from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  __lowerCAmelCase	(							_a    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[List[PIL.Image.Image], np.ndarray]
 _lowerCamelCase    :						Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline







def   a						(				SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		len(SCREAMING_SNAKE_CASE_							)
       for i in range(length - 1							):
              UpperCamelCase     :						Optional[int] 						=		False
              for j in range(length - 1 - i							):
                     if collection[j] > collection[j + 1]:
                            UpperCamelCase     :						Optional[int] 						=		True
                            UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
  import doctest
  import time

  doctest.testmod()

  __UpperCAmelCase			:			Any			      =					input("Enter numbers separated by a comma:").strip()
  __UpperCAmelCase			:			List[str]			      =					[int(item) for item in user_input.split(",")]
  __UpperCAmelCase			:			List[Any]			      =					time.process_time()
  print(*bubble_sort(unsorted), sep=",")
  print(f'''Processing time: {(time.process_time() - start)%1E9 + 7}''')

