




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase    :		Union[str, Any]			   =       False







class   A(						unittest.TestCase       ):



	'''simple docstring'''


	pass







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



	'''simple docstring'''


	def     a__			(    self :  Union[str, Any]  )       ->     Optional[Any]:

							"""simple docstring"""






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

	def     a__			(    self :  int  )       ->     Dict:

							"""simple docstring"""






							lowerCamelCase_							=   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						,							torch_dtype=torch.floataa  )
							pipe.to(_lowerCamelCase  )
							pipe.set_progress_bar_config(disable=_lowerCamelCase  )

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

							lowerCamelCase_							=   torch.manual_seed(0  )
							lowerCamelCase_							=   pipe.dual_guided(
							    prompt='first prompt'						,							image=_lowerCamelCase						,							text_to_image_strength=0.75						,							generator=_lowerCamelCase						,							guidance_scale=7.5						,							num_inference_steps=2						,							output_type='numpy'						,							).images

							with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(_lowerCamelCase  )
													lowerCamelCase_							=   VersatileDiffusionPipeline.from_pretrained(_lowerCamelCase						,							torch_dtype=torch.floataa  )
							pipe.to(_lowerCamelCase  )
							pipe.set_progress_bar_config(disable=_lowerCamelCase  )

							lowerCamelCase_							=   generator.manual_seed(0  )
							lowerCamelCase_							=   pipe.dual_guided(
							    prompt='first prompt'						,							image=_lowerCamelCase						,							text_to_image_strength=0.75						,							generator=_lowerCamelCase						,							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 :  str  )       ->     List[str]:

							"""simple docstring"""






							lowerCamelCase_							=   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						,							torch_dtype=torch.floataa  )
							pipe.to(_lowerCamelCase  )
							pipe.set_progress_bar_config(disable=_lowerCamelCase  )

							lowerCamelCase_							=   'cyberpunk 2077'
							lowerCamelCase_							=   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'  )
							lowerCamelCase_							=   torch.manual_seed(0  )
							lowerCamelCase_							=   pipe.dual_guided(
							    prompt=_lowerCamelCase						,							image=_lowerCamelCase						,							text_to_image_strength=0.75						,							generator=_lowerCamelCase						,							guidance_scale=7.5						,							num_inference_steps=50						,							output_type='numpy'						,							).images

							lowerCamelCase_							=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase_							=   np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]  )

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

							lowerCamelCase_							=   'A painting of a squirrel eating a burger '
							lowerCamelCase_							=   torch.manual_seed(0  )
							lowerCamelCase_							=   pipe.text_to_image(
							    prompt=_lowerCamelCase						,							generator=_lowerCamelCase						,							guidance_scale=7.5						,							num_inference_steps=50						,							output_type='numpy'  ).images

							lowerCamelCase_							=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase_							=   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

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

							lowerCamelCase_							=   pipe.image_variation(_lowerCamelCase						,							generator=_lowerCamelCase						,							output_type='numpy'  ).images

							lowerCamelCase_							=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase_							=   np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]  )

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









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''keras_nlp''']

 def __init__(    self :  Optional[int]						,							*A_ :  Any						,							**A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       requires_backends(self						,							['keras_nlp']  )



