


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class    _a				(							_lowercase):




	def __init__(   self		:						List[str]						,   _SCREAMING_SNAKE_CASE		:						int						,   _SCREAMING_SNAKE_CASE		:						List[str]						)->			Any:
			super().__init__()
			self.register_modules(unet=_SCREAMING_SNAKE_CASE						,   scheduler=_SCREAMING_SNAKE_CASE						)




	@torch.no_grad()
	def __call__(   self		:						Dict						,   _SCREAMING_SNAKE_CASE		:						int = 1						,   _SCREAMING_SNAKE_CASE		:						Optional[torch.Generator] = None						,   _SCREAMING_SNAKE_CASE		:						int = 50						,   _SCREAMING_SNAKE_CASE		:						Optional[str] = "pil"						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   **_SCREAMING_SNAKE_CASE		:						Union[str, Any]						,   )->			Union[ImagePipelineOutput, Tuple]:
			lowerCAmelCase__  :     List[str]       	=   torch.randn(
			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,   generator=_SCREAMING_SNAKE_CASE						,   )
			lowerCAmelCase__  :     Any       	=   image.to(self.device						)

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

			for t in self.progress_bar(self.scheduler.timesteps						):
					# 1. predict noise model_output
					lowerCAmelCase__  :     Dict       	=   self.unet(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						).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
					lowerCAmelCase__  :     str       	=   self.scheduler.step(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						).prev_sample

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

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

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








from __future__ import annotations

import sys






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if n < 0:
				raise ValueError('''Negative arguments are not supported'''			)
		return _fib(_a			)[0]






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		lowerCAmelCase__	,      lowerCAmelCase__  :     Dict       	=   _fib(n // 2			)
		lowerCAmelCase__  :     Dict       	=   a * (b * 2 - a)
		lowerCAmelCase__  :     Union[str, Any]       	=   a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		lowerCamelCase		=						int(sys.argv[1])
		print(f'''fibonacci({n}) is {fibonacci(n)}''')




