



"""simple docstring"""





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	(CMStochasticIterativeScheduler,)
       UpperCamelCase		=	10



       def 	lowercase__							(				self     :    Optional[int]						,							**_UpperCAmelCase     :    Dict       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				{
                 "num_train_timesteps": 201,
                 "sigma_min": 0.002,
                 "sigma_max": 80.0,
             }

             config.update(**_UpperCAmelCase       )
             return config



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


             '''simple docstring'''




             UpperCAmelCase_     					=				10

             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				self.scheduler_classes[0](**_UpperCAmelCase       )

             scheduler.set_timesteps(_UpperCAmelCase       )

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

             UpperCAmelCase_     					=				self.dummy_sample
             UpperCAmelCase_     					=				0.1 * sample

             UpperCAmelCase_     					=				scheduler.step(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       ).prev_sample
             UpperCAmelCase_     					=				scheduler.step(_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase       ).prev_sample

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



       def 	lowercase__							(				self     :    List[Any]       )					->		Tuple:


             '''simple docstring'''




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



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


             '''simple docstring'''




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



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


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				1
             scheduler.set_timesteps(_UpperCAmelCase       )
             UpperCAmelCase_     					=				scheduler.timesteps

             UpperCAmelCase_     					=				torch.manual_seed(0       )

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

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

                   # 2. predict noise residual
                   UpperCAmelCase_     					=				model(_UpperCAmelCase						,							_UpperCAmelCase       )

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

                   UpperCAmelCase_     					=				pred_prev_sample

             UpperCAmelCase_     					=				torch.sum(torch.abs(_UpperCAmelCase       )       )
             UpperCAmelCase_     					=				torch.mean(torch.abs(_UpperCAmelCase       )       )

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



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


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				[106, 0]
             scheduler.set_timesteps(timesteps=_UpperCAmelCase       )
             UpperCAmelCase_     					=				scheduler.timesteps

             UpperCAmelCase_     					=				torch.manual_seed(0       )

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

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

                   # 2. predict noise residual
                   UpperCAmelCase_     					=				model(_UpperCAmelCase						,							_UpperCAmelCase       )

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

                   UpperCAmelCase_     					=				pred_prev_sample

             UpperCAmelCase_     					=				torch.sum(torch.abs(_UpperCAmelCase       )       )
             UpperCAmelCase_     					=				torch.mean(torch.abs(_UpperCAmelCase       )       )

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



       def 	lowercase__							(				self     :    Tuple       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

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

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



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


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				[39, 30, 12, 1, 0]
             UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Union[str, Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.scheduler_classes[0]
             UpperCAmelCase_     					=				self.get_scheduler_config()
             UpperCAmelCase_     					=				scheduler_class(**_UpperCAmelCase       )

             UpperCAmelCase_     					=				[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       )










import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__a							     =					logging.get_logger(__name__)

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						None:
         warnings.warn(
             '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use CLIPImageProcessor instead.'''  ,_SCREAMING_SNAKE_CASE  ,)
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )