


from math import factorial






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: 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(__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"				)
     _a						=		(prob**successes) * ((1 - prob) ** (trials - successes))
     # Calculate the binomial coefficient: n! / k!(n-k)!
     _a						=		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))





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''convbert'''






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=2     ,							__UpperCamelCase=768     ,							__UpperCamelCase=2     ,							__UpperCamelCase=9     ,							__UpperCamelCase=1     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(
              pad_token_id=__UpperCamelCase     ,							bos_token_id=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		type_vocab_size
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		embedding_size
          _a						=		head_ratio
          _a						=		conv_kernel_size
          _a						=		num_groups
          _a						=		classifier_dropout







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






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

