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

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput





@dataclass
class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							42





class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict:
         _lowerCAmelCase						=		CustomOutput(images=np.random.rand(1  ,      3  ,      4  ,      4       )       )

         # check every way of getting the attribute
         assert isinstance(outputs.images  ,      np.ndarray       )
         assert outputs.images.shape == (1, 3, 4, 4)
         assert isinstance(outputs['images']  ,      np.ndarray       )
         assert outputs["images"].shape == (1, 3, 4, 4)
         assert isinstance(outputs[0]  ,      np.ndarray       )
         assert outputs[0].shape == (1, 3, 4, 4)

         # test with a non-tensor attribute
         _lowerCAmelCase						=		CustomOutput(images=[PIL.Image.new('RGB'  ,      (4, 4)       )]       )

         # check every way of getting the attribute
         assert isinstance(outputs.images  ,      _lowerCAmelCase       )
         assert isinstance(outputs.images[0]  ,      PIL.Image.Image       )
         assert isinstance(outputs['images']  ,      _lowerCAmelCase       )
         assert isinstance(outputs['images'][0]  ,      PIL.Image.Image       )
         assert isinstance(outputs[0]  ,      _lowerCAmelCase       )
         assert isinstance(outputs[0][0]  ,      PIL.Image.Image       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         # test output reinitialization with a `dict` for compatibility with `accelerate`
         _lowerCAmelCase						=		CustomOutput({'images': np.random.rand(1  ,      3  ,      4  ,      4       )}       )

         # check every way of getting the attribute
         assert isinstance(outputs.images  ,      np.ndarray       )
         assert outputs.images.shape == (1, 3, 4, 4)
         assert isinstance(outputs['images']  ,      np.ndarray       )
         assert outputs["images"].shape == (1, 3, 4, 4)
         assert isinstance(outputs[0]  ,      np.ndarray       )
         assert outputs[0].shape == (1, 3, 4, 4)

         # test with a non-tensor attribute
         _lowerCAmelCase						=		CustomOutput({'images': [PIL.Image.new('RGB'  ,      (4, 4)       )]}       )

         # check every way of getting the attribute
         assert isinstance(outputs.images  ,      _lowerCAmelCase       )
         assert isinstance(outputs.images[0]  ,      PIL.Image.Image       )
         assert isinstance(outputs['images']  ,      _lowerCAmelCase       )
         assert isinstance(outputs['images'][0]  ,      PIL.Image.Image       )
         assert isinstance(outputs[0]  ,      _lowerCAmelCase       )
         assert isinstance(outputs[0][0]  ,      PIL.Image.Image       )


import random



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  def is_sorted(__SCREAMING_SNAKE_CASE					:  str    ):
    for i in range(len(__SCREAMING_SNAKE_CASE    ) - 1    ):
      if collection[i] > collection[i + 1]:
        return False
    return True

  while not is_sorted(__SCREAMING_SNAKE_CASE    ):
    random.shuffle(__SCREAMING_SNAKE_CASE    )
  return collection


if __name__ == "__main__":
       _UpperCamelCase:					Dict     =input('Enter numbers separated by a comma:\n').strip()
       _UpperCamelCase:					Optional[Any]     =[int(item) for item in user_input.split(',')]
       print(bogo_sort(unsorted))
