




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


UpperCamelCase__						=		False


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

					def       lowercase_				(							self   ):







						'''simple docstring'''




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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )

						lowerCamelCase_				:						int				  =				torch.manual_seed(0   )
						lowerCamelCase_				:						List[Any]				  =				pipe(
						    prompt='first prompt'					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			guidance_scale=7.5					,			num_inference_steps=2					,			output_type='numpy'					,			).images

						with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(__lowercase   )
							lowerCamelCase_				:						Dict				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained(__lowercase   )

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

						lowerCamelCase_				:						Optional[Any]				  =				generator.manual_seed(0   )
						lowerCamelCase_				:						List[Any]				  =				pipe(
						    prompt='first prompt'					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			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       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
						pipe.remove_unused_weights()
						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Union[str, Any]				  =				'cyberpunk 2077'
						lowerCamelCase_				:						Optional[Any]				  =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
						lowerCamelCase_				:						str				  =				torch.manual_seed(0   )
						lowerCamelCase_				:						Dict				  =				pipe(
						    prompt=__lowercase					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			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_				:						List[str]				  =				np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]   )

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








"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator
from typing import Any







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Optional[int]					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	data
												a__ 							=	None





							def __str__( self:  str     ):

												'''simple docstring'''

												return F'{self.data}'







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  int     ):

												'''simple docstring'''

												a__ 							=	None
												a__ 							=	None




							def __iter__( self:  Tuple     ):

												'''simple docstring'''

												a__ 							=	self.front
												while node:
																	yield node.data
																	a__ 							=	node.next




							def __len__( self:  Union[str, Any]     ):

												'''simple docstring'''

												return len(tuple(iter(self     )     )     )




							def __str__( self:  Any     ):

												'''simple docstring'''

												return " <- ".join(str(__A     ) for item in self     )




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												return len(self     ) == 0




							def 			lowercase		( self:  Tuple					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	Node(__A     )
												if self.is_empty():
																	a__ 							=	a__ 							=	node
												else:
																	assert isinstance(self.rear					,		__A     )
																	a__ 							=	node
																	a__ 							=	node




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												if self.is_empty():
																	raise IndexError('''dequeue from empty queue'''     )
												assert isinstance(self.front					,		__A     )
												a__ 							=	self.front
												a__ 							=	self.front.next
												if self.front is None:
																	a__ 							=	None
												return node.data





							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												a__ 							=	a__ 							=	None


if __name__ == "__main__":
	from doctest import testmod

	testmod()


