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

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

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

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

																		SCREAMING_SNAKE_CASE							:					Tuple           =							new_value
																		if fpaa_statistics is not None:
																								setattr(module.weight	,						'''SCB'''	,						fpaa_statistics.to(_lowercase		)		)

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

												if is_buffer:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							new_value
												else:
																		SCREAMING_SNAKE_CASE							:					Dict           =							nn.Parameter(_lowercase	,						requires_grad=old_value.requires_grad		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							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:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[]
												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		):
																																				SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							module.weight.shape
																														else:
																																				SCREAMING_SNAKE_CASE							:					Tuple           =							module.in_features
																																				SCREAMING_SNAKE_CASE							:					Optional[int]           =							module.out_features

																														if quantization_config.quantization_method() == "llm_int8":
																																				SCREAMING_SNAKE_CASE							:					str           =							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	,						)
																																				SCREAMING_SNAKE_CASE							:					Optional[int]           =							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							:					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	,						)
																																										SCREAMING_SNAKE_CASE							:					int           =							True
                    # Store the module class in case we need to transpose the weight later
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							type(_lowercase		)
																														# Force requires grad to False to avoid unexpected errors
																														model._modules[name].requires_grad_(_lowercase		)
												if len(list(module.children()		)		) > 0:
																		SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							_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		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							_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		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							deepcopy(_lowercase		)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
						tied_model.tie_weights()

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

						# Check if it is a base model
						SCREAMING_SNAKE_CASE							:					List[Any]           =							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
						SCREAMING_SNAKE_CASE							:					List[Any]           =							list(model.named_children()		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							[list_modules[-1][0]]

						# add last module together with tied weights
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							set(_lowercase		) - set(_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							list(set(_lowercase		)		) + list(_lowercase		)

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

						return filtered_module_names






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					int           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Dict           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							relative_attention
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							max_relative_positions
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							position_biased_input

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{0: '''batch''', 1: '''sequence'''}
												if self._config.type_vocab_size > 0:
																		return OrderedDict(
																		    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]		)
												else:
																		return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]		)


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





