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


A_	     =   False






class   __lowercase       (     unittest.TestCase     ):
						pass






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

						def 							__a		(						self					:   List[Any] )				->		List[str]:


									'''simple docstring'''

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

						def 							__a		(						self					:   int )				->		List[str]:


									'''simple docstring'''

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

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

									lowercase       		=		torch.manual_seed(0 )
									lowercase       		=		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 )
												lowercase       		=		VersatileDiffusionPipeline.from_pretrained(__lowerCamelCase				,				torch_dtype=torch.floataa )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									lowercase       		=		generator.manual_seed(0 )
									lowercase       		=		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					:   Optional[int] )				->		Optional[Any]:


									'''simple docstring'''

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

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

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

									assert image.shape == (1, 5_12, 5_12, 3)
									lowercase       		=		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

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

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

									assert image.shape == (1, 5_12, 5_12, 3)
									lowercase       		=		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

									lowercase       		=		pipe.image_variation(__lowerCamelCase				,				generator=__lowerCamelCase				,				output_type='''numpy''' ).images

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

									assert image.shape == (1, 5_12, 5_12, 3)
									lowercase       		=		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









'''simple docstring'''





from ..utils import DummyObject, requires_backends




class 				UpperCAmelCase			(			metaclass=_snake_case						):
 UpperCAmelCase				=					["note_seq"]


 def __init__(      self							:	List[str]			,       *__lowerCamelCase							:	int			,       **__lowerCamelCase							:	Optional[int] ):
    requires_backends(self			,       ['''note_seq'''] )


 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	Optional[Any]			,       *__lowerCamelCase							:	Dict			,       **__lowerCamelCase							:	Optional[Any] ):
    requires_backends(cls			,       ['''note_seq'''] )





 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	str			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Optional[Any] ):
    requires_backends(cls			,       ['''note_seq'''] )



