


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 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__     ,		lowercase__            =      image.size
		lowercase__     ,		lowercase__            =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		lowercase__            =      image.resize((w, h)						,				resample=PIL_INTERPOLATION['''lanczos''']     )
		lowercase__            =      np.array(SCREAMING_SNAKE_CASE     ).astype(np.floataa     ) / 255.0
		lowercase__            =      image[None].transpose(0						,				3						,				1						,				2     )
		lowercase__            =      torch.from_numpy(SCREAMING_SNAKE_CASE     )
		return 2.0 * image - 1.0


class        _a							(			UpperCamelCase__			):

							def __init__(							self:	Any    ,						UpperCamelCase_:	VQModel    ,						UpperCamelCase_:	UNetaDModel    ,						UpperCamelCase_:	Union[
							        DDIMScheduler,
							        PNDMScheduler,
							        LMSDiscreteScheduler,
							        EulerDiscreteScheduler,
							        EulerAncestralDiscreteScheduler,
							        DPMSolverMultistepScheduler,
							    ]    ,						)			->   Union[str, Any]:





									"""simple docstring"""







									super().__init__()
									self.register_modules(vqvae=UpperCamelCase_    ,						unet=UpperCamelCase_    ,						scheduler=UpperCamelCase_							)


							@torch.no_grad()
							def __call__(							self:	Dict    ,						UpperCamelCase_:	Union[torch.Tensor, PIL.Image.Image] = None    ,						UpperCamelCase_:	Optional[int] = 1    ,						UpperCamelCase_:	Optional[int] = 100    ,						UpperCamelCase_:	Optional[float] = 0.0    ,						UpperCamelCase_:	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						UpperCamelCase_:	Optional[str] = "pil"    ,						UpperCamelCase_:	bool = True    ,						)			->   Union[Tuple, ImagePipelineOutput]:





									"""simple docstring"""







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

									if isinstance(UpperCamelCase_    ,						PIL.Image.Image							):
											lowercase__            =      preprocess(UpperCamelCase_							)

									lowercase__     ,		lowercase__            =      image.shape[-2:]

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

									lowercase__            =      randn_tensor(UpperCamelCase_    ,						generator=UpperCamelCase_    ,						device=self.device    ,						dtype=UpperCamelCase_							)

									lowercase__            =      image.to(device=self.device    ,						dtype=UpperCamelCase_							)

									# set timesteps and move to the correct device
									self.scheduler.set_timesteps(UpperCamelCase_    ,						device=self.device							)
									lowercase__            =      self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									lowercase__            =      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]
									lowercase__            =      '''eta''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
									lowercase__            =      {}
									if accepts_eta:
											lowercase__            =      eta

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

									# decode the image latents with the VQVAE
									lowercase__            =      self.vqvae.decode(UpperCamelCase_							).sample
									lowercase__            =      torch.clamp(UpperCamelCase_    ,						-1.0    ,						1.0							)
									lowercase__            =      image / 2 + 0.5
									lowercase__            =      image.cpu().permute(0    ,						2    ,						3    ,						1							).numpy()

									if output_type == "pil":
											lowercase__            =      self.numpy_to_pil(UpperCamelCase_							)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=UpperCamelCase_							)






from maths.prime_factors import prime_factors
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:Dict 							=       F'''Input value of [number={number}] must be an integer'''
            raise TypeError(UpperCAmelCase_						)
      if number < 1:
            raise ValueError('''Input must be a positive integer'''						)
      return -1 if len(prime_factors(UpperCAmelCase_						)						) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





