





'''simple docstring'''




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		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_=None			,			lowerCamelCase_=None	):


		"""simple docstring"""



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

		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}.'''	)

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

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

						SCREAMING_SNAKE_CASE :				List[Any]		=			old_value.__dict__
						if is_abit:
								SCREAMING_SNAKE_CASE :				Any		=			bnb.nn.IntaParams(lowerCamelCase_			,			requires_grad=lowerCamelCase_			,			**lowerCamelCase_	).to(lowerCamelCase_	)
						elif is_abit:
								SCREAMING_SNAKE_CASE :				List[str]		=			bnb.nn.Paramsabit(lowerCamelCase_			,			requires_grad=lowerCamelCase_			,			**lowerCamelCase_	).to(lowerCamelCase_	)

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

		else:
				if value is None:
						SCREAMING_SNAKE_CASE :				int		=			old_value.to(lowerCamelCase_	)
				elif isinstance(lowerCamelCase_			,			torch.Tensor	):
						SCREAMING_SNAKE_CASE :				Optional[int]		=			value.to(lowerCamelCase_	)
				else:
						SCREAMING_SNAKE_CASE :				int		=			torch.tensor(lowerCamelCase_			,			device=lowerCamelCase_	)

				if is_buffer:
						SCREAMING_SNAKE_CASE :				List[Any]		=			new_value
				else:
						SCREAMING_SNAKE_CASE :				int		=			nn.Parameter(lowerCamelCase_			,			requires_grad=old_value.requires_grad	)
						SCREAMING_SNAKE_CASE :				str		=			new_value



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_=None			,			lowerCamelCase_=None			,			lowerCamelCase_=None			,			lowerCamelCase_=False	):


		"""simple docstring"""



		for name, module in model.named_children():
				if current_key_name is None:
						SCREAMING_SNAKE_CASE :				Dict		=			[]
				current_key_name.append(lowerCamelCase_	)

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

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



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_=None			,			lowerCamelCase_=None			,			lowerCamelCase_=None	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			_replace_with_bnb_linear(
		    lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)

		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		(  *lowerCamelCase_			,			**lowerCamelCase_	):


		"""simple docstring"""



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



def 		__A		(  *lowerCamelCase_			,			**lowerCamelCase_	):


		"""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"""			,			lowerCamelCase_			,			)
		return set_module_quantized_tensor_to_device(*lowerCamelCase_			,			**lowerCamelCase_	)



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Optional[Any]		=			deepcopy(lowerCamelCase_	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
		tied_model.tie_weights()

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

		# Check if it is a base model
		SCREAMING_SNAKE_CASE :				str		=			not hasattr(lowerCamelCase_			,			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
		SCREAMING_SNAKE_CASE :				List[str]		=			list(model.named_children()	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			[list_modules[-1][0]]

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

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

		return filtered_module_names










'''simple docstring'''




from __future__ import annotations



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		if partitions <= 0:
				raise ValueError("""partitions must be a positive number!"""	)
		if partitions > number_of_bytes:
				raise ValueError("""partitions can not > number_of_bytes!"""	)
		SCREAMING_SNAKE_CASE :				int		=			number_of_bytes // partitions
		SCREAMING_SNAKE_CASE :				int		=			[]
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			i * bytes_per_partition + 1
				SCREAMING_SNAKE_CASE :				Optional[int]		=			(
				    number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
				)
				allocation_list.append(f'''{start_bytes}-{end_bytes}'''	)
		return allocation_list


if __name__ == "__main__":
							import doctest

							doctest.testmod()



