






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 		lowerCAmelCase     ( __SCREAMING_SNAKE_CASE			):

					'''simple docstring'''

					_A						:			Union[str, Any]									=      (CMStochasticIterativeScheduler,)
					_A						:			Union[str, Any]									=      10



					def       lowerCAmelCase				(  self				:	Dict		,     **__a				:	int							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     {
												    """num_train_timesteps""": 201,
												    """sigma_min""": 0.002,
												    """sigma_max""": 80.0,
												}

												config.update(**_a							)
												return config



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


												"""simple docstring"""

												__lowercase      :							Any				=     10

												__lowercase      :							Optional[Any]				=     self.get_scheduler_config()
												__lowercase      :							Union[str, Any]				=     self.scheduler_classes[0](**_a							)

												scheduler.set_timesteps(_a							)

												__lowercase      :							List[str]				=     scheduler.timesteps[0]
												__lowercase      :							Union[str, Any]				=     scheduler.timesteps[1]

												__lowercase      :							Optional[Any]				=     self.dummy_sample
												__lowercase      :							int				=     0.1 * sample

												__lowercase      :							Optional[Any]				=     scheduler.step(_a		,     _a		,     _a							).prev_sample
												__lowercase      :							str				=     scheduler.step(_a		,     _a		,     _a							).prev_sample

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



					def       lowerCAmelCase				(  self				:	Any							)			->  Any:


												"""simple docstring"""

												for timesteps in [10, 50, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=_a							)



					def       lowerCAmelCase				(  self				:	int							)			->  Any:


												"""simple docstring"""

												for clip_denoised in [True, False]:
																			self.check_over_configs(clip_denoised=_a							)



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							int				=     self.scheduler_classes[0]
												__lowercase      :							Any				=     self.get_scheduler_config()
												__lowercase      :							List[Any]				=     scheduler_class(**_a							)

												__lowercase      :							Optional[int]				=     1
												scheduler.set_timesteps(_a							)
												__lowercase      :							List[str]				=     scheduler.timesteps

												__lowercase      :							Tuple				=     torch.manual_seed(0							)

												__lowercase      :							Dict				=     self.dummy_model()
												__lowercase      :							Tuple				=     self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(_a							):
																			# 1. scale model input
																			__lowercase      :							Union[str, Any]				=     scheduler.scale_model_input(_a		,     _a							)

																			# 2. predict noise residual
																			__lowercase      :							List[Any]				=     model(_a		,     _a							)

																			# 3. predict previous sample x_t-1
																			__lowercase      :							Any				=     scheduler.step(_a		,     _a		,     _a		,     generator=_a							).prev_sample

																			__lowercase      :							str				=     pred_prev_sample

												__lowercase      :							int				=     torch.sum(torch.abs(_a							)							)
												__lowercase      :							Optional[int]				=     torch.mean(torch.abs(_a							)							)

												assert abs(result_sum.item() - 192.7614							) < 1E-2
												assert abs(result_mean.item() - 0.2510							) < 1E-3



					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.scheduler_classes[0]
												__lowercase      :							Tuple				=     self.get_scheduler_config()
												__lowercase      :							List[Any]				=     scheduler_class(**_a							)

												__lowercase      :							List[str]				=     [106, 0]
												scheduler.set_timesteps(timesteps=_a							)
												__lowercase      :							List[Any]				=     scheduler.timesteps

												__lowercase      :							Any				=     torch.manual_seed(0							)

												__lowercase      :							Optional[int]				=     self.dummy_model()
												__lowercase      :							List[str]				=     self.dummy_sample_deter * scheduler.init_noise_sigma

												for t in timesteps:
																			# 1. scale model input
																			__lowercase      :							Any				=     scheduler.scale_model_input(_a		,     _a							)

																			# 2. predict noise residual
																			__lowercase      :							Tuple				=     model(_a		,     _a							)

																			# 3. predict previous sample x_t-1
																			__lowercase      :							Optional[Any]				=     scheduler.step(_a		,     _a		,     _a		,     generator=_a							).prev_sample

																			__lowercase      :							Any				=     pred_prev_sample

												__lowercase      :							List[str]				=     torch.sum(torch.abs(_a							)							)
												__lowercase      :							List[Any]				=     torch.mean(torch.abs(_a							)							)

												assert abs(result_sum.item() - 347.6357							) < 1E-2
												assert abs(result_mean.item() - 0.4527							) < 1E-3



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							str				=     self.scheduler_classes[0]
												__lowercase      :							Union[str, Any]				=     self.get_scheduler_config()
												__lowercase      :							int				=     scheduler_class(**_a							)

												__lowercase      :							Tuple				=     [39, 30, 12, 15, 0]

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



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


												"""simple docstring"""

												__lowercase      :							Dict				=     self.scheduler_classes[0]
												__lowercase      :							str				=     self.get_scheduler_config()
												__lowercase      :							List[str]				=     scheduler_class(**_a							)

												__lowercase      :							Union[str, Any]				=     [39, 30, 12, 1, 0]
												__lowercase      :							int				=     len(_a							)

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



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.scheduler_classes[0]
												__lowercase      :							Optional[Any]				=     self.get_scheduler_config()
												__lowercase      :							Tuple				=     scheduler_class(**_a							)

												__lowercase      :							Any				=     [scheduler.config.num_train_timesteps]

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







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase : Union[str, Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	int		,     *__a				:	Dict		,     **__a				:	Optional[Any]							)			->  None:


												"""simple docstring"""

												warnings.warn(
												    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use BeitImageProcessor instead."""		,     __a		,     )
												super().__init__(*__a		,     **__a							)