


from __future__ import annotations






def 						a__			(				A__,      A__,      A__,      ):

			if (stress, tangential_force, area).count(0				) != 1:
						raise ValueError('You cannot supply more or less than 2 values'				)
			elif stress < 0:
						raise ValueError('Stress cannot be negative'				)
			elif tangential_force < 0:
						raise ValueError('Tangential Force cannot be negative'				)
			elif area < 0:
						raise ValueError('Area cannot be negative'				)
			elif stress == 0:
						return (
						    "stress",
						    tangential_force / area,
						)
			elif tangential_force == 0:
						return (
						    "tangential_force",
						    stress * area,
						)
			else:
						return (
						    "area",
						    tangential_force / stress,
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()







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

UpperCamelCase_						:     int	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Dict=None ,			_UpperCAmelCase  :   Dict=None  )   ->			Tuple:






						'''simple docstring'''

						if "." in tensor_name:
												_lowercase       :	Dict           =   tensor_name.split("."  )
												for split in splits[:-1]:
																		_lowercase       :	Optional[int]           =   getattr(_UpperCAmelCase ,			_UpperCAmelCase  )
																		if new_module is None:
																								raise ValueError(f"""{module} has no attribute {split}."""  )
																		_lowercase       :	List[Any]           =   new_module
												_lowercase       :	Dict           =   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}."""  )
						_lowercase       :	Dict           =   tensor_name in module._buffers
						_lowercase       :	Union[str, Any]           =   getattr(_UpperCAmelCase ,			_UpperCAmelCase  )

						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}."""  )

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

						if is_abit or is_abit:
												_lowercase       :	Optional[int]           =   module._parameters[tensor_name]
												if param.device.type != "cuda":
																		if value is None:
																								_lowercase       :	int           =   old_value.to(_UpperCAmelCase  )
																		elif isinstance(_UpperCAmelCase ,			torch.Tensor  ):
																								_lowercase       :	Dict           =   value.to("cpu"  )
																								if value.dtype == torch.inta:
																														_lowercase       :	str           =   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:
																								_lowercase       :	List[str]           =   torch.tensor(_UpperCAmelCase ,			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 ,			_UpperCAmelCase  ) and fpaa_statistics is None:
																								_lowercase       :	List[Any]           =   new_value.T

																		_lowercase       :	List[str]           =   old_value.__dict__
																		if is_abit:
																								_lowercase       :	Optional[Any]           =   bnb.nn.IntaParams(_UpperCAmelCase ,			requires_grad=_UpperCAmelCase ,			**_UpperCAmelCase  ).to(_UpperCAmelCase  )
																		elif is_abit:
																								_lowercase       :	Dict           =   bnb.nn.Paramsabit(_UpperCAmelCase ,			requires_grad=_UpperCAmelCase ,			**_UpperCAmelCase  ).to(_UpperCAmelCase  )

																		_lowercase       :	str           =   new_value
																		if fpaa_statistics is not None:
																								setattr(module.weight ,			"SCB" ,			fpaa_statistics.to(_UpperCAmelCase  )  )

						else:
												if value is None:
																		_lowercase       :	Optional[Any]           =   old_value.to(_UpperCAmelCase  )
												elif isinstance(_UpperCAmelCase ,			torch.Tensor  ):
																		_lowercase       :	Tuple           =   value.to(_UpperCAmelCase  )
												else:
																		_lowercase       :	Optional[Any]           =   torch.tensor(_UpperCAmelCase ,			device=_UpperCAmelCase  )

												if is_buffer:
																		_lowercase       :	str           =   new_value
												else:
																		_lowercase       :	int           =   nn.Parameter(_UpperCAmelCase ,			requires_grad=old_value.requires_grad  )
																		_lowercase       :	str           =   new_value



def 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   str=None ,			_UpperCAmelCase  :   List[str]=None ,			_UpperCAmelCase  :   Tuple=None ,			_UpperCAmelCase  :   Tuple=False  )   ->			int:






						'''simple docstring'''

						for name, module in model.named_children():
												if current_key_name is None:
																		_lowercase       :	int           =   []
												current_key_name.append(_UpperCAmelCase  )

												if (isinstance(_UpperCAmelCase ,			nn.Linear  ) or isinstance(_UpperCAmelCase ,			_UpperCAmelCase  )) 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(_UpperCAmelCase  ) for key in modules_to_not_convert  ):
																								with init_empty_weights():
																														if isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ):
																																				_lowercase      ,    _lowercase       :	Tuple           =   module.weight.shape
																														else:
																																				_lowercase       :	List[Any]           =   module.in_features
																																				_lowercase       :	List[str]           =   module.out_features

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Tuple=None ,			_UpperCAmelCase  :   Optional[int]=None ,			_UpperCAmelCase  :   int=None  )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	str           =   ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
						_lowercase      ,    _lowercase       :	Any           =   _replace_with_bnb_linear(
						    _UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

						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 		UpperCamelCase     (       *_UpperCAmelCase  :   Optional[Any] ,			**_UpperCAmelCase  :   Dict  )   ->			Tuple:






						'''simple docstring'''

						warnings.warn(
						    "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead" ,			_UpperCAmelCase ,			)
						return replace_with_bnb_linear(*_UpperCAmelCase ,			**_UpperCAmelCase  )



def 		UpperCamelCase     (       *_UpperCAmelCase  :   Dict ,			**_UpperCAmelCase  :   Dict  )   ->			int:






						'''simple docstring'''

						warnings.warn(
						    "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead" ,			_UpperCAmelCase ,			)
						return set_module_quantized_tensor_to_device(*_UpperCAmelCase ,			**_UpperCAmelCase  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[Any]  )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Tuple           =   deepcopy(_UpperCAmelCase  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
						tied_model.tie_weights()

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

						# Check if it is a base model
						_lowercase       :	Any           =   not hasattr(_UpperCAmelCase ,			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
						_lowercase       :	int           =   list(model.named_children()  )
						_lowercase       :	str           =   [list_modules[-1][0]]

						# add last module together with tied weights
						_lowercase       :	Optional[int]           =   set(_UpperCAmelCase  ) - set(_UpperCAmelCase  )
						_lowercase       :	Optional[int]           =   list(set(_UpperCAmelCase  )  ) + list(_UpperCAmelCase  )

						# remove ".weight" from the keys
						_lowercase       :	str           =   [".weight", ".bias"]
						_lowercase       :	Optional[Any]           =   []
						for name in list_untouched:
												for name_to_remove in names_to_remove:
																		if name_to_remove in name:
																								_lowercase       :	Optional[Any]           =   name.replace(_UpperCAmelCase ,			""  )
												filtered_module_names.append(_UpperCAmelCase  )

						return filtered_module_names




