


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   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       (DPMSolverSDEScheduler,)
      UpperCamelCase								=       10





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






         """simple docstring"""







         UpperCAmelCase							=     {
             """num_train_timesteps""": 1_1_0_0,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """noise_sampler_seed""": 0,
         }

         config.update(**__lowerCamelCase       )
         return config





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






         """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       )    ->   Any:






         """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						:					str       )    ->   str:






         """simple docstring"""







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





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






         """simple docstring"""







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





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






         """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_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        _lowercase				(		self						:					Dict       )    ->   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_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        _lowercase				(		self						:					Tuple       )    ->   int:






         """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_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        _lowercase				(		self						:					Any       )    ->   str:






         """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_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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->int:
   return int((input_a, input_a).count(0   ) == 0   )
def   _UpperCamelCase				(						)						->None:
   assert and_gate(0  ,						0   ) == 0
   assert and_gate(0  ,						1   ) == 0
   assert and_gate(1  ,						0   ) == 0
   assert and_gate(1  ,						1   ) == 1


if __name__ == "__main__":
 test_and_gate()
 print(and_gate(1, 0))
 print(and_gate(0, 0))
 print(and_gate(0, 1))
 print(and_gate(1, 1))



