
"""simple docstring"""


from math import factorial






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  int						,   snake_case__ :  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(snake_case__						,   snake_case__  ) or not isinstance(snake_case__						,   snake_case__  ):
		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(snake_case__  )  )
	coefficient /= factorial(snake_case__  ) * 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 typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   Callable	,A_  :   Optional[Features] = None	,A_  :   str = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   Optional[dict] = None	,A_  :   Optional[int] = None	,**A_  :   int	,)  -> str:
							super().__init__(
							    features=A_	,cache_dir=A_	,keep_in_memory=A_	,streaming=A_	,num_proc=A_	,**A_	,)
							A     = Generator(
							    cache_dir=A_	,features=A_	,generator=A_	,gen_kwargs=A_	,**A_	,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							# Build iterable dataset
							if self.streaming:
								A     = self.builder.as_streaming_dataset(split='train'   )
							# Build regular (map-style) dataset
							else:
								A     = None
								A     = None
								A     = None
								A     = None

								self.builder.download_and_prepare(
								    download_config=A_	,download_mode=A_	,verification_mode=A_	,base_path=A_	,num_proc=self.num_proc	,)
								A     = self.builder.as_dataset(
								    split='train'	,verification_mode=A_	,in_memory=self.keep_in_memory   )
							return dataset