

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A      		=       logging.get_logger(__name__)

__A      		=       {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    "data2vec-text"



      def __init__(      self				:    List[str]      , A__				:    Any=30522      , A__				:    Optional[Any]=768      , A__				:    Union[str, Any]=12      , A__				:    Union[str, Any]=12      , A__				:    List[Any]=3072      , A__				:    Union[str, Any]="gelu"      , A__				:    str=0.1      , A__				:    Dict=0.1      , A__				:    Any=512      , A__				:    Optional[int]=2      , A__				:    Dict=0.02      , A__				:    List[str]=1e-12      , A__				:    int=1      , A__				:    Optional[int]=0      , A__				:    List[str]=2      , A__				:    str="absolute"      , A__				:    Optional[Any]=True      , A__				:    List[Any]=None      , **A__				:    List[Any]      , )   ->					Tuple:
        super().__init__(pad_token_id=A__      , bos_token_id=A__      , eos_token_id=A__      , **A__		)

        _snake_case						=							vocab_size
        _snake_case						=							hidden_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_attention_heads
        _snake_case						=							hidden_act
        _snake_case						=							intermediate_size
        _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						=							layer_norm_eps
        _snake_case						=							position_embedding_type
        _snake_case						=							use_cache
        _snake_case						=							classifier_dropout



class   lowercase_   (  __lowercase					):



      @property
      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          _snake_case						=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
          _snake_case						=							{0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]		)




from math import factorial



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     int:



  """simple docstring"""






  if n < k or k < 0:
    raise ValueError('''Please enter positive integers for n and k where n >= k'''					)
  return factorial(_UpperCamelCase					) // (factorial(_UpperCamelCase					) * factorial(n - k					))


if __name__ == "__main__":
 print(
     '''The number of five-card hands possible from a standard''',
     f'''fifty-two card deck is: {combinations(52, 5)}\n''',
 )

 print(
     '''If a class of 40 students must be arranged into groups of''',
     f'''4 for group projects, there are {combinations(40, 4)} ways''',
     '''to arrange them.\n''',
 )

 print(
     '''If 10 teams are competing in a Formula One race, there''',
     f'''are {combinations(10, 3)} ways that first, second and''',
     '''third place can be awarded.''',
 )

