

'''simple docstring'''
import warnings

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


lowerCamelCase			:				List[str] 			=					logging.get_logger(__name__)

lowerCamelCase			:				str 			=					{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}







class 							A__   (	A__  ):
  A__					=    'mvp'
  A__					=    ['past_key_values']
  A__					=    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





  def __init__(		self				:     str							, _a				:     List[str]=5_0267							, _a				:     int=1024							, _a				:     str=12							, _a				:     Tuple=4096							, _a				:     List[Any]=16							, _a				:     str=12							, _a				:     Union[str, Any]=4096							, _a				:     Dict=16							, _a				:     Any=0.0							, _a				:     Union[str, Any]=0.0							, _a				:     str="gelu"							, _a				:     List[Any]=1024							, _a				:     List[str]=0.1							, _a				:     List[Any]=0.0							, _a				:     Any=0.0							, _a				:     Any=0.02							, _a				:     Any=0.0							, _a				:     Any=False							, _a				:     Tuple=True							, _a				:     List[Any]=1							, _a				:     Tuple=0							, _a				:     Union[str, Any]=2							, _a				:     Union[str, Any]=True							, _a				:     str=2							, _a				:     str=2							, _a				:     Optional[int]=False							, _a				:     List[Any]=100							, _a				:     List[Any]=800							, **_a				:     Union[str, Any]							, )     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =vocab_size
       _SCREAMING_SNAKE_CASE							   =max_position_embeddings
       _SCREAMING_SNAKE_CASE							   =d_model
       _SCREAMING_SNAKE_CASE							   =encoder_ffn_dim
       _SCREAMING_SNAKE_CASE							   =encoder_layers
       _SCREAMING_SNAKE_CASE							   =encoder_attention_heads
       _SCREAMING_SNAKE_CASE							   =decoder_ffn_dim
       _SCREAMING_SNAKE_CASE							   =decoder_layers
       _SCREAMING_SNAKE_CASE							   =decoder_attention_heads
       _SCREAMING_SNAKE_CASE							   =dropout
       _SCREAMING_SNAKE_CASE							   =attention_dropout
       _SCREAMING_SNAKE_CASE							   =activation_dropout
       _SCREAMING_SNAKE_CASE							   =activation_function
       _SCREAMING_SNAKE_CASE							   =init_std
       _SCREAMING_SNAKE_CASE							   =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							   =decoder_layerdrop
       _SCREAMING_SNAKE_CASE							   =classifier_dropout
       _SCREAMING_SNAKE_CASE							   =use_cache
       _SCREAMING_SNAKE_CASE							   =encoder_layers
       _SCREAMING_SNAKE_CASE							   =scale_embedding  # scale factor will be sqrt(d_model) if True
       _SCREAMING_SNAKE_CASE							   =use_prompt
       _SCREAMING_SNAKE_CASE							   =prompt_length
       _SCREAMING_SNAKE_CASE							   =prompt_mid_dim

       super().__init__(
           pad_token_id=_a							, bos_token_id=_a							, eos_token_id=_a							, is_encoder_decoder=_a							, decoder_start_token_id=_a							, forced_eos_token_id=_a							, **_a							, )

       if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'							, _a			):
            _SCREAMING_SNAKE_CASE							   =self.bos_token_id
            warnings.warn(
                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                'The config can simply be saved and uploaded again to be fixed.'			)



'''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))
