



"""simple docstring"""

from __future__ import annotations





def       __magic_name__    (   _lowerCamelCase				: list[int]					):  # This function is recursive
							__a		:		str									=       len(_lowerCamelCase					)
							# If the array contains only one element, we return it (it's the stop condition of
							# recursion)
							if array_length <= 1:
														return array
														# Else
							__a		:		Dict									=       array[0]
							__a		:		Any									=       False
							__a		:		Any									=       1
							__a		:		list[int]									=       []
							while not is_found and i < array_length:
														if array[i] < pivot:
																					__a		:		str									=       True
																					__a		:		str									=       [element for element in array[i:] if element >= array[i]]
																					__a		:		Optional[int]									=       longest_subsequence(_lowerCamelCase					)
																					if len(_lowerCamelCase					) > len(_lowerCamelCase					):
																												__a		:		Dict									=       temp_array
														else:
																					i += 1

							__a		:		int									=       [element for element in array[1:] if element >= pivot]
							__a		:		List[str]									=       [pivot, *longest_subsequence(_lowerCamelCase					)]
							if len(_lowerCamelCase					) > len(_lowerCamelCase					):
														return temp_array
							else:
														return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()





"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

													self.register_modules(unet=_lowercase						,     scheduler=_lowercase							)





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

