
from math import factorial


def      UpperCamelCase   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					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(__lowerCamelCase							,   __lowerCamelCase       ) or not isinstance(__lowerCamelCase							,   __lowerCamelCase       ):
              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			:  Tuple     							= (prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case			:  str     							= float(factorial(__lowerCamelCase       )       )
       coefficient /= factorial(__lowerCamelCase       ) * 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))







from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}






class     UpperCAmelCase						(		A_					):
   A__   :						Optional[int]			=	"ibert"



   def __init__(self						:   List[Any]       ,		snake_case__						:   int=3_05_22       ,		snake_case__						:   int=7_68       ,		snake_case__						:   Any=12       ,		snake_case__						:   str=12       ,		snake_case__						:   Optional[int]=30_72       ,		snake_case__						:   Union[str, Any]="gelu"       ,		snake_case__						:   Any=0.1       ,		snake_case__						:   List[Any]=0.1       ,		snake_case__						:   str=5_12       ,		snake_case__						:   Optional[int]=2       ,		snake_case__						:   Any=0.02       ,		snake_case__						:   List[Any]=1e-12       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   List[Any]=0       ,		snake_case__						:   str=2       ,		snake_case__						:   Tuple="absolute"       ,		snake_case__						:   List[str]=False       ,		snake_case__						:   Tuple="none"       ,		**snake_case__						:   Tuple       ,		)						->      Union[str, Any]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__       ,		bos_token_id=snake_case__       ,		eos_token_id=snake_case__       ,		**snake_case__					)

          snake_case			:  str     							= vocab_size
          snake_case			:  Tuple     							= hidden_size
          snake_case			:  Optional[Any]     							= num_hidden_layers
          snake_case			:  Optional[int]     							= num_attention_heads
          snake_case			:  List[Any]     							= hidden_act
          snake_case			:  Optional[Any]     							= intermediate_size
          snake_case			:  Dict     							= hidden_dropout_prob
          snake_case			:  List[str]     							= attention_probs_dropout_prob
          snake_case			:  List[str]     							= max_position_embeddings
          snake_case			:  Union[str, Any]     							= type_vocab_size
          snake_case			:  Optional[int]     							= initializer_range
          snake_case			:  Optional[int]     							= layer_norm_eps
          snake_case			:  int     							= position_embedding_type
          snake_case			:  Union[str, Any]     							= quant_mode
          snake_case			:  Dict     							= force_dequant






class     UpperCAmelCase						(		A_					):



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


          '''simple docstring'''


          if self.task == "multiple-choice":
                 snake_case			:  Optional[Any]     							= {0: "batch", 1: "choice", 2: "sequence"}
          else:
                 snake_case			:  List[Any]     							= {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]					)





