
"""simple docstring"""





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_       :    List[str]						      =			logging.get_logger(__name__)

UpperCamelCase_       :    Union[str, 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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					"deberta-v2"




			def __init__(					self    :       Any							, _snake_case    :       Dict=128_100							, _snake_case    :       Any=1_536							, _snake_case    :       List[Any]=24							, _snake_case    :       Union[str, Any]=24							, _snake_case    :       str=6_144							, _snake_case    :       Optional[Any]="gelu"							, _snake_case    :       Tuple=0.1							, _snake_case    :       int=0.1							, _snake_case    :       List[str]=512							, _snake_case    :       List[Any]=0							, _snake_case    :       int=0.0_2							, _snake_case    :       int=1e-7							, _snake_case    :       Optional[Any]=False							, _snake_case    :       Union[str, Any]=-1							, _snake_case    :       Dict=0							, _snake_case    :       Dict=True							, _snake_case    :       int=None							, _snake_case    :       List[Any]=0							, _snake_case    :       Optional[int]="gelu"							, **_snake_case    :       Dict							, )   ->			Any:







								"""simple docstring"""
								super().__init__(**_snake_case						)

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

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

								A_								=    pos_att_type
								A_								=    vocab_size
								A_								=    layer_norm_eps

								A_								=    kwargs.get("pooler_hidden_size"							, _snake_case						)
								A_								=    pooler_dropout
								A_								=    pooler_hidden_act





class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       int						)   ->			List[Any]:







								"""simple docstring"""
								if self.task == "multiple-choice":
													A_								=    {0: "batch", 1: "choice", 2: "sequence"}
								else:
													A_								=    {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   lowerCamelCase__  (					self    :       str						)   ->			List[Any]:







								"""simple docstring"""
								return 12






			def   lowerCamelCase__  (					self    :       int							, _snake_case    :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							, _snake_case    :       int = -1							, _snake_case    :       int = -1							, _snake_case    :       int = -1							, _snake_case    :       bool = False							, _snake_case    :       Optional["TensorType"] = None							, _snake_case    :       int = 3							, _snake_case    :       int = 40							, _snake_case    :       int = 40							, _snake_case    :       "PreTrainedTokenizerBase" = None							, )   ->			Any:







								"""simple docstring"""
								A_								=    super().generate_dummy_inputs(preprocessor=_snake_case							, framework=_snake_case						)
								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'''
from __future__ import annotations

from typing import Any







def       a_				(	UpperCamelCase_ ):
							create_state_space_tree(UpperCamelCase_						,  []						,  0 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):

							if index == len(UpperCamelCase_ ):
														print(UpperCamelCase_ )
														return

							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.append(sequence[index] )
							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.pop()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				list[Any]									=		[3, 1, 2, 4]
							generate_all_subsequences(seq)

							seq.clear()
							seq.extend(['''A''', '''B''', '''C'''])
							generate_all_subsequences(seq)





