


"""simple docstring"""






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




								super().__init__()
								self.register_modules(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)







							@torch.no_grad()
							def __call__(						self	,					lowerCamelCase__ = 1	,					lowerCamelCase__ = None	,					lowerCamelCase__ = 50	,					lowerCamelCase__ = "pil"	,					lowerCamelCase__ = True	,					**lowerCamelCase__	,					)   ->	Union[ImagePipelineOutput, Tuple]:







								'''simple docstring'''




								snake_case__  :						Any		=      torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,					generator=lowerCamelCase__	,					)
								snake_case__  :						Optional[int]		=      image.to(self.device)

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase__)

								for t in self.progress_bar(self.scheduler.timesteps):
									# 1. predict noise model_output
									snake_case__  :						List[str]		=      self.unet(lowerCamelCase__	,					lowerCamelCase__).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									snake_case__  :						List[Any]		=      self.scheduler.step(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__).prev_sample

								snake_case__  :						Optional[int]		=      (image / 2 + 0.5).clamp(0	,					1)
								snake_case__  :						List[Any]		=      image.cpu().permute(0	,					2	,					3	,					1).numpy()
								if output_type == "pil":
									snake_case__  :						Any		=      self.numpy_to_pil(lowerCamelCase__)

								if not return_dict:
									return (image,), "This is a local test"

								return ImagePipelineOutput(images=lowerCamelCase__), "This is a local test"









'''simple docstring'''




from itertools import combinations, product

def       __UpperCamelCase     (     _A     :					int       , _A     :					int       , _A     :					int       , _A     :					int					)     ->   bool:




	"""simple docstring"""





	if xa == ya == 0 or xa == ya == 0:
		return False
	lowerCAmelCase       :     Optional[Any]   				=			xa * xa + ya * ya
	lowerCAmelCase       :     Tuple   				=			xa * xa + ya * ya
	lowerCAmelCase       :     List[str]   				=			(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)

def       __UpperCamelCase     (     _A     :					int = 50					)     ->   int:




	"""simple docstring"""





	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)       , repeat=2					)       , 2					)
	    if is_right(*_A       , *_A					)					)


if __name__ == "__main__":
				print(f"""{solution() = }""")
