


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       (EulerDiscreteScheduler,)
      UpperCamelCase								=       10





      def        _lowercase				(		self						:					Tuple					,       **__lowerCamelCase						:					int       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     {
             """num_train_timesteps""": 1_1_0_0,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
         }

         config.update(**__lowerCamelCase       )
         return config





      def        _lowercase				(		self						:					List[Any]       )    ->   int:






         """simple docstring"""







         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__lowerCamelCase       )





      def        _lowercase				(		self						:					str       )    ->   str:






         """simple docstring"""







         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]					,       [0.0_002, 0.002, 0.02]       ):
            self.check_over_configs(beta_start=__lowerCamelCase					,       beta_end=__lowerCamelCase       )





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






         """simple docstring"""







         for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=__lowerCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Tuple:






         """simple docstring"""







         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowerCamelCase       )





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






         """simple docstring"""







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

         scheduler.set_timesteps(self.num_inference_steps       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

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

         assert abs(result_sum.item() - 10.0_807       ) < 1e-2
         assert abs(result_mean.item() - 0.0_131       ) < 1e-3





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






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config(prediction_type="""v_prediction"""       )
         UpperCAmelCase							=     scheduler_class(**__lowerCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

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

         assert abs(result_sum.item() - 0.0_002       ) < 1e-2
         assert abs(result_mean.item() - 2.2_6_7_6e-0_6       ) < 1e-3





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






         """simple docstring"""







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

         scheduler.set_timesteps(self.num_inference_steps					,       device=__lowerCamelCase       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

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

         assert abs(result_sum.item() - 10.0_807       ) < 1e-2
         assert abs(result_mean.item() - 0.0_131       ) < 1e-3





      def        _lowercase				(		self						:					Dict       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__lowerCamelCase					,       use_karras_sigmas=__lowerCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps					,       device=__lowerCamelCase       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

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

         assert abs(result_sum.item() - 124.52_299_499_511_719       ) < 1e-2
         assert abs(result_mean.item() - 0.16_213_932_633_399_963       ) < 1e-3







__a     =			[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   while place < len(lowerCAmelCase_   ):
      if (place + 1 < len(lowerCAmelCase_   )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     []
   for arabic, roman in ROMAN:
      ((UpperCAmelCase)				,  (UpperCAmelCase))							=     divmod(lowerCAmelCase_  ,						lowerCAmelCase_   )
      result.append(roman * factor   )
      if number == 0:
         break
   return "".join(lowerCAmelCase_   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



