


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


A							:					Optional[int]     						=     logging.get_logger(__name__)

A							:					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     UpperCamelCase(  __a   ):
					snake_case_		:				Any										=	"deberta-v2"


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=1_2_8_1_0_0     ,    SCREAMING_SNAKE_CASE	:		int=1_5_3_6     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2_4     ,    SCREAMING_SNAKE_CASE	:		Dict=6_1_4_4     ,    SCREAMING_SNAKE_CASE	:		str="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=0.1     ,    SCREAMING_SNAKE_CASE	:		List[str]=0.1     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=0     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1e-7     ,    SCREAMING_SNAKE_CASE	:		Tuple=False     ,    SCREAMING_SNAKE_CASE	:		int=-1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=None     ,    SCREAMING_SNAKE_CASE	:		Any=0     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="gelu"     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	int:




											'''simple docstring'''

											super().__init__(**lowerCAmelCase_						)

											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		max_position_embeddings
											__snake_case				=		type_vocab_size
											__snake_case				=		initializer_range
											__snake_case				=		relative_attention
											__snake_case				=		max_relative_positions
											__snake_case				=		pad_token_id
											__snake_case				=		position_biased_input

											# Backwards compatibility
											if type(lowerCAmelCase_						) == str:
																	__snake_case				=		[x.strip() for x in pos_att_type.lower().split("|"						)]

											__snake_case				=		pos_att_type
											__snake_case				=		vocab_size
											__snake_case				=		layer_norm_eps

											__snake_case				=		kwargs.get("pooler_hidden_size"     ,    lowerCAmelCase_						)
											__snake_case				=		pooler_dropout
											__snake_case				=		pooler_hidden_act






class     UpperCamelCase(  __a   ):


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											if self.task == "multiple-choice":
																	__snake_case				=		{0: "batch", 1: "choice", 2: "sequence"}
											else:
																	__snake_case				=		{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 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	int:




											'''simple docstring'''

											return 1_2






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Tuple = -1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any] = -1     ,    SCREAMING_SNAKE_CASE	:		str = -1     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = False     ,    SCREAMING_SNAKE_CASE	:		Tuple = None     ,    SCREAMING_SNAKE_CASE	:		str = 3     ,    SCREAMING_SNAKE_CASE	:		int = 4_0     ,    SCREAMING_SNAKE_CASE	:		Dict = 4_0     ,    SCREAMING_SNAKE_CASE	:		List[Any] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

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





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A							:					Any     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						)			->	None:




											'''simple docstring'''

											warnings.warn(
											    "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
											    " use BeitImageProcessor instead."     ,    SCREAMING_SNAKE_CASE     ,    )
											super().__init__(*SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)





