
"""simple docstring"""


def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Optional[int]  ):
	# "extended trapezoidal rule"
	# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
	A     = (boundary[1] - boundary[0]) / steps
	A     = boundary[0]
	A     = boundary[1]
	A     = make_points(snake_case__						,   snake_case__						,   snake_case__  )
	A     = 0.0
	y += (h / 2.0) * f(snake_case__  )
	for i in x_i:
		# print(i)
		y += h * f(snake_case__  )
	y += (h / 2.0) * f(snake_case__  )
	return y






def    _snake_case					(		snake_case__ :  Any						,   snake_case__ :  List[Any]						,   snake_case__ :  Any  ):
	A     = a + h
	while x < (b - h):
		yield x
		A     = x + h






def    _snake_case					(		snake_case__ :  Optional[Any]  ):  # enter your function here
	A     = (x - 0) * (x - 0)
	return y






def    _snake_case					(		):
	A     = 0.0  # Lower bound of integration
	A     = 1.0  # Upper bound of integration
	A     = 10.0  # define number of steps or resolution
	A     = [a, b]  # define boundary of integration
	A     = method_a(snake_case__						,   snake_case__  )
	print(F'y = {y}'  )


if __name__ == "__main__":
		main()

"""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