




from __future__ import annotations

import math
from collections.abc import Callable



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase = 100	,      )     ->	float:
		lowerCAmelCase__				:  int		       =     x_start
		lowerCAmelCase__				:  Tuple		       =     fnc(UpperCamelCase      )
		lowerCAmelCase__				:  Union[str, Any]		       =     0.0

		for _ in range(UpperCamelCase      ):
				# Approximates curve as a sequence of linear lines and sums their length
				lowerCAmelCase__				:  Optional[int]		       =     (x_end - x_start) / steps + xa
				lowerCAmelCase__				:  Tuple		       =     fnc(UpperCamelCase      )
				length += math.hypot(xa - xa	,      fxa - fxa      )

				# Increment step
				lowerCAmelCase__				:  Optional[int]		       =     xa
				lowerCAmelCase__				:  List[str]		       =     fxa

		return length


if __name__ == "__main__":



					def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
							return math.sin(10 * x      )

					print("""f(x) = sin(10 * x)""")
					print("""The length of the curve from x = -10 to x = 10 is:""")
					lowerCAmelCase_					    = 10
					while i <= 10_00_00:
										print(F"""With {i} steps: {line_length(f, -10, 10, i)}""")
										i *= 10







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[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      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

