code: 
  
  
  '''simple docstring'''
  
  import gc
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import VersatileDiffusionDualGuidedPipeline
  from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
  
  
  lowerCAmelCase   :Any								=    False
  @nightly
  @require_torch_gpu
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Optional[int]:
  			# clean up the VRAM after each test
  			super().tearDown()
  			gc.collect()
  			torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				Dict:
  			__magic_name__ :					Optional[int]  			=  VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
  			# remove text_unet
  			pipe.remove_unused_weights()
  			pipe.to(_A    )
  			pipe.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					str  			=  load_image(
  			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
  
  			__magic_name__ :					List[str]  			=  torch.manual_seed(0    )
  			__magic_name__ :					Union[str, Any]  			=  pipe(
  			    prompt='first prompt'						,  image=_A						,  text_to_image_strength=0.75						,  generator=_A						,  guidance_scale=7.5						,  num_inference_steps=2						,  output_type='numpy'						,  ).images
  
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				pipe.save_pretrained(_A    )
  				__magic_name__ :					List[str]  			=  VersatileDiffusionDualGuidedPipeline.from_pretrained(_A    )
  
  			pipe.to(_A    )
  			pipe.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					str  			=  generator.manual_seed(0    )
  			__magic_name__ :					int  			=  pipe(
  			    prompt='first prompt'						,  image=_A						,  text_to_image_strength=0.75						,  generator=_A						,  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      __lowerCAmelCase	(      self	:		str    )						->				Union[str, Any]:
  			__magic_name__ :					int  			=  VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
  			pipe.remove_unused_weights()
  			pipe.to(_A    )
  			pipe.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					Union[str, Any]  			=  'cyberpunk 2077'
  			__magic_name__ :					Optional[Any]  			=  load_image(
  			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
  			__magic_name__ :					Optional[Any]  			=  torch.manual_seed(0    )
  			__magic_name__ :					Optional[int]  			=  pipe(
  			    prompt=_A						,  image=_A						,  text_to_image_strength=0.75						,  generator=_A						,  guidance_scale=7.5						,  num_inference_steps=50						,  output_type='numpy'						,  ).images
  
  			__magic_name__ :					Tuple  			=  image[0, 253:256, 253:256, -1]
  
  			assert image.shape == (1, 512, 512, 3)
  			__magic_name__ :					Union[str, Any]  			=  np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]    )
  
  			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[int | float]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if len(lowerCAmelCase	) == 0:
  		raise ValueError('find_min() arg is an empty sequence'	)
  	if (
  	    left >= len(lowerCAmelCase	)
  	    or left < -len(lowerCAmelCase	)
  	    or right >= len(lowerCAmelCase	)
  	    or right < -len(lowerCAmelCase	)
  	):
  		raise IndexError('list index out of range'	)
  	if left == right:
  		return nums[left]
  	__magic_name__ :					Tuple  			=  (left + right) >> 1  # the middle
  	__magic_name__ :					str  			=  find_min(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)  # find min in range[left, mid]
  	__magic_name__ :					Optional[Any]  			=  find_min(lowerCAmelCase	,	mid + 1	,	lowerCAmelCase	)  # find min in range[mid + 1, right]
  
  	return left_min if left_min <= right_min else right_min
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod(verbose=True)