





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a   	=    False







class __a			( unittest.TestCase      ):
   pass







@nightly
@require_torch_gpu
class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
      # remove text_unet
      pipe.remove_unused_weights()
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							"""A painting of a squirrel eating a burger """
      __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							pipe(
          prompt=lowerCamelCase					,generator=lowerCamelCase					,guidance_scale=7.5					,num_inference_steps=2					,output_type="""numpy"""     ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							generator.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							pipe(
          prompt=lowerCamelCase					,generator=lowerCamelCase					,guidance_scale=7.5					,num_inference_steps=2					,output_type="""numpy"""     ).images

      assert np.abs(image - new_image     ).sum() < 1E-5, "Models don't have the same forward pass"





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							VersatileDiffusionTextToImagePipeline.from_pretrained(
          """shi-labs/versatile-diffusion"""					,torch_dtype=torch.floataa     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							"""A painting of a squirrel eating a burger """
      __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							pipe(
          prompt=lowerCamelCase					,generator=lowerCamelCase					,guidance_scale=7.5					,num_inference_steps=50					,output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE   				=							image[0, 253:256, 253:256, -1]

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2













__magic_name__   :  Optional[Any]        =							0  # The first color of the flag.
__magic_name__   :  List[str]        =							1  # The second color of the flag.
__magic_name__   :  Dict        =							2  # The third color of the flag.
__magic_name__   :  Union[str, Any]        =							(red, white, blue)
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if not sequence:
            return []
      if len(_UpperCamelCase) == 1:
            return list(_UpperCamelCase)
      UpperCamelCase					       =  0
      UpperCamelCase					       =  len(_UpperCamelCase) - 1
      UpperCamelCase					       =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  UpperCamelCase					       =  F'The elements inside the sequence must contains only {colors} values'
                  raise ValueError(_UpperCamelCase)
      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __magic_name__   :  str        =							input('''Enter numbers separated by commas:\n''').strip()
       __magic_name__   :  Union[str, Any]        =							[int(item.strip()) for item in user_input.split(''',''')]
       print(F'{dutch_national_flag_sort(unsorted)}')



