


"""simple docstring"""



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 _lowerCAmelCase				(      a__	):







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		List[Any]:

       '''simple docstring'''




       super().__init__()

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

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







   @torch.no_grad()
   def __call__(	self						,	UpperCamelCase__ = 1						,	UpperCamelCase__ = None						,	UpperCamelCase__ = 0.0						,	UpperCamelCase__ = 50						,	UpperCamelCase__ = None						,	UpperCamelCase__ = "pil"						,	UpperCamelCase__ = True						,	)	->		str:

       '''simple docstring'''




       if isinstance(self.unet.config.sample_size						,	UpperCamelCase_						):
           snake_case    :      Optional[int]      					=    (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
       else:
           snake_case    :      List[str]      					=    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

       snake_case    :      Any      					=    randn_tensor(UpperCamelCase_						,	generator=UpperCamelCase_						,	device=self.device						,	dtype=self.unet.dtype						)

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

       for t in self.progress_bar(self.scheduler.timesteps						):
           # 1. predict noise model_output
           snake_case    :      Optional[int]      					=    self.unet(UpperCamelCase_						,	UpperCamelCase_						).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
           snake_case    :      Optional[int]      					=    self.scheduler.step(
               UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	eta=UpperCamelCase_						,	use_clipped_model_output=UpperCamelCase_						,	generator=UpperCamelCase_						).prev_sample

       snake_case    :      Union[str, Any]      					=    (image / 2 + 0.5).clamp(0						,	1						)
       snake_case    :      int      					=    image.cpu().permute(0						,	2						,	3						,	1						).numpy()
       if output_type == "pil":
           snake_case    :      List[str]      					=    self.numpy_to_pil(UpperCamelCase_						)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase_						)









"""simple docstring"""





def    _snake_case  (  _snake_case :	list	):

      def merge(_snake_case :	list       ,      _snake_case :	list	) -> list:

            def _merge():
                  while left and right:
                        yield (left if left[0] <= right[0] else right).pop(0	)
                  yield from left
                  yield from right

            return list(_merge()	)

      if len(_snake_case	) <= 1:
            return collection
      lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	) // 2
      return merge(merge_sort(collection[:mid]	)       ,      merge_sort(collection[mid:]	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 snake_case__						:   Optional[Any]			 = input('''Enter numbers separated by a comma:\n''').strip()
 snake_case__						:   Union[str, Any]			 = [int(item) for item in user_input.split(''',''')]
 print(*merge_sort(unsorted), sep=''',''')


