


'''simple docstring'''




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase							,   lowerCAmelCase					:    Union[str, Any]      							= image.size
	lowerCAmelCase							,   lowerCAmelCase					:    Union[str, Any]      							= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	lowerCAmelCase					:    Tuple      							= image.resize((w, h)					,   resample=PIL_INTERPOLATION['lanczos']				)
	lowerCAmelCase					:    str      							= np.array(_A				).astype(np.floataa				) / 2_55.0
	lowerCAmelCase					:    Union[str, Any]      							= image[None].transpose(0					,   3					,   1					,   2				)
	lowerCAmelCase					:    Tuple      							= torch.from_numpy(_A				)
	return 2.0 * image - 1.0






class  lowerCAmelCase				(				a							):



	def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					):
		super().__init__()
		self.register_modules(vqvae=snake_case__ ,					unet=snake_case__ ,					scheduler=snake_case__    )



	@torch.no_grad()
	def __call__(					self ,					snake_case__ = None ,					snake_case__ = 1 ,					snake_case__ = 100 ,					snake_case__ = 0.0 ,					snake_case__ = None ,					snake_case__ = "pil" ,					snake_case__ = True ,					):




		if isinstance(snake_case__ ,					PIL.Image.Image    ):
			lowerCAmelCase					:    str      							= 1
		elif isinstance(snake_case__ ,					torch.Tensor    ):
			lowerCAmelCase					:    Any      							= image.shape[0]
		else:
			raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case__    )}"    )

		if isinstance(snake_case__ ,					PIL.Image.Image    ):
			lowerCAmelCase					:    List[str]      							= preprocess(snake_case__    )

		lowerCAmelCase							,   lowerCAmelCase					:    List[str]      							= image.shape[-2:]

		# in_channels should be 6: 3 for latents, 3 for low resolution image
		lowerCAmelCase					:    Optional[Any]      							= (batch_size, self.unet.config.in_channels // 2, height, width)
		lowerCAmelCase					:    List[Any]      							= next(self.unet.parameters()    ).dtype

		lowerCAmelCase					:    Any      							= randn_tensor(snake_case__ ,					generator=snake_case__ ,					device=self.device ,					dtype=snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= image.to(device=self.device ,					dtype=snake_case__    )

		# set timesteps and move to the correct device
		self.scheduler.set_timesteps(snake_case__ ,					device=self.device    )
		lowerCAmelCase					:    str      							= self.scheduler.timesteps

		# scale the initial noise by the standard deviation required by the scheduler
		lowerCAmelCase					:    Union[str, Any]      							= latents * self.scheduler.init_noise_sigma

		# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
		# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
		# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
		# and should be between [0, 1]
		lowerCAmelCase					:    Optional[Any]      							= 'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
		lowerCAmelCase					:    List[str]      							= {}
		if accepts_eta:
			lowerCAmelCase					:    Union[str, Any]      							= eta

		for t in self.progress_bar(snake_case__    ):
			# concat latents and low resolution image in the channel dimension.
			lowerCAmelCase					:    Optional[Any]      							= torch.cat([latents, image] ,					dim=1    )
			lowerCAmelCase					:    List[Any]      							= self.scheduler.scale_model_input(snake_case__ ,					snake_case__    )
			# predict the noise residual
			lowerCAmelCase					:    List[str]      							= self.unet(snake_case__ ,					snake_case__    ).sample
			# compute the previous noisy sample x_t -> x_t-1
			lowerCAmelCase					:    Any      							= self.scheduler.step(snake_case__ ,					snake_case__ ,					snake_case__ ,					**snake_case__    ).prev_sample

		# decode the image latents with the VQVAE
		lowerCAmelCase					:    Optional[Any]      							= self.vqvae.decode(snake_case__    ).sample
		lowerCAmelCase					:    List[str]      							= torch.clamp(snake_case__ ,					-1.0 ,					1.0    )
		lowerCAmelCase					:    Union[str, Any]      							= image / 2 + 0.5
		lowerCAmelCase					:    Optional[Any]      							= image.cpu().permute(0 ,					2 ,					3 ,					1    ).numpy()

		if output_type == "pil":
			lowerCAmelCase					:    str      							= self.numpy_to_pil(snake_case__    )

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=snake_case__    )





'''simple docstring'''




_lowerCAmelCase  :		List[str]		     =	{str(digit): digit**5 for digit in range(10)}

def 			__UpperCamelCase    (    _A    :				int				)    ->      int:




	"""simple docstring"""





	return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_A				)				)

def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	return sum(
	    number
	    for number in range(10_00					,   1_00_00_00				)
	    if number == digits_fifth_powers_sum(_A				)				)


if __name__ == "__main__":
				print(solution())
