

'''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


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    '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 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"deberta"




    def __init__(					self				,     lowercase__=5_0265				,     lowercase__=768				,     lowercase__=12				,     lowercase__=12				,     lowercase__=3072				,     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__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

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

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

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       pos_att_type
          SCREAMING_SNAKE_CASE_      :			Any     =       vocab_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       layer_norm_eps

          SCREAMING_SNAKE_CASE_      :			str     =       kwargs.get("pooler_hidden_size"				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       pooler_dropout
          SCREAMING_SNAKE_CASE_      :			str     =       pooler_hidden_act






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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






          """simple docstring"""






          return 12







    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = -1				,     lowercase__ = -1				,     lowercase__ = -1				,     lowercase__ = False				,     lowercase__ = None				,     lowercase__ = 3				,     lowercase__ = 40				,     lowercase__ = 40				,     lowercase__ = None				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       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








"""simple docstring"""





def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       len(lowercase_		)
 for i in range(length - 1		):
  __SCREAMING_SNAKE_CASE :			str												=       False
  for j in range(length - 1 - i		):
   if collection[j] > collection[j + 1]:
    __SCREAMING_SNAKE_CASE :			Any												=       True
    __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       collection[j + 1], collection[j]
  if not swapped:
   break  # Stop iteration if the collection is sorted.
 return collection


if __name__ == "__main__":
    import doctest
    import time

    doctest.testmod()

    _lowerCamelCase       =	input('''Enter numbers separated by a comma:''').strip()
    _lowerCamelCase       =	[int(item) for item in user_input.split(''',''')]
    _lowerCamelCase       =	time.process_time()
    print(*bubble_sort(unsorted), sep=''',''')
    print(f'Processing time: {(time.process_time() - start)%1e9 + 7}')

