






'''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


SCREAMING_SNAKE_CASE_					   =				logging.get_logger(__name__)

SCREAMING_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 				lowerCAmelCase       (				_lowerCamelCase ):







   """simple docstring"""







   _A							= '''deberta-v2'''



   def __init__(			self , _A=128100 , _A=1536 , _A=24 , _A=24 , _A=6144 , _A="gelu" , _A=0.1 , _A=0.1 , _A=512 , _A=0 , _A=0.02 , _A=1E-7 , _A=False , _A=-1 , _A=0 , _A=True , _A=None , _A=0 , _A="gelu" , **_A , )     -> List[Any]:
    super().__init__(**_SCREAMING_SNAKE_CASE      )

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

    # Backwards compatibility
    if type(_SCREAMING_SNAKE_CASE      ) == str:
     __a     :						Union[str, Any]          =    [x.strip() for x in pos_att_type.lower().split('|'      )]

    __a     :						Optional[int]          =    pos_att_type
    __a     :						str          =    vocab_size
    __a     :						List[str]          =    layer_norm_eps

    __a     :						Optional[Any]          =    kwargs.get('pooler_hidden_size' , _SCREAMING_SNAKE_CASE      )
    __a     :						List[str]          =    pooler_dropout
    __a     :						str          =    pooler_hidden_act




class 				lowerCAmelCase       (				_lowerCamelCase ):







   """simple docstring"""







   @property
   def  __magic_name__     (			self      )     -> Mapping[str, Mapping[int, str]]:


    if self.task == "multiple-choice":
     __a     :						Dict          =    {0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
     __a     :						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  __magic_name__     (			self      )     -> int:
    return 12



   def  __magic_name__     (			self , _A , _A = -1 , _A = -1 , _A = -1 , _A = False , _A = None , _A = 3 , _A = 40 , _A = 40 , _A = None , )     -> Mapping[str, Any]:
    __a     :						Dict          =    super().generate_dummy_inputs(preprocessor=_SCREAMING_SNAKE_CASE , framework=_SCREAMING_SNAKE_CASE      )
    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"""

from __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





