




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (IPNDMScheduler,)
    lowerCAmelCase         = (('''num_inference_steps''', 50),)





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Tuple:
         UpperCAmelCase_     :				Any  =     {'''num_train_timesteps''': 1_000}
         config.update(**_SCREAMING_SNAKE_CASE    )
         return config





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE=0  ,**_SCREAMING_SNAKE_CASE    )					->						Dict:
         UpperCAmelCase_     :				str  =     dict(self.forward_default_kwargs    )
         UpperCAmelCase_     :				Union[str, Any]  =     kwargs.pop('''num_inference_steps'''  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     self.dummy_sample
         UpperCAmelCase_     :				Any  =     0.1 * sample
         UpperCAmelCase_     :				Optional[Any]  =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_     :				Tuple  =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				str  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
              # copy over dummy past residuals
              UpperCAmelCase_     :				Dict  =     dummy_past_residuals[:]

              if time_step is None:
                   UpperCAmelCase_     :				Optional[Any]  =     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Dict  =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
                   new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
                   # copy over dummy past residuals
                   UpperCAmelCase_     :				List[Any]  =     dummy_past_residuals[:]

              UpperCAmelCase_     :				Optional[Any]  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample
              UpperCAmelCase_     :				Tuple  =     new_scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample

              assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase_     :				Optional[Any]  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample
              UpperCAmelCase_     :				str  =     new_scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample

              assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"





    def 				a__ ( self    )					->						List[Any]:
         pass





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE=0  ,**_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         UpperCAmelCase_     :				List[str]  =     dict(self.forward_default_kwargs    )
         UpperCAmelCase_     :				List[Any]  =     kwargs.pop('''num_inference_steps'''  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.dummy_sample
         UpperCAmelCase_     :				Optional[Any]  =     0.1 * sample
         UpperCAmelCase_     :				List[Any]  =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_     :				List[Any]  =     self.get_scheduler_config()
              UpperCAmelCase_     :				str  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

              # copy over dummy past residuals (must be after setting timesteps)
              UpperCAmelCase_     :				Dict  =     dummy_past_residuals[:]

              if time_step is None:
                   UpperCAmelCase_     :				int  =     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Optional[Any]  =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

                   # copy over dummy past residual (must be after setting timesteps)
                   UpperCAmelCase_     :				str  =     dummy_past_residuals[:]

              UpperCAmelCase_     :				List[Any]  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample
              UpperCAmelCase_     :				Optional[int]  =     new_scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample

              assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase_     :				int  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample
              UpperCAmelCase_     :				List[str]  =     new_scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample

              assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Dict  =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				str  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[str]  =     10
         UpperCAmelCase_     :				Any  =     self.dummy_model()
         UpperCAmelCase_     :				Tuple  =     self.dummy_sample_deter
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

         for i, t in enumerate(scheduler.timesteps    ):
              UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample

         for i, t in enumerate(scheduler.timesteps    ):
              UpperCAmelCase_     :				str  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Dict  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample

         return sample





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Tuple  =     dict(self.forward_default_kwargs    )

         UpperCAmelCase_     :				List[str]  =     kwargs.pop('''num_inference_steps'''  ,_SCREAMING_SNAKE_CASE    )

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_     :				Any  =     self.get_scheduler_config()
              UpperCAmelCase_     :				str  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

              UpperCAmelCase_     :				List[Any]  =     self.dummy_sample
              UpperCAmelCase_     :				Any  =     0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE  ,'''set_timesteps'''    ):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE  ,'''set_timesteps'''    ):
                   UpperCAmelCase_     :				int  =     num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCAmelCase_     :				Any  =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              UpperCAmelCase_     :				str  =     dummy_past_residuals[:]

              UpperCAmelCase_     :				Union[str, Any]  =     scheduler.timesteps[5]
              UpperCAmelCase_     :				Dict  =     scheduler.timesteps[6]

              UpperCAmelCase_     :				Optional[Any]  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample
              UpperCAmelCase_     :				int  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample

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

              UpperCAmelCase_     :				List[str]  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample
              UpperCAmelCase_     :				List[Any]  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    ).prev_sample

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





    def 				a__ ( self    )					->						Tuple:
         for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE  ,time_step=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[int]:
         for t, num_inference_steps in zip([1, 5, 10]  ,[10, 50, 100]    ):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE  ,time_step=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Union[str, Any]  =     self.full_loop()
         UpperCAmelCase_     :				List[Any]  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_mean.item() - 2_540_529    ) < 10





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    )