




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(UnCLIPScheduler,)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			**UpperCAmelCase__				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   {
               '''num_train_timesteps''': 1_000,
               '''variance_type''': '''fixed_small_log''',
               '''clip_sample''': True,
               '''clip_sample_range''': 1.0,
               '''prediction_type''': '''epsilon''',
           }

           config.update(**UpperCAmelCase__)
           return config


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





           '''simple docstring'''

           for timesteps in [1, 5, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Tuple:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->List[Any]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[Any]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Tuple:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->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=UpperCAmelCase__ ,			prev_timestep=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Dict:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config(variance_type='''fixed_small_log''')
           A__							=   scheduler_class(**UpperCAmelCase__)

           assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.00_00e-10)) < 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 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Tuple:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config(variance_type='''learned_range''')
           A__							=   scheduler_class(**UpperCAmelCase__)

           A__							=   0.5

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Tuple:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config()
           A__							=   scheduler_class(**UpperCAmelCase__)

           A__							=   scheduler.timesteps

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter
           A__							=   torch.manual_seed(0)

           for i, t in enumerate(UpperCAmelCase__):
                  # 1. predict noise residual
                  A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__)

                  # 2. predict previous mean of sample x_t-1
                  A__							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			generator=UpperCAmelCase__).prev_sample

                  A__							=   pred_prev_sample

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Dict:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config()
           A__							=   scheduler_class(**UpperCAmelCase__)

           scheduler.set_timesteps(25)

           A__							=   scheduler.timesteps

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter
           A__							=   torch.manual_seed(0)

           for i, t in enumerate(UpperCAmelCase__):
                  # 1. predict noise residual
                  A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__)

                  if i + 1 == timesteps.shape[0]:
                         A__							=   None
                  else:
                         A__							=   timesteps[i + 1]

                  # 2. predict previous mean of sample x_t-1
                  A__							=   scheduler.step(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			prev_timestep=UpperCAmelCase__ ,			generator=UpperCAmelCase__).prev_sample

                  A__							=   pred_prev_sample

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Optional[Any]:





           '''simple docstring'''

           pass





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Optional[Any]:





           '''simple docstring'''

           pass








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       return " ".join(
           ''''''.join(word[::-1]						) if len(lowercase_						) > 4 else word for word in sentence.split()						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words("""Hey wollef sroirraw"""))


