"""simple docstring"""






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Dict				    =      (CMStochasticIterativeScheduler,)
      snake_case__     :  Tuple				    =      10

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      {
                """num_train_timesteps""": 201,
                """sigma_min""": 0.0_0_2,
                """sigma_max""": 8_0.0,
            }

            config.update(**__lowerCAmelCase		)
            return config

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      10

            lowercase								=      self.get_scheduler_config()
            lowercase								=      self.scheduler_classes[0](**__lowerCAmelCase		)

            scheduler.set_timesteps(__lowerCAmelCase		)

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

            lowercase								=      self.dummy_sample
            lowercase								=      0.1 * sample

            lowercase								=      scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample
            lowercase								=      scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

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

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



            for clip_denoised in [True, False]:
                  self.check_over_configs(clip_denoised=__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.scheduler_classes[0]
            lowercase								=      self.get_scheduler_config()
            lowercase								=      scheduler_class(**__lowerCAmelCase		)

            lowercase								=      1
            scheduler.set_timesteps(__lowerCAmelCase		)
            lowercase								=      scheduler.timesteps

            lowercase								=      torch.manual_seed(0		)

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

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

                  # 2. predict noise residual
                  lowercase								=      model(__lowerCAmelCase						,			__lowerCAmelCase		)

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

                  lowercase								=      pred_prev_sample

            lowercase								=      torch.sum(torch.abs(__lowerCAmelCase		)		)
            lowercase								=      torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_sum.item() - 1_9_2.7_6_1_4		) < 1E-2
            assert abs(result_mean.item() - 0.2_5_1_0		) < 1E-3

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.scheduler_classes[0]
            lowercase								=      self.get_scheduler_config()
            lowercase								=      scheduler_class(**__lowerCAmelCase		)

            lowercase								=      [106, 0]
            scheduler.set_timesteps(timesteps=__lowerCAmelCase		)
            lowercase								=      scheduler.timesteps

            lowercase								=      torch.manual_seed(0		)

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

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

                  # 2. predict noise residual
                  lowercase								=      model(__lowerCAmelCase						,			__lowerCAmelCase		)

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

                  lowercase								=      pred_prev_sample

            lowercase								=      torch.sum(torch.abs(__lowerCAmelCase		)		)
            lowercase								=      torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_sum.item() - 3_4_7.6_3_5_7		) < 1E-2
            assert abs(result_mean.item() - 0.4_5_2_7		) < 1E-3

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.scheduler_classes[0]
            lowercase								=      self.get_scheduler_config()
            lowercase								=      scheduler_class(**__lowerCAmelCase		)

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

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

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.scheduler_classes[0]
            lowercase								=      self.get_scheduler_config()
            lowercase								=      scheduler_class(**__lowerCAmelCase		)

            lowercase								=      [39, 30, 12, 1, 0]
            lowercase								=      len(__lowerCAmelCase		)

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







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.scheduler_classes[0]
            lowercase								=      self.get_scheduler_config()
            lowercase								=      scheduler_class(**__lowerCAmelCase		)

            lowercase								=      [scheduler.config.num_train_timesteps]

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




"""simple docstring"""






import math


def      UpperCAmelCase__			(							lowerCAmelCase__	:int						)					->       bool:
      '''simple docstring'''


      lowercase								=      math.loga(math.sqrt(4 * positive_integer + 1						) / 2 + 1 / 2						)

      return exponent == int(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:float = 1 / 1_2_3_4_5						)					->       int:
      '''simple docstring'''


      lowercase								=      0
      lowercase								=      0

      lowercase								=      3
      while True:
            lowercase								=      (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(lowerCAmelCase__						):
                  lowercase								=      int(lowerCAmelCase__						)
                  total_partitions += 1
                  if check_partition_perfect(lowerCAmelCase__						):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(lowerCAmelCase__						)
            integer += 1


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



