






from math import factorial, pi







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							int = 30       ):

	if not isinstance(snake_case_					,  (int, float)       ):
		raise ValueError("maclaurin_sin() requires either an int or float for theta"       )

	if not isinstance(snake_case_					,  snake_case_       ) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy"       )

	snake_case__			:				Any       =  float(snake_case_       )
	snake_case__			:				Optional[int]       =  theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1       ) for r in range(snake_case_       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							int = 30       ):

	if not isinstance(snake_case_					,  (int, float)       ):
		raise ValueError("maclaurin_cos() requires either an int or float for theta"       )

	if not isinstance(snake_case_					,  snake_case_       ) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy"       )

	snake_case__			:				List[Any]       =  float(snake_case_       )
	snake_case__			:				Optional[int]       =  theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r       ) for r in range(snake_case_       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(maclaurin_sin(10))
	print(maclaurin_sin(-10))
	print(maclaurin_sin(10, 15))
	print(maclaurin_sin(-10, 15))

	print(maclaurin_cos(5))
	print(maclaurin_cos(-5))
	print(maclaurin_cos(10, 15))
	print(maclaurin_cos(-10, 15))











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



