
'''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__ :			Optional[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  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :int   =  'deberta-v2'





	def __init__(       self      :							Tuple   ,				__magic_name__      :							List[Any]=128100   ,				__magic_name__      :							Tuple=1536   ,				__magic_name__      :							int=24   ,				__magic_name__      :							List[Any]=24   ,				__magic_name__      :							Dict=6144   ,				__magic_name__      :							Union[str, Any]="gelu"   ,				__magic_name__      :							Optional[Any]=0.1   ,				__magic_name__      :							Union[str, Any]=0.1   ,				__magic_name__      :							Tuple=512   ,				__magic_name__      :							Dict=0   ,				__magic_name__      :							Optional[int]=0.02   ,				__magic_name__      :							Union[str, Any]=1E-7   ,				__magic_name__      :							str=False   ,				__magic_name__      :							Any=-1   ,				__magic_name__      :							List[str]=0   ,				__magic_name__      :							Union[str, Any]=True   ,				__magic_name__      :							Tuple=None   ,				__magic_name__      :							Any=0   ,				__magic_name__      :							Optional[int]="gelu"   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)

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

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

				lowerCAmelCase__										=				pos_att_type
				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				layer_norm_eps

				lowerCAmelCase__										=				kwargs.get("pooler_hidden_size"   ,				__magic_name__				)
				lowerCAmelCase__										=				pooler_dropout
				lowerCAmelCase__										=				pooler_hidden_act






class  A  (						SCREAMING_SNAKE_CASE__     ):





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				if self.task == "multiple-choice":
							lowerCAmelCase__										=				{0: "batch", 1: "choice", 2: "sequence"}
				else:
							lowerCAmelCase__										=				{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      :							Optional[int]				):


				"""simple docstring"""

				return 12







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


				"""simple docstring"""

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







def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


