



'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class __A    (						A		):



	'''simple docstring'''







	__lowerCamelCase					: UNetaDModel
	__lowerCamelCase					: ScoreSdeVeScheduler



	def __init__(self				,     A				,     A )					->  Dict:


						"""simple docstring"""



						super().__init__()
						self.register_modules(unet=A				,     scheduler=A )



	@torch.no_grad()
	def __call__(self				,     A = 1				,     A = 2_000				,     A = None				,     A = "pil"				,     A = True				,     **A				,     )					->  Union[ImagePipelineOutput, Tuple]:


						"""simple docstring"""



						_a											=						self.unet.config.sample_size
						_a											=						(batch_size, 3, img_size, img_size)

						_a											=						self.unet

						_a											=						randn_tensor(A				,     generator=A ) * self.scheduler.init_noise_sigma
						_a											=						sample.to(self.device )

						self.scheduler.set_timesteps(A )
						self.scheduler.set_sigmas(A )

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
											_a											=						self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device )

											# correction step
											for _ in range(self.scheduler.config.correct_steps ):
																_a											=						self.unet(A				,     A ).sample
																_a											=						self.scheduler.step_correct(A				,     A				,     generator=A ).prev_sample

											# prediction step
											_a											=						model(A				,     A ).sample
											_a											=						self.scheduler.step_pred(A				,     A				,     A				,     generator=A )

											_a						,					_a											=						output.prev_sample, output.prev_sample_mean

						_a											=						sample_mean.clamp(0				,     1 )
						_a											=						sample.cpu().permute(0				,     2				,     3				,     1 ).numpy()
						if output_type == "pil":
											_a											=						self.numpy_to_pil(A )

						if not return_dict:
											return (sample,)

						return ImagePipelineOutput(images=A )








'''simple docstring'''





from __future__ import annotations

lowercase_	 =						[True] * 1_000_001
lowercase_	 =						2
while i * i <= 1_000_000:
  if seive[i]:
    for j in range(i * i, 1_000_001, i):
      lowercase_	 =						False
  i += 1




def lowerCAmelCase						(__A):



     """simple docstring"""



     return seive[n]




def lowerCAmelCase						(__A):



     """simple docstring"""



     return any(digit in '''02468''' for digit in str(__A))




def lowerCAmelCase						(__A = 1_000_000):



     """simple docstring"""



     _a											=						[2]  # result already includes the number 2.
     for num in range(3						,			limit + 1						,			2):
          if is_prime(__A) and not contains_an_even_digit(__A):
               _a											=						str(__A)
               _a											=						[int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
               if all(is_prime(__A) for i in list_nums):
                    result.append(__A)
     return result




def lowerCAmelCase						():



     """simple docstring"""



     return len(find_circular_primes())


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



