


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class   __lowercase				(  __lowercase  ):
      UpperCamelCase								=       (DDPMScheduler,)





      def        _lowercase				(		self						:					Any					,       **__lowerCamelCase						:					int       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     {
             """num_train_timesteps""": 1_0_0_0,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """variance_type""": """fixed_small""",
             """clip_sample""": True,
         }

         config.update(**__a       )
         return config





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






         """simple docstring"""







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





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,       [0.002, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=__a					,       beta_end=__a       )





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






         """simple docstring"""







         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__a       )





      def        _lowercase				(		self						:					str       )    ->   str:






         """simple docstring"""







         for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=__a       )





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






         """simple docstring"""







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





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






         """simple docstring"""







         self.check_over_configs(thresholding=__a       )
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(
                   thresholding=__a					,       prediction_type=__a					,       sample_max_value=__a					,       )





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






         """simple docstring"""







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





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






         """simple docstring"""







         for t in [0, 5_0_0, 9_9_9]:
            self.check_over_forward(time_step=__a       )





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






         """simple docstring"""







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

         assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.00_979       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5





      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







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

         UpperCAmelCase							=     len(__a       )

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

         for t in reversed(range(__a       )       ):
            # 1. predict noise residual
            UpperCAmelCase							=     model(__a					,       __a       )

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

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase							=     pred_prev_sample

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

         assert abs(result_sum.item() - 2_5_8.9_6_0_6       ) < 1e-2
         assert abs(result_mean.item() - 0.3_372       ) < 1e-3





      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







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

         UpperCAmelCase							=     len(__a       )

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

         for t in reversed(range(__a       )       ):
            # 1. predict noise residual
            UpperCAmelCase							=     model(__a					,       __a       )

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

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase							=     pred_prev_sample

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

         assert abs(result_sum.item() - 2_0_2.0_2_9_6       ) < 1e-2
         assert abs(result_mean.item() - 0.2_631       ) < 1e-3





      def        _lowercase				(		self						:					Any       )    ->   Dict:






         """simple docstring"""







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

         UpperCAmelCase							=     [1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=__a       )

         UpperCAmelCase							=     scheduler.timesteps

         for i, timestep in enumerate(__a       ):
            if i == len(__a       ) - 1:
               UpperCAmelCase							=     -1
            else:
               UpperCAmelCase							=     timesteps[i + 1]

            UpperCAmelCase							=     scheduler.previous_timestep(__a       )
            UpperCAmelCase							=     prev_t.item()

            self.assertEqual(__a					,       __a       )





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






         """simple docstring"""







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

         UpperCAmelCase							=     [1_0_0, 8_7, 5_0, 5_1, 0]

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





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






         """simple docstring"""







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

         UpperCAmelCase							=     [1_0_0, 8_7, 5_0, 1, 0]
         UpperCAmelCase							=     len(__a       )

         with self.assertRaises(__a					,       msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""       ):
            scheduler.set_timesteps(num_inference_steps=__a					,       timesteps=__a       )





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






         """simple docstring"""







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

         UpperCAmelCase							=     [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __a					,       msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""					,       ):
            scheduler.set_timesteps(timesteps=__a       )








__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()



