ["meta", torch.device("meta"					)] and value is None:
    raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.'''					)

  __UpperCAmelCase						    =       False
  __UpperCAmelCase						    =       False
  if is_buffer or not is_bitsandbytes_available():
    __UpperCAmelCase						    =       False
    __UpperCAmelCase						    =       False
  else:
    __UpperCAmelCase						    =       hasattr(bnb.nn						,	"Params4bit"					) and isinstance(module._parameters[tensor_name]						,	bnb.nn.Paramsabit					)
    __UpperCAmelCase						    =       isinstance(module._parameters[tensor_name]						,	bnb.nn.IntaParams					)

  if is_abit or is_abit:
    __UpperCAmelCase						    =       module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        __UpperCAmelCase						    =       old_value.to(A_					)
      elif isinstance(A_						,	torch.Tensor					):
        __UpperCAmelCase						    =       value.to("cpu"					)
        if value.dtype == torch.inta:
          __UpperCAmelCase						    =       version.parse(importlib.metadata.version("bitsandbytes"					)					) > version.parse(
              "0.37.2"					)
          if not is_abit_serializable:
            raise ValueError(
                "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
                "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."					)
      else:
        __UpperCAmelCase						    =       torch.tensor(A_						,	device="cpu"					)

      # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
      # Since weights are saved in the correct "orientation", we skip transposing when loading.
      if issubclass(module.source_cls						,	A_					) and fpaa_statistics is None:
        __UpperCAmelCase						    =       new_value.T

      __UpperCAmelCase						    =       old_value.__dict__
      if is_abit:
        __UpperCAmelCase						    =       bnb.nn.IntaParams(A_						,	requires_grad=A_						,	**A_					).to(A_					)
      elif is_abit:
        __UpperCAmelCase						    =       bnb.nn.Paramsabit(A_						,	requires_grad=A_						,	**A_					).to(A_					)

      __UpperCAmelCase						    =       new_value
      if fpaa_statistics is not None:
        setattr(module.weight						,	"SCB"						,	fpaa_statistics.to(A_					)					)

  else:
    if value is None:
      __UpperCAmelCase						    =       old_value.to(A_					)
    elif isinstance(A_						,	torch.Tensor					):
      __UpperCAmelCase						    =       value.to(A_					)
    else:
      __UpperCAmelCase						    =       torch.tensor(A_						,	device=A_					)

    if is_buffer:
      __UpperCAmelCase						    =       new_value
    else:
      __UpperCAmelCase						    =       nn.Parameter(A_						,	requires_grad=old_value.requires_grad					)
      __UpperCAmelCase						    =       new_value




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Union[str, Any]=None						,	A_   : str=None						,	A_   : List[Any]=None						,	A_   : List[Any]=False					)    ->  Dict:
  for name, module in model.named_children():
    if current_key_name is None:
      __UpperCAmelCase						    =       []
    current_key_name.append(A_					)

    if (isinstance(A_						,	nn.Linear					) or isinstance(A_						,	A_					)) and name not in modules_to_not_convert:
      # Check if the current key is not in the `modules_to_not_convert`
      if not any(key in ".".join(A_					) for key in modules_to_not_convert					):
        with init_empty_weights():
          if isinstance(A_						,	A_					):
            __UpperCAmelCase    ,		__UpperCAmelCase						    =       module.weight.shape
          else:
            __UpperCAmelCase						    =       module.in_features
            __UpperCAmelCase						    =       module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            __UpperCAmelCase						    =       bnb.nn.LinearabitLt(
                A_						,	A_						,	module.bias is not None						,	has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight						,	threshold=quantization_config.llm_inta_threshold						,	)
            __UpperCAmelCase						    =       True
          else:
            if (
                quantization_config.llm_inta_skip_modules is not None
                and name in quantization_config.llm_inta_skip_modules
            ):
              pass
            else:
              __UpperCAmelCase						    =       bnb.nn.Linearabit(
                  A_						,	A_						,	module.bias is not None						,	quantization_config.bnb_abit_compute_dtype						,	compress_statistics=quantization_config.bnb_abit_use_double_quant						,	quant_type=quantization_config.bnb_abit_quant_type						,	)
              __UpperCAmelCase						    =       True
                    # Store the module class in case we need to transpose the weight later
          __UpperCAmelCase						    =       type(A_					)
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(A_					)
    if len(list(module.children()					)					) > 0:
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       _replace_with_bnb_linear(
          A_						,	A_						,	A_						,	A_						,	has_been_replaced=A_						,	)
    # Remove the last key for recursion
    current_key_name.pop(-1					)
  return model, has_been_replaced




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : int=None						,	A_   : List[Any]=None						,	A_   : Tuple=None					)    ->  int:
  __UpperCAmelCase						    =       ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       _replace_with_bnb_linear(
      A_						,	A_						,	A_						,	A_					)

  if not has_been_replaced:
    logger.warning(
        "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
        " Please double check your model architecture, or submit an issue on github if you think this is"
        " a bug."					)

  return model




def       __lowerCAmelCase      (						*A_   : str						,	**A_   : Tuple					)    ->  List[str]:
  warnings.warn(
      "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"						,	A_						,	)
  return replace_with_bnb_linear(*A_						,	**A_					)




def       __lowerCAmelCase      (						*A_   : Union[str, Any]						,	**A_   : int					)    ->  Any:
  warnings.warn(
      "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"						,	A_						,	)
  return set_module_quantized_tensor_to_device(*A_						,	**A_					)





def       __lowerCAmelCase      (						A_   : Any					)    ->  Optional[int]:
  __UpperCAmelCase						    =       deepcopy(A_					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

  __UpperCAmelCase						    =       find_tied_parameters(A_					)
  # For compatibility with Accelerate < 0.18
  if isinstance(A_						,	A_					):
    __UpperCAmelCase						    =       sum(list(tied_params.values()					)						,	[]					) + list(tied_params.keys()					)
  else:
    __UpperCAmelCase						    =       sum(A_						,	[]					)
  __UpperCAmelCase						    =       len(A_					) > 0

  # Check if it is a base model
  __UpperCAmelCase						    =       not hasattr(A_						,	model.base_model_prefix					)

  # Ignore this for base models (BertModel, GPT2Model, etc.)
  if (not has_tied_params) and is_base_model:
    return []

  # otherwise they have an attached head
  __UpperCAmelCase						    =       list(model.named_children()					)
  __UpperCAmelCase						    =       [list_modules[-1][0]]

  # add last module together with tied weights
  __UpperCAmelCase						    =       set(A_					) - set(A_					)
  __UpperCAmelCase						    =       list(set(A_					)					) + list(A_					)

  # remove ".weight" from the keys
  __UpperCAmelCase						    =       [".weight", ".bias"]
  __UpperCAmelCase						    =       []
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        __UpperCAmelCase						    =       name.replace(A_						,	""					)
    filtered_module_names.append(A_					)

  return filtered_module_names




from abc import ABC, abstractmethod
from argparse import ArgumentParser


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  @staticmethod
  @abstractmethod
  def 	_UpperCAmelCase				(				__lowerCAmelCase:			ArgumentParser   ) -> Tuple:




    '''simple docstring'''




    raise NotImplementedError()






  @abstractmethod
  def 	_UpperCAmelCase				(				self:			List[str]   ) -> List[Any]:




    '''simple docstring'''




    raise NotImplementedError()



