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


__SCREAMING_SNAKE_CASE						:   Dict         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   int         =			{
    '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_   (					__snake_case		):
							_lowerCamelCase						=		'deberta-v2'



							def __init__(						self				,    lowercase_=128_100				,    lowercase_=1_536				,    lowercase_=24				,    lowercase_=24				,    lowercase_=6_144				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=512				,    lowercase_=0				,    lowercase_=0.02				,    lowercase_=1e-7				,    lowercase_=False				,    lowercase_=-1				,    lowercase_=0				,    lowercase_=True				,    lowercase_=None				,    lowercase_=0				,    lowercase_="gelu"				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

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

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

													_snake_case						:					str    =   pos_att_type
													_snake_case						:					int    =   vocab_size
													_snake_case						:					List[str]    =   layer_norm_eps

													_snake_case						:					List[str]    =   kwargs.get("pooler_hidden_size"				,    lowercase_    )
													_snake_case						:					Any    =   pooler_dropout
													_snake_case						:					Tuple    =   pooler_hidden_act




class        lowercase_   (					__snake_case		):



							@property
							def 	UpperCamelCase	(						self    ):
													if self.task == "multiple-choice":
																			_snake_case						:					Dict    =   {0: "batch", 1: "choice", 2: "sequence"}
													else:
																			_snake_case						:					str    =   {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    ):
													return 12



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    lowercase_ = 3				,    lowercase_ = 40				,    lowercase_ = 40				,    lowercase_ = None				,    ):
													_snake_case						:					int    =   super().generate_dummy_inputs(preprocessor=lowercase_				,    framework=lowercase_    )
													if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																			del dummy_inputs["token_type_ids"]
													return dummy_inputs
def  snake_case						()	->	Dict:

						'''simple docstring'''



						_snake_case						:					List[str]    =   0
						for i in range(1 ,      1_001						):
												total += i**i
						return str(__lowercase						)[-10:]


if __name__ == "__main__":
				print(solution())