



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


snake_case     :    Any						=    logging.get_logger(__name__)

snake_case     :    Union[str, Any]						=    {
    '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__	(						UpperCamelCase    ):
					__A  :     List[Any]     =    'deberta'







					def __init__(							self   :  Union[str, Any]      ,		_A   :  str=5_0265      ,		_A   :  List[Any]=768      ,		_A   :  Tuple=12      ,		_A   :  Optional[Any]=12      ,		_A   :  Optional[int]=3072      ,		_A   :  Any="gelu"      ,		_A   :  str=0.1      ,		_A   :  Optional[int]=0.1      ,		_A   :  Any=512      ,		_A   :  Tuple=0      ,		_A   :  Dict=0.02      ,		_A   :  List[Any]=1e-7      ,		_A   :  Tuple=False      ,		_A   :  List[str]=-1      ,		_A   :  Union[str, Any]=0      ,		_A   :  int=True      ,		_A   :  Optional[Any]=None      ,		_A   :  Optional[Any]=0      ,		_A   :  List[str]="gelu"      ,		**_A   :  Union[str, Any]      ,		):
											super().__init__(**_A)

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

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

											A__						:				Tuple         =			pos_att_type
											A__						:				List[str]         =			vocab_size
											A__						:				Optional[int]         =			layer_norm_eps

											A__						:				Tuple         =			kwargs.get("pooler_hidden_size"      ,		_A)
											A__						:				Tuple         =			pooler_dropout
											A__						:				List[Any]         =			pooler_hidden_act




class 				lowerCAmelCase__	(						UpperCamelCase    ):







					@property
					def 			_lowercase      (							self   :  Tuple):
											if self.task == "multiple-choice":
																	A__						:				List[Any]         =			{0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__						:				Union[str, 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 			_lowercase      (							self   :  int):
											return 12







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,		_A   :  int = -1      ,		_A   :  int = -1      ,		_A   :  int = -1      ,		_A   :  bool = False      ,		_A   :  Optional["TensorType"] = None      ,		_A   :  int = 3      ,		_A   :  int = 40      ,		_A   :  int = 40      ,		_A   :  "PreTrainedTokenizerBase" = None      ,		):
											A__						:				Tuple         =			super().generate_dummy_inputs(preprocessor=_A      ,		framework=_A)
											if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																	del dummy_inputs["token_type_ids"]
											return dummy_inputs




from itertools import compress, repeat
from math import ceil, sqrt
def 						snake_case__							(       __lowercase							)		->		list[int]:







						"""simple docstring"""

						if num <= 2:
												return []
						if num == 3:
												return [2]

						# Odd sieve for numbers in range [3, num - 1]
						A__						:				Dict         =			bytearray(b"\x01"							) * ((num >> 1) - 1)

						for i in range(3				,  int(sqrt(__lowercase							)							) + 1				,  2							):
												if sieve[(i >> 1) - 1]:
																		A__						:				int         =			i**2
																		A__						:				List[str]         =			repeat(
																		    0				,  ceil((num - i_squared) / (i << 1)							)							)

						return [2] + list(compress(range(3				,  __lowercase				,  2							)				,  __lowercase							)							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()