

"""simple docstring"""




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class 		_A       (			__a					):
      __a								=	(CMStochasticIterativeScheduler,)
      __a								=	10





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			{
             "num_train_timesteps": 201,
             "sigma_min": 0.0_02,
             "sigma_max": 80.0,
         }

         config.update(**SCREAMING_SNAKE_CASE__		)
         return config





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			10

         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			self.scheduler_classes[0](**SCREAMING_SNAKE_CASE__		)

         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			scheduler.timesteps[0]
         lowerCamelCase__						=			scheduler.timesteps[1]

         lowerCamelCase__						=			self.dummy_sample
         lowerCamelCase__						=			0.1 * sample

         lowerCamelCase__						=			scheduler.step(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		).prev_sample
         lowerCamelCase__						=			scheduler.step(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		).prev_sample

         self.assertEqual(output_a.shape    ,						sample.shape		)
         self.assertEqual(output_a.shape    ,						output_a.shape		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         for clip_denoised in [True, False]:
            self.check_over_configs(clip_denoised=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			1
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			scheduler.timesteps

         lowerCamelCase__						=			torch.manual_seed(0		)

         lowerCamelCase__						=			self.dummy_model()
         lowerCamelCase__						=			self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(SCREAMING_SNAKE_CASE__		):
            # 1. scale model input
            lowerCamelCase__						=			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # 2. predict noise residual
            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # 3. predict previous sample x_t-1
            lowerCamelCase__						=			scheduler.step(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__		).prev_sample

            lowerCamelCase__						=			pred_prev_sample

         lowerCamelCase__						=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 1_92.76_14		) < 1e-2
         assert abs(result_mean.item() - 0.25_10		) < 1e-3





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			[106, 0]
         scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			scheduler.timesteps

         lowerCamelCase__						=			torch.manual_seed(0		)

         lowerCamelCase__						=			self.dummy_model()
         lowerCamelCase__						=			self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
            # 1. scale model input
            lowerCamelCase__						=			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # 2. predict noise residual
            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

            # 3. predict previous sample x_t-1
            lowerCamelCase__						=			scheduler.step(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__		).prev_sample

            lowerCamelCase__						=			pred_prev_sample

         lowerCamelCase__						=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 3_47.63_57		) < 1e-2
         assert abs(result_mean.item() - 0.45_27		) < 1e-3





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			[39, 30, 12, 15, 0]

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





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			[39, 30, 12, 1, 0]
         lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)

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





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			[scheduler.config.num_train_timesteps]

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







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if length <= 0 or not isinstance(A__	,    A__   ):
      raise ValueError("Length must be a positive integer."   )
   return [n * (2 * n - 1) for n in range(A__   )]


if __name__ == "__main__":
     print(hexagonal_numbers(length=5))
     print(hexagonal_numbers(length=10))




