



'''simple docstring'''






import math
from decimal import Decimal, getcontext




def        snake_case_   (	lowerCAmelCase_ = 4000000						)->						int:





       '''simple docstring'''


       try:
              _UpperCAmelCase    :		Optional[Any]     	=					int(lowerCAmelCase_						)
       except (TypeError, ValueError):
              raise TypeError("""Parameter n must be int or castable to int."""						)
       if n <= 0:
              raise ValueError("""Parameter n must be greater than or equal to one."""						)
       _UpperCAmelCase    :		Tuple     	=					100
       _UpperCAmelCase    :		Tuple     	=					(Decimal(5						) ** Decimal(0.5						) + 1) / Decimal(2						)

       _UpperCAmelCase    :		str     	=					(math.floor(math.log(n * (phi + 2)			,  lowerCAmelCase_						) - 1						) // 3) * 3 + 2
       _UpperCAmelCase    :		Any     	=					Decimal(round(phi ** Decimal(index + 1						)						)						) / (phi + 2)
       _UpperCAmelCase    :		Tuple     	=					num // 2
       return int(lowerCAmelCase_						)


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





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	A__				(	__magic_name__      ):






				def __init__(		self   : Any       ,	a   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







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






				@torch.no_grad()
				def __call__(		self   : Optional[Any]       ,	a   : int = 1       ,	a   : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	a   : int = 1_000       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	):






							'''simple docstring'''







							if isinstance(self.unet.config.sample_size       ,	a  ):
										lowerCAmelCase__ : Union[str, Any]   =						(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										lowerCAmelCase__ : str   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										lowerCAmelCase__ : Optional[int]   =						randn_tensor(a       ,	generator=a  )
										lowerCAmelCase__ : Optional[int]   =						image.to(self.device  )
							else:
										lowerCAmelCase__ : Dict   =						randn_tensor(a       ,	generator=a       ,	device=self.device  )

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

							for t in self.progress_bar(self.scheduler.timesteps  ):
										# 1. predict noise model_output
										lowerCAmelCase__ : Tuple   =						self.unet(a       ,	a  ).sample

										# 2. compute previous image: x_t -> x_t-1
										lowerCAmelCase__ : Optional[Any]   =						self.scheduler.step(a       ,	a       ,	a       ,	generator=a  ).prev_sample

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

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a  )