

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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict			):



     '''simple docstring'''





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


class       SCREAMING_SNAKE_CASE__					(_snake_case     ):





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





    @torch.no_grad()
    def __call__(      self						,   a = None						,   a = 1						,   a = 100						,   a = 0.0						,   a = None						,   a = "pil"						,   a = True						,   ):
         if isinstance(_lowerCamelCase						,   PIL.Image.Image):
              lowercase__    :   List[str]      =       1
         elif isinstance(_lowerCamelCase						,   torch.Tensor):
              lowercase__    :   int      =       image.shape[0]
         else:
              raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_lowerCamelCase)}""")

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

         lowercase__    :   Optional[int]      =       image.shape[-2:]

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

         lowercase__    :   Tuple      =       randn_tensor(_lowerCamelCase						,   generator=_lowerCamelCase						,   device=self.device						,   dtype=_lowerCamelCase)

         lowercase__    :   Optional[Any]      =       image.to(device=self.device						,   dtype=_lowerCamelCase)

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

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

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

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

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

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase)





def 				__lowerCamelCase						( UpperCAmelCase_						:		int = 100_0000						):




      """simple docstring"""





      a						:Any 							=       set(range(3 ,  UpperCAmelCase_ ,  2						)						)
      primes.add(2						)
      for p in range(3 ,  UpperCAmelCase_ ,  2						):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p ,  UpperCAmelCase_ ,  UpperCAmelCase_						)						)						)

      a						:Union[str, Any] 							=       [float(UpperCAmelCase_						) for n in range(limit + 1						)]

      for p in primes:
            for n in range(UpperCAmelCase_ ,  limit + 1 ,  UpperCAmelCase_						):
                  phi[n] *= 1 - 1 / p

      return int(sum(phi[2:]						)						)


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





