




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


__lowerCamelCase :    List[Any]      						=      logging.get_logger(__name__)

__lowerCamelCase :    Optional[int]      						=      {
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}


class  lowerCamelCase     (		lowerCamelCase__    ):






			'''simple docstring'''





			UpperCamelCase__										='''deberta'''

			def __init__(			self      : List[str]							,    lowerCamelCase_      : Optional[int]=50265							,    lowerCamelCase_      : List[Any]=768							,    lowerCamelCase_      : Dict=12							,    lowerCamelCase_      : Optional[int]=12							,    lowerCamelCase_      : Dict=3072							,    lowerCamelCase_      : int="gelu"							,    lowerCamelCase_      : Any=0.1							,    lowerCamelCase_      : Optional[Any]=0.1							,    lowerCamelCase_      : Tuple=512							,    lowerCamelCase_      : Tuple=0							,    lowerCamelCase_      : str=0.0_2							,    lowerCamelCase_      : Optional[int]=1E-7							,    lowerCamelCase_      : List[Any]=False							,    lowerCamelCase_      : Optional[int]=-1							,    lowerCamelCase_      : Optional[Any]=0							,    lowerCamelCase_      : Union[str, Any]=True							,    lowerCamelCase_      : Union[str, Any]=None							,    lowerCamelCase_      : int=0							,    lowerCamelCase_      : Union[str, Any]="gelu"							,    **lowerCamelCase_      : List[str]							,    )    -> Union[str, Any]:
								super().__init__(**__UpperCamelCase	)

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

								# Backwards compatibility
								if type(__UpperCamelCase	) == str:
													__magic_name__				:						List[str]     				=				[x.strip() for x in pos_att_type.lower().split('''|'''	)]

								__magic_name__				:						Union[str, Any]     				=				pos_att_type
								__magic_name__				:						List[str]     				=				vocab_size
								__magic_name__				:						Tuple     				=				layer_norm_eps

								__magic_name__				:						Tuple     				=				kwargs.get('''pooler_hidden_size'''							,    __UpperCamelCase	)
								__magic_name__				:						Dict     				=				pooler_dropout
								__magic_name__				:						Optional[int]     				=				pooler_hidden_act






class  lowerCamelCase     (		lowerCamelCase__    ):






			'''simple docstring'''





			@property
			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
													__magic_name__				:						Optional[int]     				=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
													__magic_name__				:						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 				UpperCAmelCase__							(			self      : Any	)    -> int:
								return 12


			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : Optional[int]							,    lowerCamelCase_      : int = -1							,    lowerCamelCase_      : List[str] = -1							,    lowerCamelCase_      : List[str] = -1							,    lowerCamelCase_      : Any = False							,    lowerCamelCase_      : Optional[Any] = None							,    lowerCamelCase_      : List[str] = 3							,    lowerCamelCase_      : List[str] = 40							,    lowerCamelCase_      : Dict = 40							,    lowerCamelCase_      : List[str] = None							,    )    -> Mapping[str, Any]:
								__magic_name__				:						Optional[int]     				=				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







'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase   ,	__lowerCamelCase					) or number < 0:
          raise ValueError("Input must be a non-negative integer"					)

     _a            =		number & ~(number - 1)
     _a            =		0
     while intermediate:
          intermediate >>= 1
          index += 1
     return index - 1


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

