


'''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



'''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)