





'''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


snake_case_   :  Any								=      False

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

			'''simple docstring'''





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




			def       a	(					self   ):
								_UpperCamelCase											=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(_A   )
								pipe.set_progress_bar_config(disable=_A   )

								_UpperCamelCase											=   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

								_UpperCamelCase											=   torch.manual_seed(0   )
								_UpperCamelCase											=   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   )
													_UpperCamelCase											=   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A   )

								pipe.to(_A   )
								pipe.set_progress_bar_config(disable=_A   )

								_UpperCamelCase											=   generator.manual_seed(0   )
								_UpperCamelCase											=   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       a	(					self   ):
								_UpperCamelCase											=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
								pipe.remove_unused_weights()
								pipe.to(_A   )
								pipe.set_progress_bar_config(disable=_A   )

								_UpperCamelCase											=   'cyberpunk 2077'
								_UpperCamelCase											=   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
								_UpperCamelCase											=   torch.manual_seed(0   )
								_UpperCamelCase											=   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

								_UpperCamelCase											=   image[0, 2_53:2_56, 2_53:2_56, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								_UpperCamelCase											=   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  lowercase__(		_UpperCamelCase  : list[int]				)->   list[int]:






					"""simple docstring"""

					if not list_of_ints:
										return []

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

					_UpperCamelCase											=   max(len(bin(_UpperCamelCase				)[2:]				) for x in list_of_ints				)
					return _msd_radix_sort(_UpperCamelCase ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int				)->   list[int]:






					"""simple docstring"""

					if bit_position == 0 or len(_UpperCamelCase				) in [0, 1]:
										return list_of_ints

					_UpperCamelCase											=   []
					_UpperCamelCase											=   []
					# Split numbers based on bit at bit_position from the right
					for number in list_of_ints:
										if (number >> (bit_position - 1)) & 1:
															# number has a one at bit bit_position
															ones.append(_UpperCamelCase				)
										else:
															# number has a zero at bit bit_position
															zeros.append(_UpperCamelCase				)

    # recursively split both lists further
					_UpperCamelCase											=   _msd_radix_sort(_UpperCamelCase ,   bit_position - 1				)
					_UpperCamelCase											=   _msd_radix_sort(_UpperCamelCase ,   bit_position - 1				)

					# recombine lists
					_UpperCamelCase											=   zeros
					res.extend(_UpperCamelCase				)

					return res



def  lowercase__(		_UpperCamelCase  : list[int]				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   len(_UpperCamelCase				)
					if not list_of_ints or length == 1:
										return

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

					_UpperCamelCase											=   max(len(bin(_UpperCamelCase				)[2:]				) for x in list_of_ints				)
					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   0 ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int				)->   Tuple:






					"""simple docstring"""

					if bit_position == 0 or end_index - begin_index <= 1:
										return

					bit_position -= 1

					_UpperCamelCase											=   begin_index
					_UpperCamelCase											=   end_index - 1
					while i <= j:
										_UpperCamelCase											=   False
										if not (list_of_ints[i] >> bit_position) & 1:
															# found zero at the beginning
															i += 1
															_UpperCamelCase											=   True
										if (list_of_ints[j] >> bit_position) & 1:
															# found one at the end
															j -= 1
															_UpperCamelCase											=   True

										if changed:
															continue

										_UpperCamelCase				,	_UpperCamelCase											=   list_of_ints[j], list_of_ints[i]
										j -= 1
										if j != i:
															i += 1

					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



