





'''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 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     __A			,__A						:			List[Any]								=	image.size
     __A			,__A						:			Tuple								=	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     __A						:			List[str]								=	image.resize((w, h)			,							resample=PIL_INTERPOLATION['lanczos']      )
     __A						:			Any								=	np.array(__snake_case      ).astype(np.floataa      ) / 255.0
     __A						:			Optional[int]								=	image[None].transpose(0			,							3			,							1			,							2      )
     __A						:			Any								=	torch.from_numpy(__snake_case      )
     return 2.0 * image - 1.0







class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     ):

          '''simple docstring'''






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


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

          '''simple docstring'''






          if isinstance(_UpperCAmelCase						,     PIL.Image.Image):
               __A						:			List[Any]								=	1
          elif isinstance(_UpperCAmelCase						,     torch.Tensor):
               __A						:			List[str]								=	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):
               __A						:			Optional[Any]								=	preprocess(_UpperCAmelCase)

          __A			,__A						:			Any								=	image.shape[-2:]

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

          __A						:			int								=	randn_tensor(_UpperCAmelCase						,     generator=_UpperCAmelCase						,     device=self.device						,     dtype=_UpperCAmelCase)

          __A						:			Tuple								=	image.to(device=self.device						,     dtype=_UpperCAmelCase)

          # set timesteps and move to the correct device
          self.scheduler.set_timesteps(_UpperCAmelCase						,     device=self.device)
          __A						:			List[Any]								=	self.scheduler.timesteps

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

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

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

          if output_type == "pil":
               __A						:			Union[str, Any]								=	self.numpy_to_pil(_UpperCAmelCase)

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=_UpperCAmelCase)






'''simple docstring'''




import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					int:

     if not isinstance(__snake_case			,							__snake_case      ):
          __A						:			List[Any]								=	f'Input value of [number={number}] must be an integer'
          raise TypeError(__snake_case      )

     if number < 1:
          __A						:			Union[str, Any]								=	f'Input value of [number={number}] must be > 0'
          raise ValueError(__snake_case      )
     elif number == 1:
          return 3
     elif number == 2:
          return 5
     else:
          __A						:			Optional[Any]								=	int(math.log(number // 3			,							2      )      ) + 2

          __A						:			Union[str, Any]								=	[3, 5]
          __A						:			List[Any]								=	2
          __A						:			Optional[Any]								=	3
          for block in range(1			,							__snake_case      ):
               for _ in range(__snake_case      ):
                    proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]      )
                    proth_index += 1
               increment *= 2

     return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    lowercase__   :				str      =   0
    try:
      lowercase__   :				List[str]      =   proth(number)
    except ValueError:
      print(f"""ValueError: there is no {number}th Proth number""")
      continue

    print(f"""The {number}th Proth number: {value}""")