
'''simple docstring'''







import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				List[Any]   =					(UnCLIPScheduler,)


  def   __lowercase		(		self      :     Any							,**_a      :     str       ):


     '''simple docstring'''






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

     config.update(**_a       )
     return config


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[Any]       ):


     '''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=_a							,prev_timestep=_a       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   self.scheduler_classes[0]
     _a				:	str			=   self.get_scheduler_config(variance_type='fixed_small_log'       )
     _a				:	Union[str, Any]			=   scheduler_class(**_a       )

     assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000E-10       )       ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.054_9625       )       ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.999_4987       )       ) < 1E-5


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	int			=   self.scheduler_classes[0]
     _a				:	str			=   self.get_scheduler_config(variance_type='learned_range'       )
     _a				:	str			=   scheduler_class(**_a       )

     _a				:	Any			=   0.5

     assert scheduler._get_variance(1							,predicted_variance=_a       ) - -10.171_2790 < 1E-5
     assert scheduler._get_variance(487							,predicted_variance=_a       ) - -5.799_8052 < 1E-5
     assert scheduler._get_variance(999							,predicted_variance=_a       ) - -0.001_0011 < 1E-5


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






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

     _a				:	List[str]			=   scheduler.timesteps

     _a				:	int			=   self.dummy_model()
     _a				:	Any			=   self.dummy_sample_deter
     _a				:	Dict			=   torch.manual_seed(0       )

     for i, t in enumerate(_a       ):
        # 1. predict noise residual
        _a				:	List[str]			=   model(_a							,_a       )

        # 2. predict previous mean of sample x_t-1
        _a				:	Any			=   scheduler.step(_a							,_a							,_a							,generator=_a       ).prev_sample

        _a				:	List[str]			=   pred_prev_sample

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

     assert abs(result_sum.item() - 252.268_2495       ) < 1E-2
     assert abs(result_mean.item() - 0.328_4743       ) < 1E-3


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






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

     scheduler.set_timesteps(25       )

     _a				:	List[str]			=   scheduler.timesteps

     _a				:	List[str]			=   self.dummy_model()
     _a				:	List[str]			=   self.dummy_sample_deter
     _a				:	str			=   torch.manual_seed(0       )

     for i, t in enumerate(_a       ):
        # 1. predict noise residual
        _a				:	List[str]			=   model(_a							,_a       )

        if i + 1 == timesteps.shape[0]:
           _a				:	Optional[int]			=   None
        else:
           _a				:	List[str]			=   timesteps[i + 1]

        # 2. predict previous mean of sample x_t-1
        _a				:	List[Any]			=   scheduler.step(
            _a							,_a							,_a							,prev_timestep=_a							,generator=_a       ).prev_sample

        _a				:	Dict			=   pred_prev_sample

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

     assert abs(result_sum.item() - 258.204_4983       ) < 1E-2
     assert abs(result_mean.item() - 0.336_2038       ) < 1E-3


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     pass





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,*_a      :     List[str]							,**_a      :     Any       ):


     '''simple docstring'''






     warnings.warn(
         'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use VideoMAEImageProcessor instead.'							,_a							,)
     super().__init__(*_a							,**_a       )



