



'''simple docstring'''



import warnings

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






class 					UpperCAmelCase__	(		UpperCamelCase_ ):
					a      :     Union[str, Any]        =							["""image_processor""", """tokenizer"""]
					a      :     Optional[Any]        =							"""ViTImageProcessor"""
					a      :     Optional[Any]        =							("""CLIPTokenizer""", """CLIPTokenizerFast""")



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				**UpperCamelCase     )     ->			List[Any]:
						__lowerCAmelCase												=  None
						if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."			,				__A			,				)
							__lowerCAmelCase												=  kwargs.pop("feature_extractor"     )

						__lowerCAmelCase												=  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__(__A			,				__A     )



					def __call__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				**UpperCamelCase     )     ->			List[Any]:
						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:
							__lowerCAmelCase												=  self.tokenizer(__A			,				return_tensors=__A			,				**__A     )

						if visual_prompt is not None:
							__lowerCAmelCase												=  self.image_processor(__A			,				return_tensors=__A			,				**__A     )

						if images is not None:
							__lowerCAmelCase												=  self.image_processor(__A			,				return_tensors=__A			,				**__A     )

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



					def        UpperCAmelCase_ (    self			,				*UpperCamelCase			,				**UpperCamelCase     )     ->			Union[str, Any]:
						return self.tokenizer.batch_decode(*__A			,				**__A     )



					def        UpperCAmelCase_ (    self			,				*UpperCamelCase			,				**UpperCamelCase     )     ->			int:
						return self.tokenizer.decode(*__A			,				**__A     )



					@property
					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,				__A			,				)
						return self.image_processor_class



					@property
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,				__A			,				)
						return self.image_processor




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

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



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )