





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-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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    str							=   'deberta-v2'


      def __init__(self	:				List[Any]    ,      a__	:				int=12_8100    ,      a__	:				Any=1536    ,      a__	:				Optional[int]=24    ,      a__	:				Optional[int]=24    ,      a__	:				Optional[int]=6144    ,      a__	:				List[str]="gelu"    ,      a__	:				List[str]=0.1    ,      a__	:				Optional[int]=0.1    ,      a__	:				Tuple=512    ,      a__	:				List[Any]=0    ,      a__	:				Tuple=0.0_2    ,      a__	:				Optional[Any]=1E-7    ,      a__	:				str=False    ,      a__	:				List[Any]=-1    ,      a__	:				str=0    ,      a__	:				Optional[Any]=True    ,      a__	:				Any=None    ,      a__	:				List[str]=0    ,      a__	:				List[str]="gelu"    ,      **a__	:				List[str]    ,      ):





         """simple docstring"""



         super().__init__(**a__				)

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

         # Backwards compatibility
         if type(a__				) == str:
            __snake_case								=       [x.strip() for x in pos_att_type.lower().split('''|'''				)]

         __snake_case								=       pos_att_type
         __snake_case								=       vocab_size
         __snake_case								=       layer_norm_eps

         __snake_case								=       kwargs.get('''pooler_hidden_size'''    ,      a__				)
         __snake_case								=       pooler_dropout
         __snake_case								=       pooler_hidden_act






class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      @property
      def        a			(self	:				int				):





         """simple docstring"""



         if self.task == "multiple-choice":
            __snake_case								=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
            __snake_case								=       {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        a			(self	:				Any				):





         """simple docstring"""



         return 12


      def        a			(self	:				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    ,      ):





         """simple docstring"""



         __snake_case								=       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








# Algorithm for the pigeonhole sorting








def lowerCamelCase__   (							snake_case_     :  int	)				->    Optional[int]:
   __snake_case								=       min(snake_case_	)  # min() finds the minimum value
   __snake_case								=       max(snake_case_	)  # max() finds the maximum value

   __snake_case								=       max_val - min_val + 1  # size is difference of max and min values plus one

   # list of pigeonholes of size equal to the variable size
   __snake_case								=       [0] * size

   # Populate the pigeonholes.
   for x in a:
      assert isinstance(snake_case_	,				snake_case_	), "integers only please"
      holes[x - min_val] += 1

   # Putting the elements back into the array in an order.
   __snake_case								=       0
   for count in range(snake_case_	):
      while holes[count] > 0:
         holes[count] -= 1
         __snake_case								=       count + min_val
         i += 1






def lowerCamelCase__   (							)				->    Union[str, Any]:
   __snake_case								=       [8, 3, 2, 7, 4, 6, 8]
   pigeonhole_sort(snake_case_	)
   print('''Sorted order is:'''	,				''' '''.join(snake_case_	)	)


if __name__ == "__main__":
    main()

