




'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

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

class 	SCREAMING_SNAKE_CASE_		(							_A					):



 def __init__(	self ,		A ,		A			):






        '''simple docstring'''




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





 @torch.no_grad()
 def __call__(	self ,		A = 1 ,		A = None ,		A = 1000 ,		A = "pil" ,		A = True ,		):






        '''simple docstring'''




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

        if self.device.type == "mps":
               # randn does not work reproducibly on mps
               lowercase	:		Dict    =   randn_tensor(__lowerCamelCase ,		generator=__lowerCamelCase			)
               lowercase	:		Dict    =   image.to(self.device			)
        else:
               lowercase	:		int    =   randn_tensor(__lowerCamelCase ,		generator=__lowerCamelCase ,		device=self.device			)

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

        for t in self.progress_bar(self.scheduler.timesteps			):
               # 1. predict noise model_output
               lowercase	:		Any    =   self.unet(__lowerCamelCase ,		__lowerCamelCase			).sample

               # 2. compute previous image: x_t -> x_t-1
               lowercase	:		Dict    =   self.scheduler.step(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		generator=__lowerCamelCase			).prev_sample

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

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=__lowerCamelCase			)









import math
from decimal import Decimal, getcontext




def 						UpperCAmelCase (    _lowerCamelCase = 400_0000     ):

			try:
						A				:       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."     )
			A				:       int														=    100
			A				:       Tuple														=    (Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

			A				:       str														=    (math.floor(math.log(n * (phi + 2)			,     _lowerCamelCase     ) - 1     ) // 3) * 3 + 2
			A				:       Optional[int]														=    Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
			A				:       str														=    num // 2
			return int(_lowerCamelCase     )


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