import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= (DDPMParallelScheduler,)




 def 		__snake_case						(      self :  Dict      ,   **lowercase__ :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = {
            'num_train_timesteps': 1_000,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'variance_type': 'fixed_small',
            'clip_sample': True,
        }

        config.update(**lowercase__)
        return config




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase__)




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      ,   [0.002, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=lowercase__      ,   beta_end=lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowercase__)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=lowercase__)




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=lowercase__)




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        self.check_over_configs(thresholding=lowercase__)
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=lowercase__      ,   prediction_type=lowercase__      ,   sample_max_value=lowercase__      ,   )




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        for t in [0, 500, 999]:
               self.check_over_forward(time_step=lowercase__)




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config()
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00_979)) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config()
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        lowerCAmelCase__					     = len(lowercase__)

        lowerCAmelCase__					     = self.dummy_model()
        lowerCAmelCase__					     = self.dummy_sample_deter
        lowerCAmelCase__					     = self.dummy_sample_deter + 0.1
        lowerCAmelCase__					     = self.dummy_sample_deter - 0.1

        lowerCAmelCase__					     = samplea.shape[0]
        lowerCAmelCase__					     = torch.stack([samplea, samplea, samplea]      ,   dim=0)
        lowerCAmelCase__					     = torch.arange(lowercase__)[0:3, None].repeat(1      ,   lowercase__)

        lowerCAmelCase__					     = model(samples.flatten(0      ,   1)      ,   timesteps.flatten(0      ,   1))
        lowerCAmelCase__					     = scheduler.batch_step_no_noise(lowercase__      ,   timesteps.flatten(0      ,   1)      ,   samples.flatten(0      ,   1))

        lowerCAmelCase__					     = torch.sum(torch.abs(lowercase__))
        lowerCAmelCase__					     = torch.mean(torch.abs(lowercase__))

        assert abs(result_sum.item() - 1_153.1_833) < 1e-2
        assert abs(result_mean.item() - 0.5_005) < 1e-3




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config()
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        lowerCAmelCase__					     = len(lowercase__)

        lowerCAmelCase__					     = self.dummy_model()
        lowerCAmelCase__					     = self.dummy_sample_deter
        lowerCAmelCase__					     = torch.manual_seed(0)

        for t in reversed(range(lowercase__)):
               # 1. predict noise residual
               lowerCAmelCase__					     = model(lowercase__      ,   lowercase__)

               # 2. predict previous mean of sample x_t-1
               lowerCAmelCase__					     = scheduler.step(lowercase__      ,   lowercase__      ,   lowercase__      ,   generator=lowercase__).prev_sample

               lowerCAmelCase__					     = pred_prev_sample

        lowerCAmelCase__					     = torch.sum(torch.abs(lowercase__))
        lowerCAmelCase__					     = torch.mean(torch.abs(lowercase__))

        assert abs(result_sum.item() - 258.9_606) < 1e-2
        assert abs(result_mean.item() - 0.3_372) < 1e-3




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config(prediction_type='v_prediction')
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        lowerCAmelCase__					     = len(lowercase__)

        lowerCAmelCase__					     = self.dummy_model()
        lowerCAmelCase__					     = self.dummy_sample_deter
        lowerCAmelCase__					     = torch.manual_seed(0)

        for t in reversed(range(lowercase__)):
               # 1. predict noise residual
               lowerCAmelCase__					     = model(lowercase__      ,   lowercase__)

               # 2. predict previous mean of sample x_t-1
               lowerCAmelCase__					     = scheduler.step(lowercase__      ,   lowercase__      ,   lowercase__      ,   generator=lowercase__).prev_sample

               lowerCAmelCase__					     = pred_prev_sample

        lowerCAmelCase__					     = torch.sum(torch.abs(lowercase__))
        lowerCAmelCase__					     = torch.mean(torch.abs(lowercase__))

        assert abs(result_sum.item() - 202.0_296) < 1e-2
        assert abs(result_mean.item() - 0.2_631) < 1e-3




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config()
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        lowerCAmelCase__					     = [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=lowercase__)

        lowerCAmelCase__					     = scheduler.timesteps

        for i, timestep in enumerate(lowercase__):
               if i == len(lowercase__) - 1:
                      lowerCAmelCase__					     = -1
               else:
                      lowerCAmelCase__					     = timesteps[i + 1]

               lowerCAmelCase__					     = scheduler.previous_timestep(lowercase__)
               lowerCAmelCase__					     = prev_t.item()

               self.assertEqual(lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config()
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        lowerCAmelCase__					     = [100, 87, 50, 51, 0]

        with self.assertRaises(lowercase__      ,   msg='`custom_timesteps` must be in descending order.'):
               scheduler.set_timesteps(timesteps=lowercase__)




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config()
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        lowerCAmelCase__					     = [100, 87, 50, 1, 0]
        lowerCAmelCase__					     = len(lowercase__)

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




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config()
        lowerCAmelCase__					     = scheduler_class(**lowercase__)

        lowerCAmelCase__					     = [scheduler.config.num_train_timesteps]

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

def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       for i in range(lowerCAmelCase__     ):
              for j in range(i + 1     ,  lowerCAmelCase__     ):
                     if numbers[j] < numbers[i]:
                            lowerCAmelCase__     , lowerCAmelCase__					     = numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    lowerCAmelCase__     						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__     						=			[int(item) for item in user_input.split(',')]
    print(exchange_sort(unsorted))
