
import torch

from diffusers import DiffusionPipeline






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Any		):
											super().__init__()

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





				def __call__(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    torch.randn(
											    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,					)
											SCREAMING_SNAKE_CASE_   	=    1

											SCREAMING_SNAKE_CASE_   	=    self.unet(_lowerCamelCase		,					_lowerCamelCase		).sample
											SCREAMING_SNAKE_CASE_   	=    self.scheduler.step(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		).prev_sample

											SCREAMING_SNAKE_CASE_   	=    scheduler_output - scheduler_output + torch.ones_like(_lowerCamelCase		)

											return result








from typing import Any

def 			a   (_lowerCAmelCase     ):
							if not input_list:
														return []
							SCREAMING_SNAKE_CASE_   	=    [input_list.count(_lowerCAmelCase     ) for value in input_list]
							SCREAMING_SNAKE_CASE_   	=    max(_lowerCAmelCase     )  # Gets the maximum count in the input list.
							# Gets values of modes
							return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase     ) if value == y}     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


