


'''simple docstring'''






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Any					=   (UnCLIPScheduler,)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					**lowerCamelCase_     ) ->							Optional[Any]:
														lowerCAmelCase__      =			{
														    '''num_train_timesteps''': 10_00,
														    '''variance_type''': '''fixed_small_log''',
														    '''clip_sample''': True,
														    '''clip_sample_range''': 1.0,
														    '''prediction_type''': '''epsilon''',
														}

														config.update(**lowerCamelCase_     )
														return config







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														for variance in ["fixed_small_log", "learned_range"]:
																					self.check_over_configs(variance_type=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														for clip_sample_range in [1, 5, 10, 20]:
																					self.check_over_configs(clip_sample_range=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(prediction_type=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														for time_step in [0, 5_00, 9_99]:
																					for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																												if prev_timestep is not None and prev_timestep >= time_step:
																																			continue

																												self.check_over_forward(time_step=lowerCamelCase_      ,					prev_timestep=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.00_00e-10     )     ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_549_625     )     ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_994_987     )     ) < 1e-5







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config(variance_type='''learned_range'''     )
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														lowerCAmelCase__      =			0.5

														assert scheduler._get_variance(1      ,					predicted_variance=lowerCamelCase_     ) - -10.1_712_790 < 1e-5
														assert scheduler._get_variance(4_87      ,					predicted_variance=lowerCamelCase_     ) - -5.7_998_052 < 1e-5
														assert scheduler._get_variance(9_99      ,					predicted_variance=lowerCamelCase_     ) - -0.0_010_011 < 1e-5







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config()
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														lowerCAmelCase__      =			scheduler.timesteps

														lowerCAmelCase__      =			self.dummy_model()
														lowerCAmelCase__      =			self.dummy_sample_deter
														lowerCAmelCase__      =			torch.manual_seed(0     )

														for i, t in enumerate(lowerCamelCase_     ):
																					# 1. predict noise residual
																					lowerCAmelCase__      =			model(lowerCamelCase_      ,					lowerCamelCase_     )

																					# 2. predict previous mean of sample x_t-1
																					lowerCAmelCase__      =			scheduler.step(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					generator=lowerCamelCase_     ).prev_sample

																					lowerCAmelCase__      =			pred_prev_sample

														lowerCAmelCase__      =			torch.sum(torch.abs(lowerCamelCase_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(lowerCamelCase_     )     )

														assert abs(result_sum.item() - 252.2_682_495     ) < 1e-2
														assert abs(result_mean.item() - 0.3_284_743     ) < 1e-3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config()
														lowerCAmelCase__      =			scheduler_class(**lowerCamelCase_     )

														scheduler.set_timesteps(25     )

														lowerCAmelCase__      =			scheduler.timesteps

														lowerCAmelCase__      =			self.dummy_model()
														lowerCAmelCase__      =			self.dummy_sample_deter
														lowerCAmelCase__      =			torch.manual_seed(0     )

														for i, t in enumerate(lowerCamelCase_     ):
																					# 1. predict noise residual
																					lowerCAmelCase__      =			model(lowerCamelCase_      ,					lowerCamelCase_     )

																					if i + 1 == timesteps.shape[0]:
																												lowerCAmelCase__      =			None
																					else:
																												lowerCAmelCase__      =			timesteps[i + 1]

																					# 2. predict previous mean of sample x_t-1
																					lowerCAmelCase__      =			scheduler.step(
																					    lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					prev_timestep=lowerCamelCase_      ,					generator=lowerCamelCase_     ).prev_sample

																					lowerCAmelCase__      =			pred_prev_sample

														lowerCAmelCase__      =			torch.sum(torch.abs(lowerCamelCase_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(lowerCamelCase_     )     )

														assert abs(result_sum.item() - 258.2_044_983     ) < 1e-2
														assert abs(result_mean.item() - 0.3_362_038     ) < 1e-3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														pass







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														pass



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




@slow
@require_torch_gpu
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														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(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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