


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__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Optional[int]=None					,							__lowerCAmelCase						:						Optional[Any]=None       ):




			"""simple docstring"""


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

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

			if is_abit or is_abit:
						lowerCAmelCase_      			=			module._parameters[tensor_name]
						if param.device.type != "cuda":
									if value is None:
												lowerCAmelCase_      			=			old_value.to(__lowerCAmelCase       )
									elif isinstance(__lowerCAmelCase					,							torch.Tensor       ):
												lowerCAmelCase_      			=			value.to("cpu"       )
												if value.dtype == torch.inta:
															lowerCAmelCase_      			=			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:
												lowerCAmelCase_      			=			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:
												lowerCAmelCase_      			=			new_value.T

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

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

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

						if is_buffer:
									lowerCAmelCase_      			=			new_value
						else:
									lowerCAmelCase_      			=			nn.Parameter(__lowerCAmelCase					,							requires_grad=old_value.requires_grad       )
									lowerCAmelCase_      			=			new_value






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						int=None					,							__lowerCAmelCase						:						Tuple=None					,							__lowerCAmelCase						:						str=None					,							__lowerCAmelCase						:						str=False       ):




			"""simple docstring"""


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

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




			"""simple docstring"""


			lowerCAmelCase_      			=			["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
			lowerCAmelCase_			,						lowerCAmelCase_      			=			_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      lowerCamelCase__       (       *__lowerCAmelCase						:						List[Any]					,							**__lowerCAmelCase						:						str       ):




			"""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      lowerCamelCase__       (       *__lowerCAmelCase						:						List[Any]					,							**__lowerCAmelCase						:						List[str]       ):




			"""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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]       ):




			"""simple docstring"""


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

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

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

			# add last module together with tied weights
			lowerCAmelCase_      			=			set(__lowerCAmelCase       ) - set(__lowerCAmelCase       )
			lowerCAmelCase_      			=			list(set(__lowerCAmelCase       )       ) + list(__lowerCAmelCase       )

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

			return filtered_module_names





from string import ascii_lowercase, ascii_uppercase






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			if not sentence:
						return ""
			lowerCAmelCase_      			=			dict(zip(__lowerCAmelCase					,							__lowerCAmelCase       )       )
			return lower_to_upper.get(sentence[0]					,							sentence[0]       ) + sentence[1:]


if __name__ == "__main__":
			from doctest import testmod

			testmod()



