





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 		UpperCamelCase__			(				__SCREAMING_SNAKE_CASE       ):



     '''simple docstring'''

     def __init__(							self						:   List[str]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Dict	)					->		Any:




           '''simple docstring'''


           super().__init__()

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

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






     @torch.no_grad()
     def __call__(							self						:   Tuple						,lowerCamelCase__						:   List[Any] = 1						,lowerCamelCase__						:   List[str] = None						,lowerCamelCase__						:   Any = 0.0						,lowerCamelCase__						:   List[Any] = 50						,lowerCamelCase__						:   Any = None						,lowerCamelCase__						:   Union[str, Any] = "pil"						,lowerCamelCase__						:   List[Any] = True						,)					->		Any:




           '''simple docstring'''


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

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

           SCREAMING_SNAKE_CASE		=						randn_tensor(_snake_case						,generator=_snake_case						,device=self.device						,dtype=self.unet.dtype	)

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

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 SCREAMING_SNAKE_CASE		=						self.unet(_snake_case						,_snake_case	).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
                 SCREAMING_SNAKE_CASE		=						self.scheduler.step(
                     _snake_case						,_snake_case						,_snake_case						,eta=_snake_case						,use_clipped_model_output=_snake_case						,generator=_snake_case	).prev_sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=_snake_case	)









from itertools import permutations



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       bool:


      '''simple docstring'''


      if num[3] % 2 != 0:
            return False

      if (num[2] + num[3] + num[4]) % 3 != 0:
            return False

      if num[5] % 5 != 0:
            return False

      SCREAMING_SNAKE_CASE		=						[7, 11, 13, 17]
      for i, test in enumerate(_SCREAMING_SNAKE_CASE     ):
            if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                  return False
      return True



def        __lowercase       (				_SCREAMING_SNAKE_CASE = 10     )			->       int:


      '''simple docstring'''


      return sum(
          int("""""".join(map(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )     )     )
          for num in permutations(range(_SCREAMING_SNAKE_CASE     )     )
          if is_substring_divisible(_SCREAMING_SNAKE_CASE     )     )


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


