


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class   __lowercase				(  __snake_case  ):





      def __init__(		self						:					Optional[Any]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					Dict       )    ->   Dict:






         """simple docstring"""







         super().__init__()

         # make sure scheduler can always be converted to DDIM
         UpperCAmelCase							=     DDIMScheduler.from_config(scheduler.config       )

         self.register_modules(unet=__lowerCamelCase					,       scheduler=__lowerCamelCase       )





      @torch.no_grad()
      def __call__(		self						:					str					,       __lowerCamelCase						:					int = 1					,       __lowerCamelCase						:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,       __lowerCamelCase						:					float = 0.0					,       __lowerCamelCase						:					int = 5_0					,       __lowerCamelCase						:					Optional[bool] = None					,       __lowerCamelCase						:					Optional[str] = "pil"					,       __lowerCamelCase						:					bool = True					,       )    ->   Union[ImagePipelineOutput, Tuple]:






         """simple docstring"""







         if isinstance(self.unet.config.sample_size					,       __lowerCamelCase       ):
            UpperCAmelCase							=     (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size,
                self.unet.config.sample_size,
            )
         else:
            UpperCAmelCase							=     (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if isinstance(__lowerCamelCase					,       __lowerCamelCase       ) and len(__lowerCamelCase       ) != batch_size:
            raise ValueError(
                F"""You have passed a list of generators of length {len(__lowerCamelCase       )}, but requested an effective batch"""
                F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

         UpperCAmelCase							=     randn_tensor(__lowerCamelCase					,       generator=__lowerCamelCase					,       device=self.device					,       dtype=self.unet.dtype       )

         # set step values
         self.scheduler.set_timesteps(__lowerCamelCase       )

         for t in self.progress_bar(self.scheduler.timesteps       ):
            # 1. predict noise model_output
            UpperCAmelCase							=     self.unet(__lowerCamelCase					,       __lowerCamelCase       ).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            UpperCAmelCase							=     self.scheduler.step(
                __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       eta=__lowerCamelCase					,       use_clipped_model_output=__lowerCamelCase					,       generator=__lowerCamelCase       ).prev_sample

         UpperCAmelCase							=     (image / 2 + 0.5).clamp(0					,       1       )
         UpperCAmelCase							=     image.cpu().permute(0					,       2					,       3					,       1       ).numpy()
         if output_type == "pil":
            UpperCAmelCase							=     self.numpy_to_pil(__lowerCamelCase       )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=__lowerCamelCase       )







from math import isqrt
def   _UpperCamelCase				(						lowerCAmelCase_   )						->bool:
   return all(number % divisor != 0 for divisor in range(2  ,						isqrt(lowerCAmelCase_   ) + 1   )   )
def   _UpperCamelCase				(						lowerCAmelCase_ = 1_0**6   )						->int:
   UpperCAmelCase							=     0
   UpperCAmelCase							=     1
   UpperCAmelCase							=     7
   while prime_candidate < max_prime:
      primes_count += is_prime(lowerCAmelCase_   )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


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



