


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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  (DPMSolverSDEScheduler,)
 __snake_case												=  1_0



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       snake_case_	=				{
           '''num_train_timesteps''': 11_00,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''noise_sampler_seed''': 0,
       }

       config.update(**_UpperCAmelCase )
       return config



 def 						UpperCamelCase__					(     self ):
       for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       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=_UpperCAmelCase		,				beta_end=_UpperCAmelCase )



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



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.scheduler_classes[0]
       snake_case_	=				self.get_scheduler_config()
       snake_case_	=				scheduler_class(**_UpperCAmelCase )

       scheduler.set_timesteps(self.num_inference_steps )

       snake_case_	=				self.dummy_model()
       snake_case_	=				self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case_	=				sample.to(_UpperCAmelCase )

       for i, t in enumerate(scheduler.timesteps ):
             snake_case_	=				scheduler.scale_model_input(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				model(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
             snake_case_	=				output.prev_sample

       snake_case_	=				torch.sum(torch.abs(_UpperCAmelCase ) )
       snake_case_	=				torch.mean(torch.abs(_UpperCAmelCase ) )

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 167.47_821_044_921_875 ) < 1E-2
             assert abs(result_mean.item() - 0.2_178_705_964_565_277 ) < 1E-3
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 171.59_352_111_816_406 ) < 1E-2
             assert abs(result_mean.item() - 0.22_342_906_892_299_652 ) < 1E-3
       else:
             assert abs(result_sum.item() - 162.52_383_422_851_562 ) < 1E-2
             assert abs(result_mean.item() - 0.211_619_570_851_326 ) < 1E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.scheduler_classes[0]
       snake_case_	=				self.get_scheduler_config(prediction_type='''v_prediction''' )
       snake_case_	=				scheduler_class(**_UpperCAmelCase )

       scheduler.set_timesteps(self.num_inference_steps )

       snake_case_	=				self.dummy_model()
       snake_case_	=				self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case_	=				sample.to(_UpperCAmelCase )

       for i, t in enumerate(scheduler.timesteps ):
             snake_case_	=				scheduler.scale_model_input(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				model(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
             snake_case_	=				output.prev_sample

       snake_case_	=				torch.sum(torch.abs(_UpperCAmelCase ) )
       snake_case_	=				torch.mean(torch.abs(_UpperCAmelCase ) )

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 124.77_149_200_439_453 ) < 1E-2
             assert abs(result_mean.item() - 0.16_226_289_014_816_284 ) < 1E-3
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 128.1_663_360_595_703 ) < 1E-2
             assert abs(result_mean.item() - 0.16_688_326_001_167_297 ) < 1E-3
       else:
             assert abs(result_sum.item() - 119.8_487_548_828_125 ) < 1E-2
             assert abs(result_mean.item() - 0.1_560_530_662_536_621 ) < 1E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.scheduler_classes[0]
       snake_case_	=				self.get_scheduler_config()
       snake_case_	=				scheduler_class(**_UpperCAmelCase )

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

       snake_case_	=				self.dummy_model()
       snake_case_	=				self.dummy_sample_deter.to(_UpperCAmelCase ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             snake_case_	=				scheduler.scale_model_input(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				model(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
             snake_case_	=				output.prev_sample

       snake_case_	=				torch.sum(torch.abs(_UpperCAmelCase ) )
       snake_case_	=				torch.mean(torch.abs(_UpperCAmelCase ) )

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 167.46_957_397_460_938 ) < 1E-2
             assert abs(result_mean.item() - 0.21_805_934_607_982_635 ) < 1E-3
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 171.59_353_637_695_312 ) < 1E-2
             assert abs(result_mean.item() - 0.22_342_908_382_415_771 ) < 1E-3
       else:
             assert abs(result_sum.item() - 162.52_383_422_851_562 ) < 1E-2
             assert abs(result_mean.item() - 0.211_619_570_851_326 ) < 1E-3





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.scheduler_classes[0]
       snake_case_	=				self.get_scheduler_config()
       snake_case_	=				scheduler_class(**_UpperCAmelCase		,				use_karras_sigmas=_UpperCAmelCase )

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

       snake_case_	=				self.dummy_model()
       snake_case_	=				self.dummy_sample_deter.to(_UpperCAmelCase ) * scheduler.init_noise_sigma
       snake_case_	=				sample.to(_UpperCAmelCase )

       for t in scheduler.timesteps:
             snake_case_	=				scheduler.scale_model_input(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				model(_UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
             snake_case_	=				output.prev_sample

       snake_case_	=				torch.sum(torch.abs(_UpperCAmelCase ) )
       snake_case_	=				torch.mean(torch.abs(_UpperCAmelCase ) )

       if torch_device in ["mps"]:
             assert abs(result_sum.item() - 176.66_974_135_742_188 ) < 1E-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1E-2
       elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 177.63_653_564_453_125 ) < 1E-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1E-2
       else:
             assert abs(result_sum.item() - 170.3_135_223_388_672 ) < 1E-2
             assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1E-2



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return x if y == 0 else greatest_common_divisor(SCREAMING_SNAKE_CASE , x % y )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return (x * y) // greatest_common_divisor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE = 20 )->  int:




      """simple docstring"""

      snake_case_	=				1
      for i in range(1 , n + 1 ):
            snake_case_	=				lcm(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      return g


if __name__ == "__main__":
   print(f'''{solution() = }''')