





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class      A       (	__lowercase						):
      _snake_case							     =(DPMSolverSDEScheduler,)
      _snake_case							     =10



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  **_lowerCAmelCase:						str       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1100,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "noise_sampler_seed": 0,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						Any       )						->    Tuple:
         '''simple docstring'''

         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[Any]:
         '''simple docstring'''

         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]		,  [0.00_02, 0.0_02, 0.02]       ):
            self.check_over_configs(beta_start=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Union[str, Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    str:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         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       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 167.47_8210_4492_1875       ) < 1e-2
            assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77       ) < 1e-3
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 171.59_3521_1181_6406       ) < 1e-2
            assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52       ) < 1e-3
         else:
            assert abs(result_sum.item() - 162.52_3834_2285_1562       ) < 1e-2
            assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26       ) < 1e-3



      def 	lowerCAmelCase__    (	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_											=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       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 124.77_1492_0043_9453       ) < 1e-2
            assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84       ) < 1e-3
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 128.1_6633_6059_5703       ) < 1e-2
            assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97       ) < 1e-3
         else:
            assert abs(result_sum.item() - 119.8_4875_4882_8125       ) < 1e-2
            assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    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		,  device=_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter.to(_lowerCAmelCase       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
            UpperCAmelCase_											=scheduler.scale_model_input(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 167.46_9573_9746_0938       ) < 1e-2
            assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35       ) < 1e-3
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 171.59_3536_3769_5312       ) < 1e-2
            assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71       ) < 1e-3
         else:
            assert abs(result_sum.item() - 162.52_3834_2285_1562       ) < 1e-2
            assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26       ) < 1e-3






      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Any:
         '''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_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter.to(_lowerCAmelCase       ) * scheduler.init_noise_sigma
         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       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 176.66_9741_3574_2188       ) < 1e-2
            assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1e-2
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 177.63_6535_6445_3125       ) < 1e-2
            assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1e-2
         else:
            assert abs(result_sum.item() - 170.3_1352_2338_8672       ) < 1e-2
            assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1e-2











__lowercase  :   Optional[Any]						      =[
    (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     a__ (					lowercase__  ):






   '''simple docstring'''






   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(lowercase__  ):
      if (place + 1 < len(lowercase__  )) 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     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   for arabic, roman in ROMAN:
      ((UpperCAmelCase_)						,       (UpperCAmelCase_))											=divmod(lowercase__  ,    lowercase__  )
      result.append(roman * factor  )
      if number == 0:
         break
   return "".join(lowercase__  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




