





"""simple docstring"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   (IPNDMScheduler,)
				UpperCamelCase								=   (('''num_inference_steps''', 50),)






				def     snake_case     (			self		:   Any,			**lowerCAmelCase__		:   List[Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   {'''num_train_timesteps''': 1_0_0_0}
										config.update(**lowerCAmelCase__							)
										return config






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Union[str, Any]=0,			**lowerCAmelCase__		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   dict(self.forward_default_kwargs							)
										_UpperCamelCase      : List[Any]         =   kwargs.pop('''num_inference_steps''',			lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   self.dummy_sample
										_UpperCamelCase      : Dict         =   0.1 * sample
										_UpperCamelCase      : Union[str, Any]         =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
																_UpperCamelCase      : Dict         =   self.get_scheduler_config(**lowerCAmelCase__							)
																_UpperCamelCase      : Any         =   scheduler_class(**lowerCAmelCase__							)
																scheduler.set_timesteps(lowerCAmelCase__							)
																# copy over dummy past residuals
																_UpperCamelCase      : Tuple         =   dummy_past_residuals[:]

																if time_step is None:
																						_UpperCamelCase      : Any         =   scheduler.timesteps[len(scheduler.timesteps							) // 2]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(lowerCAmelCase__							)
																						_UpperCamelCase      : Tuple         =   scheduler_class.from_pretrained(lowerCAmelCase__							)
																						new_scheduler.set_timesteps(lowerCAmelCase__							)
																						# copy over dummy past residuals
																						_UpperCamelCase      : int         =   dummy_past_residuals[:]

																_UpperCamelCase      : Dict         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample
																_UpperCamelCase      : Tuple         =   new_scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample

																assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

																_UpperCamelCase      : Dict         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample
																_UpperCamelCase      : Union[str, Any]         =   new_scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample

																assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"






				def     snake_case     (			self		:   Optional[int]							)     ->				Optional[int]:




										'''simple docstring'''



										pass






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Dict=0,			**lowerCAmelCase__		:   Union[str, Any]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   dict(self.forward_default_kwargs							)
										_UpperCamelCase      : Optional[int]         =   kwargs.pop('''num_inference_steps''',			lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   self.dummy_sample
										_UpperCamelCase      : Tuple         =   0.1 * sample
										_UpperCamelCase      : Dict         =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
																_UpperCamelCase      : Optional[int]         =   self.get_scheduler_config()
																_UpperCamelCase      : Optional[int]         =   scheduler_class(**lowerCAmelCase__							)
																scheduler.set_timesteps(lowerCAmelCase__							)

																# copy over dummy past residuals (must be after setting timesteps)
																_UpperCamelCase      : Optional[int]         =   dummy_past_residuals[:]

																if time_step is None:
																						_UpperCamelCase      : Optional[int]         =   scheduler.timesteps[len(scheduler.timesteps							) // 2]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(lowerCAmelCase__							)
																						_UpperCamelCase      : Any         =   scheduler_class.from_pretrained(lowerCAmelCase__							)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(lowerCAmelCase__							)

																						# copy over dummy past residual (must be after setting timesteps)
																						_UpperCamelCase      : int         =   dummy_past_residuals[:]

																_UpperCamelCase      : Optional[Any]         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample
																_UpperCamelCase      : Any         =   new_scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample

																assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

																_UpperCamelCase      : List[Any]         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample
																_UpperCamelCase      : Tuple         =   new_scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample

																assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"






				def     snake_case     (			self		:   Optional[int],			**lowerCAmelCase__		:   List[Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.scheduler_classes[0]
										_UpperCamelCase      : str         =   self.get_scheduler_config(**lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   scheduler_class(**lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   1_0
										_UpperCamelCase      : List[Any]         =   self.dummy_model()
										_UpperCamelCase      : Tuple         =   self.dummy_sample_deter
										scheduler.set_timesteps(lowerCAmelCase__							)

										for i, t in enumerate(scheduler.timesteps							):
																_UpperCamelCase      : Optional[int]         =   model(lowerCAmelCase__,			lowerCAmelCase__							)
																_UpperCamelCase      : Dict         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							).prev_sample

										for i, t in enumerate(scheduler.timesteps							):
																_UpperCamelCase      : Any         =   model(lowerCAmelCase__,			lowerCAmelCase__							)
																_UpperCamelCase      : Dict         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							).prev_sample

										return sample






				def     snake_case     (			self		:   Union[str, Any]							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   dict(self.forward_default_kwargs							)

										_UpperCamelCase      : List[str]         =   kwargs.pop('''num_inference_steps''',			lowerCAmelCase__							)

										for scheduler_class in self.scheduler_classes:
																_UpperCamelCase      : Any         =   self.get_scheduler_config()
																_UpperCamelCase      : Optional[Any]         =   scheduler_class(**lowerCAmelCase__							)

																_UpperCamelCase      : Optional[Any]         =   self.dummy_sample
																_UpperCamelCase      : Union[str, Any]         =   0.1 * sample

																if num_inference_steps is not None and hasattr(lowerCAmelCase__,			'''set_timesteps'''							):
																						scheduler.set_timesteps(lowerCAmelCase__							)
																elif num_inference_steps is not None and not hasattr(lowerCAmelCase__,			'''set_timesteps'''							):
																						_UpperCamelCase      : int         =   num_inference_steps

																# copy over dummy past residuals (must be done after set_timesteps)
																_UpperCamelCase      : Tuple         =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																_UpperCamelCase      : Optional[Any]         =   dummy_past_residuals[:]

																_UpperCamelCase      : Dict         =   scheduler.timesteps[5]
																_UpperCamelCase      : str         =   scheduler.timesteps[6]

																_UpperCamelCase      : int         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample
																_UpperCamelCase      : Any         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample

																self.assertEqual(output_a.shape,			sample.shape							)
																self.assertEqual(output_a.shape,			output_a.shape							)

																_UpperCamelCase      : List[Any]         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample
																_UpperCamelCase      : Tuple         =   scheduler.step(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							).prev_sample

																self.assertEqual(output_a.shape,			sample.shape							)
																self.assertEqual(output_a.shape,			output_a.shape							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Dict							)     ->				List[Any]:




										'''simple docstring'''



										for t, num_inference_steps in zip([1, 5, 1_0],			[1_0, 5_0, 1_0_0]							):
																self.check_over_forward(num_inference_steps=lowerCAmelCase__,			time_step=lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.full_loop()
										_UpperCamelCase      : int         =   torch.mean(torch.abs(lowerCAmelCase__							)							)

										assert abs(result_mean.item() - 2_5_4_0_5_2_9							) < 1_0










"""simple docstring"""




from ...processing_utils import ProcessorMixin




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''feature_extractor''']
				UpperCamelCase								=   '''TvltImageProcessor'''
				UpperCamelCase								=   '''TvltFeatureExtractor'''






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__(image_processor=lowerCAmelCase__,			feature_extractor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   image_processor
										_UpperCamelCase      : int         =   feature_extractor






				def __call__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Dict=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Optional[int]=False,			lowerCAmelCase__		:   str=False,			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   Optional[int],			)     ->				Dict:




										'''simple docstring'''



										if images is None and audio is None:
																raise ValueError('''You need to specify either an `images` or `audio` input to process.'''							)

										_UpperCamelCase      : Optional[int]         =   None
										if images is not None:
																_UpperCamelCase      : Optional[int]         =   self.image_processor(lowerCAmelCase__,			mask_pixel=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if images_mixed is not None:
																_UpperCamelCase      : str         =   self.image_processor(lowerCAmelCase__,			is_mixed=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if audio is not None:
																_UpperCamelCase      : Union[str, Any]         =   self.feature_extractor(
																    lowerCAmelCase__,			*lowerCAmelCase__,			sampling_rate=lowerCAmelCase__,			mask_audio=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : str         =   {}
										if audio is not None:
																output_dict.update(lowerCAmelCase__							)
										if images is not None:
																output_dict.update(lowerCAmelCase__							)
										if images_mixed_dict is not None:
																output_dict.update(lowerCAmelCase__							)
										return output_dict






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.image_processor.model_input_names
										_UpperCamelCase      : List[Any]         =   self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names							)							)



