


'''simple docstring'''



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   str							= (UnCLIPScheduler,)




				def   a__(	self    :				Optional[int]   ,					**lowerCAmelCase    :				int   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			{
										    '''num_train_timesteps''': 1000,
										    '''variance_type''': '''fixed_small_log''',
										    '''clip_sample''': True,
										    '''clip_sample_range''': 1.0,
										    '''prediction_type''': '''epsilon''',
										}

										config.update(**lowerCAmelCase   )
										return config




				def   a__(	self    :				Dict   )->  Optional[Any]:

										"""simple docstring"""




										for timesteps in [1, 5, 100, 1000]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase   )




				def   a__(	self    :				Dict   )->  str:

										"""simple docstring"""




										for variance in ["fixed_small_log", "learned_range"]:
																self.check_over_configs(variance_type=lowerCAmelCase   )




				def   a__(	self    :				str   )->  Any:

										"""simple docstring"""




										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  str:

										"""simple docstring"""




										for clip_sample_range in [1, 5, 10, 20]:
																self.check_over_configs(clip_sample_range=lowerCAmelCase   )




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

										"""simple docstring"""




										for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(prediction_type=lowerCAmelCase   )




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

										"""simple docstring"""




										for time_step in [0, 500, 999]:
																for prev_timestep in [None, 5, 100, 250, 500, 750]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																												continue

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




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

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config(variance_type='''fixed_small_log'''   )
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0549625   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9994987   )   ) < 1E-5




				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config(variance_type='''learned_range'''   )
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										UpperCAmelCase         =			0.5

										assert scheduler._get_variance(1   ,					predicted_variance=lowerCAmelCase   ) - -10.1712790 < 1E-5
										assert scheduler._get_variance(487   ,					predicted_variance=lowerCAmelCase   ) - -5.7998052 < 1E-5
										assert scheduler._get_variance(999   ,					predicted_variance=lowerCAmelCase   ) - -0.0010011 < 1E-5




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

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config()
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										UpperCAmelCase         =			scheduler.timesteps

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

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

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

																UpperCAmelCase         =			pred_prev_sample

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 252.2682495   ) < 1E-2
										assert abs(result_mean.item() - 0.3284743   ) < 1E-3




				def   a__(	self    :				List[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config()
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										scheduler.set_timesteps(25   )

										UpperCAmelCase         =			scheduler.timesteps

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

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

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

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

																UpperCAmelCase         =			pred_prev_sample

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 258.2044983   ) < 1E-2
										assert abs(result_mean.item() - 0.3362038   ) < 1E-3




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

										"""simple docstring"""




										pass





				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										pass









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''   )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''   )

										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor





