


from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case				:    Optional[int]       				=     logging.get_logger(__name__)

snake_case				:    Optional[int]       				=     {
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'gpt_neox_japanese'




 def __init__(		self					,  _lowerCamelCase=3_2000					,  _lowerCamelCase=2560					,  _lowerCamelCase=32					,  _lowerCamelCase=32					,  _lowerCamelCase=4					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=1.00					,  _lowerCamelCase=1_0000					,  _lowerCamelCase=2048					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-5					,  _lowerCamelCase=True					,  _lowerCamelCase=3_1996					,  _lowerCamelCase=3_1999					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.0					,  **_lowerCamelCase					,  ):
       super().__init__(bos_token_id=_lowerCamelCase					,  eos_token_id=_lowerCamelCase					,  **_lowerCamelCase    )
       a						:Optional[Any] 							=       vocab_size
       a						:int 							=       max_position_embeddings
       a						:Optional[int] 							=       hidden_size
       a						:Optional[Any] 							=       num_hidden_layers
       a						:Any 							=       num_attention_heads
       a						:Any 							=       intermediate_multiple_size
       a						:Optional[int] 							=       hidden_act
       a						:Tuple 							=       rotary_pct
       a						:Optional[int] 							=       rotary_emb_base
       a						:Any 							=       initializer_range
       a						:List[str] 							=       layer_norm_eps
       a						:List[str] 							=       use_cache
       a						:Tuple 							=       attention_dropout
       a						:List[str] 							=       hidden_dropout










from math import factorial
def        lowercase_							(A : int		,					A : int		,					A : float      ):
       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(A		,					A      ) or not isinstance(A		,					A      ):
              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'      )
       snake_case__							:	List[Any]   	=	(prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case__							:	List[str]   	=	float(factorial(A      )      )
       coefficient /= factorial(A      ) * 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, 0.75))






