




'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Any   							=					(UnCLIPScheduler,)






      def 							lowercase_						( self   :  str						,						**lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """num_train_timesteps""": 1000,
               """variance_type""": """fixed_small_log""",
               """clip_sample""": True,
               """clip_sample_range""": 1.0,
               """prediction_type""": """epsilon""",
           }

           config.update(**lowercase__       )
           return config






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

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






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


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[int]       ):


           '''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=lowercase__						,						prev_timestep=lowercase__       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.scheduler_classes[0]
           a_     :							str     						=  self.get_scheduler_config(variance_type="""fixed_small_log"""       )
           a_     :							List[str]     						=  scheduler_class(**lowercase__       )

           assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0_0_0_0e-1_0       )       ) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0549625       )       ) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9994987       )       ) < 1e-5






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.scheduler_classes[0]
           a_     :							int     						=  self.get_scheduler_config(variance_type="""learned_range"""       )
           a_     :							Tuple     						=  scheduler_class(**lowercase__       )

           a_     :							Any     						=  0.5

           assert scheduler._get_variance(1						,						predicted_variance=lowercase__       ) - -10.1712790 < 1e-5
           assert scheduler._get_variance(487						,						predicted_variance=lowercase__       ) - -5.7998052 < 1e-5
           assert scheduler._get_variance(999						,						predicted_variance=lowercase__       ) - -0.0010011 < 1e-5






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.scheduler_classes[0]
           a_     :							Union[str, Any]     						=  self.get_scheduler_config()
           a_     :							Any     						=  scheduler_class(**lowercase__       )

           a_     :							List[str]     						=  scheduler.timesteps

           a_     :							Optional[Any]     						=  self.dummy_model()
           a_     :							Optional[int]     						=  self.dummy_sample_deter
           a_     :							List[Any]     						=  torch.manual_seed(0       )

           for i, t in enumerate(lowercase__       ):
                # 1. predict noise residual
                a_     :							str     						=  model(lowercase__						,						lowercase__       )

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

                a_     :							Any     						=  pred_prev_sample

           a_     :							List[str]     						=  torch.sum(torch.abs(lowercase__       )       )
           a_     :							Optional[int]     						=  torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 252.2682495       ) < 1e-2
           assert abs(result_mean.item() - 0.3284743       ) < 1e-3






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

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

           scheduler.set_timesteps(25       )

           a_     :							str     						=  scheduler.timesteps

           a_     :							Any     						=  self.dummy_model()
           a_     :							Union[str, Any]     						=  self.dummy_sample_deter
           a_     :							Union[str, Any]     						=  torch.manual_seed(0       )

           for i, t in enumerate(lowercase__       ):
                # 1. predict noise residual
                a_     :							Dict     						=  model(lowercase__						,						lowercase__       )

                if i + 1 == timesteps.shape[0]:
                     a_     :							Optional[Any]     						=  None
                else:
                     a_     :							Optional[Any]     						=  timesteps[i + 1]

                # 2. predict previous mean of sample x_t-1
                a_     :							str     						=  scheduler.step(
                    lowercase__						,						lowercase__						,						lowercase__						,						prev_timestep=lowercase__						,						generator=lowercase__       ).prev_sample

                a_     :							Optional[Any]     						=  pred_prev_sample

           a_     :							Any     						=  torch.sum(torch.abs(lowercase__       )       )
           a_     :							Tuple     						=  torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 258.2044983       ) < 1e-2
           assert abs(result_mean.item() - 0.3362038       ) < 1e-3






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           pass






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           pass






'''simple docstring'''


from ..utils import DummyObject, requires_backends







class      SCREAMING_SNAKE_CASE    (						metaclass=snake_case_				):
      __magic_name__  :       Dict   							=					['''transformers''', '''torch''', '''note_seq''']






      def __init__( self   :  List[str]						,						*lowercase__   :  Dict						,						**lowercase__   :  int       ):


           '''simple docstring'''

           requires_backends(self						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						*lowercase__   :  List[str]						,						**lowercase__   :  str       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  str						,						*lowercase__   :  Optional[int]						,						**lowercase__   :  Any       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )
