



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__	:     List[Any]    =			False






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


			'''simple docstring'''

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






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

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		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						)

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

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.manual_seed(0						)
					__SCREAMING_SNAKE_CASE	:		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

					with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(_A						)
							__SCREAMING_SNAKE_CASE	:		Dict   	=		VersatileDiffusionDualGuidedPipeline.from_pretrained(_A						)

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

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		generator.manual_seed(0						)
					__SCREAMING_SNAKE_CASE	:		str   	=		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 UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						)
					pipe.remove_unused_weights()
					pipe.to(_A						)
					pipe.set_progress_bar_config(disable=_A						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		"cyberpunk 2077"
					__SCREAMING_SNAKE_CASE	:		Dict   	=		load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""						)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		torch.manual_seed(0						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		pipe(
					    prompt=_A							,					image=_A							,					text_to_image_strength=0.75							,					generator=_A							,					guidance_scale=7.5							,					num_inference_steps=5_0							,					output_type="""numpy"""							,					).images

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					assert image.shape == (1, 5_1_2, 5_1_2, 3)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		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'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass