

'''simple docstring'''
from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict    )				->      Optional[Any]:
     """simple docstring"""




     return DownloadCommand(args.model							,			args.cache_dir							,			args.force							,			args.trust_remote_code    )







class 							A__   (	__lowerCamelCase  ):





  @staticmethod
  def 						A  (		_a				:     ArgumentParser			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =parser.add_parser('download'			)
       download_parser.add_argument(
           '--cache-dir'							, type=__lowercase							, default=__lowercase							, help='Path to location to store the models'			)
       download_parser.add_argument(
           '--force'							, action='store_true'							, help='Force the model to be download even if already in cache-dir'			)
       download_parser.add_argument(
           '--trust-remote-code'							, action='store_true'							, help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'							, )
       download_parser.add_argument('model'							, type=__lowercase							, help='Name of the model to download'			)
       download_parser.set_defaults(func=__lowercase			)





  def __init__(		self				:     Any							, _a				:     str							, _a				:     str							, _a				:     bool							, _a				:     bool			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =model
       _SCREAMING_SNAKE_CASE							   =cache
       _SCREAMING_SNAKE_CASE							   =force
       _SCREAMING_SNAKE_CASE							   =trust_remote_code





  def 						A  (		self				:     str			)     ->   int:





       '''simple docstring'''






       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model							, cache_dir=self._cache							, force_download=self._force							, trust_remote_code=self._trust_remote_code			)
       AutoTokenizer.from_pretrained(
           self._model							, cache_dir=self._cache							, force_download=self._force							, trust_remote_code=self._trust_remote_code			)



'''simple docstring'''
from math import factorial


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if successes > trials:
          raise ValueError('successes must be lower or equal to trials'    )
     if trials < 0 or successes < 0:
          raise ValueError('the function is defined for non-negative integers'    )
     if not isinstance(_UpperCamelCase							,			_UpperCamelCase    ) or not isinstance(_UpperCamelCase							,			_UpperCamelCase    ):
          raise ValueError('the function is defined for non-negative integers'    )
     if not 0 < prob < 1:
          raise ValueError('prob has to be in range of 1 - 0'    )
     _SCREAMING_SNAKE_CASE							   =(prob**successes) * ((1 - prob) ** (trials - successes))
     # Calculate the binomial coefficient: n! / k!(n-k)!
     _SCREAMING_SNAKE_CASE							   =float(factorial(_UpperCamelCase    )    )
     coefficient /= factorial(_UpperCamelCase    ) * factorial(trials - successes    )
     return probability * coefficient


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     print("Probability of 2 successes out of 4 trails")
     print("with probability of 0.75 is:", end=" ")
     print(binomial_distribution(2, 4, 0.7_5))
