



'''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


_lowerCAmelCase										=     False






class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  pass







@nightly
@require_torch_gpu
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		int:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Dict											= VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""				)
      # remove text_unet
      pipe.remove_unused_weights()
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[int]											= """A painting of a squirrel eating a burger """
      lowerCAmelCase__			:  Optional[Any]											= torch.manual_seed(0				)
      lowerCAmelCase__			:  List[str]											= pipe(
          prompt=__UpperCAmelCase  ,generator=__UpperCAmelCase  ,guidance_scale=7.5  ,num_inference_steps=2  ,output_type="""numpy"""				).images

      with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(__UpperCAmelCase				)
          lowerCAmelCase__			:  Dict											= VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCAmelCase				)
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= generator.manual_seed(0				)
      lowerCAmelCase__			:  str											= pipe(
          prompt=__UpperCAmelCase  ,generator=__UpperCAmelCase  ,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				)   ->		Dict:
      lowerCAmelCase__			:  Optional[int]											= VersatileDiffusionTextToImagePipeline.from_pretrained(
          """shi-labs/versatile-diffusion"""  ,torch_dtype=torch.floataa				)
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= """A painting of a squirrel eating a burger """
      lowerCAmelCase__			:  Union[str, Any]											= torch.manual_seed(0				)
      lowerCAmelCase__			:  Union[str, Any]											= pipe(
          prompt=__UpperCAmelCase  ,generator=__UpperCAmelCase  ,guidance_scale=7.5  ,num_inference_steps=50  ,output_type="""numpy"""				).images

      lowerCAmelCase__			:  Dict											= image[0, 253:256, 253:256, -1]

      assert image.shape == (1, 512, 512, 3)
      lowerCAmelCase__			:  Optional[Any]											= np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]				)

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







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    if any(not isinstance(UpperCamelCase							,   UpperCamelCase      ) or x < 0 for x in sequence      ):
        raise TypeError("""Sequence must be list of non-negative integers"""      )
    for _ in range(len(UpperCamelCase      )      ):
        for i, (rod_upper, rod_lower) in enumerate(zip(UpperCamelCase							,   sequence[1:]      )      ):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
 assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
 assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]


