"""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 					__a   (		A      ):






     '''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(A      ).astype(np.floataa      ) / 255.0
     lowercase__				 =							image[None].transpose(0    ,				3    ,				1    ,				2      )
     lowercase__				 =							torch.from_numpy(A      )
     return 2.0 * image - 1.0






class    a__    (   _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  ):
             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  )





"""simple docstring"""


from __future__ import annotations

import math







def 					__a   (		A      ):






     '''simple docstring'''



     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5    ,				int(math.sqrt(A      ) + 1      )    ,				6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True


lowerCAmelCase_:					Optional[Any]     =					[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          raise ValueError("n must be an integer"      )
     if n <= 0:
          raise ValueError("n must be >= 0"      )

     lowercase__				 =							[]
     for num in range(len(A      )      ):
          lowercase__				 =							0
          while 2 * i * i <= odd_composites[num]:
               lowercase__				 =							odd_composites[num] - 2 * i * i
               if is_prime(A      ):
                    break
               i += 1
          else:
               list_nums.append(odd_composites[num]      )
               if len(A      ) == n:
                    return list_nums

     return []







def 					__a   (		):






     '''simple docstring'''



     return compute_nums(1      )[0]


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




