code: 
  
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  
  
  
  
  
  class 	lowercase		(				unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							(ScoreSdeVeScheduler,)
  	a__   :      Tuple	     =							()
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[int]:
  							UpperCAmelCase_=     4
  							UpperCAmelCase_=     3
  							UpperCAmelCase_=     8
  							UpperCAmelCase_=     8
  
  							UpperCAmelCase_=     torch.rand((batch_size, num_channels, height, width)  )
  
  							return sample
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				List[Any]:
  							UpperCAmelCase_=     4
  							UpperCAmelCase_=     3
  							UpperCAmelCase_=     8
  							UpperCAmelCase_=     8
  
  							UpperCAmelCase_=     batch_size * num_channels * height * width
  							UpperCAmelCase_=     torch.arange(__UpperCAmelCase  )
  							UpperCAmelCase_=     sample.reshape(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     sample / num_elems
  							UpperCAmelCase_=     sample.permute(3		,						0		,						1		,						2  )
  
  							return sample
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				List[str]:
  							def model(__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		List[Any]		,						*__UpperCAmelCase			:		List[Any]  ):
  													return sample * t / (t + 1)
  
  							return model
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						**__UpperCAmelCase			:		str  )    ->				Dict:
  							UpperCAmelCase_=     {
  							    """num_train_timesteps""": 2_000,
  							    """snr""": 0.15,
  							    """sigma_min""": 0.01,
  							    """sigma_max""": 1_348,
  							    """sampling_eps""": 1E-5,
  							}
  
  							config.update(**__UpperCAmelCase  )
  							return config
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Any=0		,						**__UpperCAmelCase			:		Optional[Any]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     dict(self.forward_default_kwargs  )
  
  							for scheduler_class in self.scheduler_classes:
  													UpperCAmelCase_=     self.dummy_sample
  													UpperCAmelCase_=     0.1 * sample
  
  													UpperCAmelCase_=     self.get_scheduler_config(**__UpperCAmelCase  )
  													UpperCAmelCase_=     scheduler_class(**__UpperCAmelCase  )
  
  													with tempfile.TemporaryDirectory() as tmpdirname:
  																			scheduler.save_config(__UpperCAmelCase  )
  																			UpperCAmelCase_=     scheduler_class.from_pretrained(__UpperCAmelCase  )
  
  													UpperCAmelCase_=     scheduler.step_pred(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  													UpperCAmelCase_=     new_scheduler.step_pred(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  
  													assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
  
  													UpperCAmelCase_=     scheduler.step_correct(__UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  													UpperCAmelCase_=     new_scheduler.step_correct(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  
  													assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler correction are not identical"
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Optional[int]=0		,						**__UpperCAmelCase			:		Optional[Any]  )    ->				List[Any]:
  							UpperCAmelCase_=     dict(self.forward_default_kwargs  )
  							kwargs.update(__UpperCAmelCase  )
  
  							for scheduler_class in self.scheduler_classes:
  													UpperCAmelCase_=     self.dummy_sample
  													UpperCAmelCase_=     0.1 * sample
  
  													UpperCAmelCase_=     self.get_scheduler_config()
  													UpperCAmelCase_=     scheduler_class(**__UpperCAmelCase  )
  
  													with tempfile.TemporaryDirectory() as tmpdirname:
  																			scheduler.save_config(__UpperCAmelCase  )
  																			UpperCAmelCase_=     scheduler_class.from_pretrained(__UpperCAmelCase  )
  
  													UpperCAmelCase_=     scheduler.step_pred(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  													UpperCAmelCase_=     new_scheduler.step_pred(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  
  													assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"
  
  													UpperCAmelCase_=     scheduler.step_correct(__UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  													UpperCAmelCase_=     new_scheduler.step_correct(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  
  													assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler correction are not identical"
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				int:
  							for timesteps in [10, 100, 1_000]:
  													self.check_over_configs(num_train_timesteps=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Union[str, Any]:
  							for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]		,						[1, 100, 1_000]  ):
  													self.check_over_configs(sigma_min=__UpperCAmelCase		,						sigma_max=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Union[str, Any]:
  							for t in [0.1, 0.5, 0.75]:
  													self.check_over_forward(time_step=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Dict:
  							UpperCAmelCase_=     dict(self.forward_default_kwargs  )
  
  							UpperCAmelCase_=     self.scheduler_classes[0]
  							UpperCAmelCase_=     self.get_scheduler_config()
  							UpperCAmelCase_=     scheduler_class(**__UpperCAmelCase  )
  
  							UpperCAmelCase_=     3
  
  							UpperCAmelCase_=     self.dummy_model()
  							UpperCAmelCase_=     self.dummy_sample_deter
  
  							scheduler.set_sigmas(__UpperCAmelCase  )
  							scheduler.set_timesteps(__UpperCAmelCase  )
  							UpperCAmelCase_=     torch.manual_seed(0  )
  
  							for i, t in enumerate(scheduler.timesteps  ):
  													UpperCAmelCase_=     scheduler.sigmas[i]
  
  													for _ in range(scheduler.config.correct_steps  ):
  																			with torch.no_grad():
  																									UpperCAmelCase_=     model(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     scheduler.step_correct(__UpperCAmelCase		,						__UpperCAmelCase		,						generator=__UpperCAmelCase		,						**__UpperCAmelCase  ).prev_sample
  
  													with torch.no_grad():
  																			UpperCAmelCase_=     model(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  													UpperCAmelCase_=     scheduler.step_pred(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						generator=__UpperCAmelCase		,						**__UpperCAmelCase  )
  													UpperCAmelCase_,     UpperCAmelCase_=     output.prev_sample, output.prev_sample_mean
  
  							UpperCAmelCase_=     torch.sum(torch.abs(__UpperCAmelCase  )  )
  							UpperCAmelCase_=     torch.mean(torch.abs(__UpperCAmelCase  )  )
  
  							assert np.isclose(result_sum.item()		,						14_372_758_528.0  )
  							assert np.isclose(result_mean.item()		,						18_714_530.0  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Optional[Any]:
  							UpperCAmelCase_=     dict(self.forward_default_kwargs  )
  
  							UpperCAmelCase_=     kwargs.pop("""num_inference_steps"""		,						__UpperCAmelCase  )
  
  							for scheduler_class in self.scheduler_classes:
  													UpperCAmelCase_=     self.get_scheduler_config()
  													UpperCAmelCase_=     scheduler_class(**__UpperCAmelCase  )
  
  													UpperCAmelCase_=     self.dummy_sample
  													UpperCAmelCase_=     0.1 * sample
  
  													if num_inference_steps is not None and hasattr(__UpperCAmelCase		,						"""set_timesteps"""  ):
  																			scheduler.set_timesteps(__UpperCAmelCase  )
  													elif num_inference_steps is not None and not hasattr(__UpperCAmelCase		,						"""set_timesteps"""  ):
  																			UpperCAmelCase_=     num_inference_steps
  
  													UpperCAmelCase_=     scheduler.step_pred(__UpperCAmelCase		,						0		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  													UpperCAmelCase_=     scheduler.step_pred(__UpperCAmelCase		,						1		,						__UpperCAmelCase		,						generator=torch.manual_seed(0  )		,						**__UpperCAmelCase  ).prev_sample
  
  													self.assertEqual(output_a.shape		,						sample.shape  )
  													self.assertEqual(output_a.shape		,						output_a.shape  )
  
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  __A       					=		{
      '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
  }
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"tvlt"
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		List[str]=2_048		,						__UpperCAmelCase			:		Tuple=128		,						__UpperCAmelCase			:		Union[str, Any]=[16, 16]		,						__UpperCAmelCase			:		str=[16, 16]		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=1		,						__UpperCAmelCase			:		Tuple=8		,						__UpperCAmelCase			:		Optional[Any]=768		,						__UpperCAmelCase			:		List[Any]=12		,						__UpperCAmelCase			:		Tuple=12		,						__UpperCAmelCase			:		List[str]=3_072		,						__UpperCAmelCase			:		List[Any]="gelu"		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		List[Any]=0.02		,						__UpperCAmelCase			:		Optional[int]=1E-6		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		List[Any]=False		,						__UpperCAmelCase			:		Union[str, Any]=16		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=8		,						__UpperCAmelCase			:		Dict=2_048		,						__UpperCAmelCase			:		Dict=0.75		,						__UpperCAmelCase			:		Union[str, Any]=0.15		,						__UpperCAmelCase			:		Any="frame-level"		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Union[str, Any]="classification"		,						**__UpperCAmelCase			:		Any		,						)    ->				Tuple:
  							super().__init__(**__UpperCAmelCase  )
  
  							if audio_mask_type not in ("frame-level", "patch_level"):
  													raise ValueError(
  													    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
  													    F"""got {audio_mask_type}"""  )
  
  							UpperCAmelCase_=     image_size
  							UpperCAmelCase_=     spectrogram_length
  							UpperCAmelCase_=     frequency_length
  							UpperCAmelCase_=     image_patch_size
  							UpperCAmelCase_=     audio_patch_size
  							UpperCAmelCase_=     num_image_channels
  							UpperCAmelCase_=     num_audio_channels
  							UpperCAmelCase_=     num_frames
  
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     layer_norm_eps
  							UpperCAmelCase_=     qkv_bias
  							UpperCAmelCase_=     use_mean_pooling
  
  							UpperCAmelCase_=     decoder_num_attention_heads
  							UpperCAmelCase_=     decoder_hidden_size
  							UpperCAmelCase_=     decoder_num_hidden_layers
  							UpperCAmelCase_=     decoder_intermediate_size
  							UpperCAmelCase_=     pixel_mask_ratio
  							UpperCAmelCase_=     audio_mask_ratio
  							UpperCAmelCase_=     audio_mask_type
  
  							UpperCAmelCase_=     task_matching
  							UpperCAmelCase_=     task_mae
  							UpperCAmelCase_=     loss_type
  
  
  
  
  