code: 
  
  
  
  
  """simple docstring"""
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  class 			lowerCamelCase__    (  unittest.TestCase			):
  			# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
  			a__ :      Union[str, Any]								=     (ScoreSdeVeScheduler,)
  			a__ :      Dict								=     ()
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Any								=				4
  					snake_case		:						Any								=				3
  					snake_case		:						Optional[int]								=				8
  					snake_case		:						Optional[Any]								=				8
  
  					snake_case		:						Optional[Any]								=				torch.rand((batch_size, num_channels, height, width)							)
  
  					return sample
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				4
  					snake_case		:						Dict								=				3
  					snake_case		:						str								=				8
  					snake_case		:						Any								=				8
  
  					snake_case		:						Tuple								=				batch_size * num_channels * height * width
  					snake_case		:						Union[str, Any]								=				torch.arange(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Dict								=				sample.reshape(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				sample / num_elems
  					snake_case		:						Optional[int]								=				sample.permute(3				, 0				, 1				, 2							)
  
  					return sample
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					def model(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, *SCREAMING_SNAKE_CASE							):
  							return sample * t / (t + 1)
  
  					return model
  
  
  			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				{
  					    "num_train_timesteps": 2_000,
  					    "snr": 0.15,
  					    "sigma_min": 0.01,
  					    "sigma_max": 1_348,
  					    "sampling_eps": 1E-5,
  					}
  
  					config.update(**SCREAMING_SNAKE_CASE							)
  					return config
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=0				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				dict(self.forward_default_kwargs							)
  
  					for scheduler_class in self.scheduler_classes:
  							snake_case		:						Any								=				self.dummy_sample
  							snake_case		:						Union[str, Any]								=				0.1 * sample
  
  							snake_case		:						Union[str, Any]								=				self.get_scheduler_config(**SCREAMING_SNAKE_CASE							)
  							snake_case		:						str								=				scheduler_class(**SCREAMING_SNAKE_CASE							)
  
  							with tempfile.TemporaryDirectory() as tmpdirname:
  									scheduler.save_config(SCREAMING_SNAKE_CASE							)
  									snake_case		:						int								=				scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						List[Any]								=				scheduler.step_pred(
  							    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  							snake_case		:						Dict								=				new_scheduler.step_pred(
  							    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  
  							assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
  
  							snake_case		:						List[Any]								=				scheduler.step_correct(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  							snake_case		:						Optional[Any]								=				new_scheduler.step_correct(
  							    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  
  							assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler correction are not identical"
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=0				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				dict(self.forward_default_kwargs							)
  					kwargs.update(SCREAMING_SNAKE_CASE							)
  
  					for scheduler_class in self.scheduler_classes:
  							snake_case		:						Tuple								=				self.dummy_sample
  							snake_case		:						Optional[int]								=				0.1 * sample
  
  							snake_case		:						Union[str, Any]								=				self.get_scheduler_config()
  							snake_case		:						Optional[int]								=				scheduler_class(**SCREAMING_SNAKE_CASE							)
  
  							with tempfile.TemporaryDirectory() as tmpdirname:
  									scheduler.save_config(SCREAMING_SNAKE_CASE							)
  									snake_case		:						Optional[int]								=				scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						Any								=				scheduler.step_pred(
  							    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  							snake_case		:						Optional[Any]								=				new_scheduler.step_pred(
  							    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  
  							assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
  
  							snake_case		:						Any								=				scheduler.step_correct(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  							snake_case		:						Optional[int]								=				new_scheduler.step_correct(
  							    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  
  							assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler correction are not identical"
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					for timesteps in [10, 100, 1_000]:
  							self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]				, [1, 100, 1_000]							):
  							self.check_over_configs(sigma_min=SCREAMING_SNAKE_CASE				, sigma_max=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					for t in [0.1, 0.5, 0.75]:
  							self.check_over_forward(time_step=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[str]								=				dict(self.forward_default_kwargs							)
  
  					snake_case		:						Optional[Any]								=				self.scheduler_classes[0]
  					snake_case		:						Any								=				self.get_scheduler_config()
  					snake_case		:						Any								=				scheduler_class(**SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[str]								=				3
  
  					snake_case		:						List[str]								=				self.dummy_model()
  					snake_case		:						Tuple								=				self.dummy_sample_deter
  
  					scheduler.set_sigmas(SCREAMING_SNAKE_CASE							)
  					scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Union[str, Any]								=				torch.manual_seed(0							)
  
  					for i, t in enumerate(scheduler.timesteps							):
  							snake_case		:						Optional[int]								=				scheduler.sigmas[i]
  
  							for _ in range(scheduler.config.correct_steps							):
  									with torch.no_grad():
  											snake_case		:						Dict								=				model(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  									snake_case		:						Optional[Any]								=				scheduler.step_correct(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							).prev_sample
  
  							with torch.no_grad():
  									snake_case		:						Tuple								=				model(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						int								=				scheduler.step_pred(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, generator=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  							snake_case			,				snake_case		:						List[str]								=				output.prev_sample, output.prev_sample_mean
  
  					snake_case		:						Any								=				torch.sum(torch.abs(SCREAMING_SNAKE_CASE							)							)
  					snake_case		:						List[str]								=				torch.mean(torch.abs(SCREAMING_SNAKE_CASE							)							)
  
  					assert np.isclose(result_sum.item()				, 1_43_72_75_85_28.0							)
  					assert np.isclose(result_mean.item()				, 18_71_45_30.0							)
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Union[str, Any]								=				dict(self.forward_default_kwargs							)
  
  					snake_case		:						List[Any]								=				kwargs.pop("num_inference_steps"				, SCREAMING_SNAKE_CASE							)
  
  					for scheduler_class in self.scheduler_classes:
  							snake_case		:						int								=				self.get_scheduler_config()
  							snake_case		:						Optional[int]								=				scheduler_class(**SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						Any								=				self.dummy_sample
  							snake_case		:						Any								=				0.1 * sample
  
  							if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE				, "set_timesteps"							):
  									scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
  							elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE				, "set_timesteps"							):
  									snake_case		:						Tuple								=				num_inference_steps
  
  							snake_case		:						List[Any]								=				scheduler.step_pred(SCREAMING_SNAKE_CASE				, 0				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  							snake_case		:						Dict								=				scheduler.step_pred(SCREAMING_SNAKE_CASE				, 1				, SCREAMING_SNAKE_CASE				, generator=torch.manual_seed(0							)				, **SCREAMING_SNAKE_CASE							).prev_sample
  
  							self.assertEqual(output_a.shape				, sample.shape							)
  							self.assertEqual(output_a.shape				, output_a.shape							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
  			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
  			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if text is None and images is None:
  							raise ValueError("You have to specify either text or images. Both cannot be none."							)
  
  					if text is not None:
  							snake_case		:						Dict								=				self.tokenizer(
  							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if images is not None:
  							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if text is not None and images is not None:
  							snake_case		:						Dict								=				image_features.pixel_values
  							return encoding
  					elif text is not None:
  							return encoding
  					else:
  							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
  					snake_case		:						Any								=				self.image_processor.model_input_names
  					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
  
  