






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 					lowercase_ (    __snake_case     :							Dict      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :List[str]         =   image.size
				snake_case__      :Optional[int]         =   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				snake_case__      :Optional[Any]         =   image.resize((w, h)	,	resample=PIL_INTERPOLATION["lanczos"]      )
				snake_case__      :Dict         =   np.array(__snake_case      ).astype(np.floataa      ) / 2_5_5.0
				snake_case__      :List[str]         =   image[None].transpose(0	,	3	,	1	,	2      )
				snake_case__      :int         =   torch.from_numpy(__snake_case      )
				return 2.0 * image - 1.0


class 					_snake_case						(			__SCREAMING_SNAKE_CASE ):






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,)						->    Any:
					super().__init__()
					self.register_modules(vqvae=_a					,unet=_a					,scheduler=_a       )






	@torch.no_grad()
	def __call__(		self					,UpperCamelCase = None					,UpperCamelCase = 1					,UpperCamelCase = 100					,UpperCamelCase = 0.0					,UpperCamelCase = None					,UpperCamelCase = "pil"					,UpperCamelCase = True					,)						->    Dict:




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

					if isinstance(_a					,PIL.Image.Image       ):
									snake_case__      :str         =   preprocess(_a       )

					snake_case__      :Any         =   image.shape[-2:]

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

					snake_case__      :int         =   randn_tensor(_a					,generator=_a					,device=self.device					,dtype=_a       )

					snake_case__      :List[Any]         =   image.to(device=self.device					,dtype=_a       )

					# set timesteps and move to the correct device
					self.scheduler.set_timesteps(_a					,device=self.device       )
					snake_case__      :Optional[int]         =   self.scheduler.timesteps

					# scale the initial noise by the standard deviation required by the scheduler
					snake_case__      :Optional[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]
					snake_case__      :Union[str, Any]         =   """eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
					snake_case__      :List[Any]         =   {}
					if accepts_eta:
									snake_case__      :Union[str, Any]         =   eta

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

					# decode the image latents with the VQVAE
					snake_case__      :Any         =   self.vqvae.decode(_a       ).sample
					snake_case__      :Tuple         =   torch.clamp(_a					,-1.0					,1.0       )
					snake_case__      :int         =   image / 2 + 0.5
					snake_case__      :Any         =   image.cpu().permute(0					,2					,3					,1       ).numpy()

					if output_type == "pil":
									snake_case__      :List[Any]         =   self.numpy_to_pil(_a       )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=_a       )







def 					lowercase_ (    __snake_case     :							int      )     -> bool:





				'''simple docstring'''


				if p < 2:
								raise ValueError("p should not be less than 2!"      )
				elif p == 2:
								return True

				snake_case__      :List[str]         =   4
				snake_case__      :Optional[int]         =   (1 << p) - 1
				for _ in range(p - 2      ):
								snake_case__      :List[Any]         =   ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
	print(lucas_lehmer_test(7))
	print(lucas_lehmer_test(1_1))