





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   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Optional[int]=None    ,      UpperCamelCase__     :    Any=None  )     ->	Optional[Any]:



					'''simple docstring'''




					if "." in tensor_name:
										_snake_case													=			tensor_name.split('.'  )
										for split in splits[:-1]:
															_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )
															if new_module is None:
																				raise ValueError(F'''{module} has no attribute {split}.'''  )
															_snake_case													=			new_module
										_snake_case													=			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}.'''  )
					_snake_case													=			tensor_name in module._buffers
					_snake_case													=			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}.'''  )

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

					if is_abit or is_abit:
										_snake_case													=			module._parameters[tensor_name]
										if param.device.type != "cuda":
															if value is None:
																				_snake_case													=			old_value.to(UpperCamelCase__  )
															elif isinstance(UpperCamelCase__    ,      torch.Tensor  ):
																				_snake_case													=			value.to('cpu'  )
																				if value.dtype == torch.inta:
																									_snake_case													=			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:
																				_snake_case													=			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:
																				_snake_case													=			new_value.T

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

															_snake_case													=			new_value
															if fpaa_statistics is not None:
																				setattr(module.weight    ,      'SCB'    ,      fpaa_statistics.to(UpperCamelCase__  )  )

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

										if is_buffer:
															_snake_case													=			new_value
										else:
															_snake_case													=			nn.Parameter(UpperCamelCase__    ,      requires_grad=old_value.requires_grad  )
															_snake_case													=			new_value





def   lowerCamelCase__       (       UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Union[str, Any]=None    ,      UpperCamelCase__     :    int=None    ,      UpperCamelCase__     :    List[Any]=None    ,      UpperCamelCase__     :    List[Any]=False  )     ->	Optional[Any]:



					'''simple docstring'''




					for name, module in model.named_children():
										if current_key_name is None:
															_snake_case													=			[]
										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__  ):
																														_snake_case							,       _snake_case													=			module.weight.shape
																									else:
																														_snake_case													=			module.in_features
																														_snake_case													=			module.out_features

																									if quantization_config.quantization_method() == "llm_int8":
																														_snake_case													=			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    ,      )
																														_snake_case													=			True
																									else:
																														if (
																														    quantization_config.llm_inta_skip_modules is not None
																														    and name in quantization_config.llm_inta_skip_modules
																														):
																																			pass
																														else:
																																			_snake_case													=			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    ,      )
																																			_snake_case													=			True
                    # Store the module class in case we need to transpose the weight later
																									_snake_case													=			type(UpperCamelCase__  )
																									# Force requires grad to False to avoid unexpected errors
																									model._modules[name].requires_grad_(UpperCamelCase__  )
										if len(list(module.children()  )  ) > 0:
															_snake_case							,       _snake_case													=			_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   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Optional[int]=None    ,      UpperCamelCase__     :    str=None    ,      UpperCamelCase__     :    Tuple=None  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
					_snake_case							,       _snake_case													=			_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   lowerCamelCase__       (       *UpperCamelCase__     :    str    ,      **UpperCamelCase__     :    Optional[int]  )     ->	Union[str, Any]:



					'''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   lowerCamelCase__       (       *UpperCamelCase__     :    str    ,      **UpperCamelCase__     :    Any  )     ->	Optional[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   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			deepcopy(UpperCamelCase__  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
					tied_model.tie_weights()

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

					# Check if it is a base model
					_snake_case													=			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
					_snake_case													=			list(model.named_children()  )
					_snake_case													=			[list_modules[-1][0]]

					# add last module together with tied weights
					_snake_case													=			set(UpperCamelCase__  ) - set(UpperCamelCase__  )
					_snake_case													=			list(set(UpperCamelCase__  )  ) + list(UpperCamelCase__  )

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

					return filtered_module_names










def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    list[int]  )     ->	bool:



					'''simple docstring'''




					if graph[path[curr_ind - 1]][next_ver] == 0:
										return False

					# 2. Validate that next vertex is not already in path
					return not any(vertex == next_ver for vertex in path  )





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    list[int]    ,      UpperCamelCase__     :    int  )     ->	bool:



					'''simple docstring'''




					if curr_ind == len(UpperCamelCase__  ):
										# return whether path exists between current and starting vertices
										return graph[path[curr_ind - 1]][path[0]] == 1

					# Recursive Step
					for next_ver in range(0    ,      len(UpperCamelCase__  )  ):
										if valid_connection(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  ):
															# Insert current vertex  into path as next transition
															_snake_case													=			next_ver
															# Validate created path
															if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      curr_ind + 1  ):
																				return True
															# Backtrack
															_snake_case													=			-1
					return False





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int = 0  )     ->	list[int]:



					'''simple docstring'''




					_snake_case													=			[-1] * (len(UpperCamelCase__  ) + 1)
					# initialize start and end of path with starting index
					_snake_case													=			_snake_case													=			start_index
					# evaluate and if we find answer return path either return empty array
					return path if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      1  ) else []



