





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_									=logging.get_logger(__name__)





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase=None	,						_lowercase=None       ):
						# Recurse if needed
						if "." in tensor_name:
												_UpperCamelCase      : Tuple         =   tensor_name.split('''.'''       )
												for split in splits[:-1]:
																		_UpperCamelCase      : List[Any]         =   getattr(_lowercase	,						_lowercase       )
																		if new_module is None:
																								raise ValueError(F"""{module} has no attribute {split}."""       )
																		_UpperCamelCase      : List[str]         =   new_module
												_UpperCamelCase      : Optional[int]         =   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      : Optional[int]         =   tensor_name in module._buffers
						_UpperCamelCase      : Tuple         =   getattr(_lowercase	,						_lowercase       )

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

						if is_abit or is_abit:
												_UpperCamelCase      : List[str]         =   module._parameters[tensor_name]
												if param.device.type != "cuda":
																		if value is None:
																								_UpperCamelCase      : List[str]         =   old_value.to(_lowercase       )
																		elif isinstance(_lowercase	,						torch.Tensor       ):
																								_UpperCamelCase      : Any         =   value.to('''cpu'''       )
																								if value.dtype == torch.inta:
																														_UpperCamelCase      : Optional[Any]         =   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      : Tuple         =   torch.tensor(_lowercase	,						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	,						_lowercase       ) and fpaa_statistics is None:
																								_UpperCamelCase      : Optional[Any]         =   new_value.T

																		_UpperCamelCase      : Any         =   old_value.__dict__
																		if is_abit:
																								_UpperCamelCase      : Union[str, Any]         =   bnb.nn.IntaParams(_lowercase	,						requires_grad=_lowercase	,						**_lowercase       ).to(_lowercase       )
																		elif is_abit:
																								_UpperCamelCase      : Any         =   bnb.nn.Paramsabit(_lowercase	,						requires_grad=_lowercase	,						**_lowercase       ).to(_lowercase       )

																		_UpperCamelCase      : List[str]         =   new_value
																		if fpaa_statistics is not None:
																								setattr(module.weight	,						'''SCB'''	,						fpaa_statistics.to(_lowercase       )       )

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

												if is_buffer:
																		_UpperCamelCase      : Optional[int]         =   new_value
												else:
																		_UpperCamelCase      : Tuple         =   nn.Parameter(_lowercase	,						requires_grad=old_value.requires_grad       )
																		_UpperCamelCase      : Tuple         =   new_value





def 	a_    (							_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None	,						_lowercase=False       ):
						for name, module in model.named_children():
												if current_key_name is None:
																		_UpperCamelCase      : Tuple         =   []
												current_key_name.append(_lowercase       )

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

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





def 	a_    (							_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None       ):
						_UpperCamelCase      : Dict         =   ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
						_UpperCamelCase       ,							_UpperCamelCase      : List[str]         =   _replace_with_bnb_linear(
						    _lowercase	,						_lowercase	,						_lowercase	,						_lowercase       )

						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 	a_    (							*_lowercase	,						**_lowercase       ):
						warnings.warn(
						    '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''	,						_lowercase	,						)
						return replace_with_bnb_linear(*_lowercase	,						**_lowercase       )





def 	a_    (							*_lowercase	,						**_lowercase       ):
						warnings.warn(
						    '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'''	,						_lowercase	,						)
						return set_module_quantized_tensor_to_device(*_lowercase	,						**_lowercase       )






def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[Any]         =   deepcopy(_lowercase       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
						tied_model.tie_weights()

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

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

						# add last module together with tied weights
						_UpperCamelCase      : int         =   set(_lowercase       ) - set(_lowercase       )
						_UpperCamelCase      : Any         =   list(set(_lowercase       )       ) + list(_lowercase       )

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

						return filtered_module_names










"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)



