'''simple docstring'''




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class        a__ (  UpperCAmelCase_					):
     lowerCamelCase    :	Union[str, Any]				=(CMStochasticIterativeScheduler,)
     lowerCamelCase    :	Optional[Any]				=1_0






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							**a						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    {
             """num_train_timesteps""": 2_01,
             """sigma_min""": 0.0_02,
             """sigma_max""": 80.0,
         }

         config.update(**lowerCamelCase__       )
         return config






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    10

         __lowerCamelCase            =    self.get_scheduler_config()
         __lowerCamelCase            =    self.scheduler_classes[0](**lowerCamelCase__       )

         scheduler.set_timesteps(lowerCamelCase__       )

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

         __lowerCamelCase            =    self.dummy_sample
         __lowerCamelCase            =    0.1 * sample

         __lowerCamelCase            =    scheduler.step(lowerCamelCase__    ,							lowerCamelCase__    ,							lowerCamelCase__       ).prev_sample
         __lowerCamelCase            =    scheduler.step(lowerCamelCase__    ,							lowerCamelCase__    ,							lowerCamelCase__       ).prev_sample

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






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=lowerCamelCase__       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



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






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.scheduler_classes[0]
         __lowerCamelCase            =    self.get_scheduler_config()
         __lowerCamelCase            =    scheduler_class(**lowerCamelCase__       )

         __lowerCamelCase            =    1
         scheduler.set_timesteps(lowerCamelCase__       )
         __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(lowerCamelCase__       ):
             # 1. scale model input
             __lowerCamelCase            =    scheduler.scale_model_input(lowerCamelCase__    ,							lowerCamelCase__       )

             # 2. predict noise residual
             __lowerCamelCase            =    model(lowerCamelCase__    ,							lowerCamelCase__       )

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

             __lowerCamelCase            =    pred_prev_sample

         __lowerCamelCase            =    torch.sum(torch.abs(lowerCamelCase__       )       )
         __lowerCamelCase            =    torch.mean(torch.abs(lowerCamelCase__       )       )

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






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.scheduler_classes[0]
         __lowerCamelCase            =    self.get_scheduler_config()
         __lowerCamelCase            =    scheduler_class(**lowerCamelCase__       )

         __lowerCamelCase            =    [1_06, 0]
         scheduler.set_timesteps(timesteps=lowerCamelCase__       )
         __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(lowerCamelCase__    ,							lowerCamelCase__       )

             # 2. predict noise residual
             __lowerCamelCase            =    model(lowerCamelCase__    ,							lowerCamelCase__       )

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

             __lowerCamelCase            =    pred_prev_sample

         __lowerCamelCase            =    torch.sum(torch.abs(lowerCamelCase__       )       )
         __lowerCamelCase            =    torch.mean(torch.abs(lowerCamelCase__       )       )

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






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    self.scheduler_classes[0]
         __lowerCamelCase            =    self.get_scheduler_config()
         __lowerCamelCase            =    scheduler_class(**lowerCamelCase__       )

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

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






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.scheduler_classes[0]
         __lowerCamelCase            =    self.get_scheduler_config()
         __lowerCamelCase            =    scheduler_class(**lowerCamelCase__       )

         __lowerCamelCase            =    [39, 30, 12, 1, 0]
         __lowerCamelCase            =    len(lowerCamelCase__       )

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






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase            =    self.scheduler_classes[0]
         __lowerCamelCase            =    self.get_scheduler_config()
         __lowerCamelCase            =    scheduler_class(**lowerCamelCase__       )

         __lowerCamelCase            =    [scheduler.config.num_train_timesteps]

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








import warnings

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


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

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



