'''simple docstring'''






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   (DDIMParallelScheduler,)
   __lowerCAmelCase         =   (("eta", 0.0), ("num_inference_steps", 5_0))
   def 	__UpperCAmelCase   (      self      :			Any, **UpperCamelCase__      :			Union[str, Any]     )  ->			Tuple:
        _A    		=  {
            'num_train_timesteps': 10_00,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'clip_sample': True,
        }

        config.update(**UpperCamelCase__     )
        return config
   def 	__UpperCAmelCase   (      self      :			int, **UpperCamelCase__      :			Dict     )  ->			int:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config(**UpperCamelCase__     )
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A		,       _A    		=  10, 0.0

        _A    		=  self.dummy_model()
        _A    		=  self.dummy_sample_deter

        scheduler.set_timesteps(UpperCamelCase__     )

        for t in scheduler.timesteps:
             _A    		=  model(UpperCamelCase__, UpperCamelCase__     )
             _A    		=  scheduler.step(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     ).prev_sample

        return sample
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			List[Any]:
        for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=UpperCamelCase__     )

        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config(steps_offset=1     )
        _A    		=  scheduler_class(**UpperCamelCase__     )
        scheduler.set_timesteps(5     )
        assert torch.equal(scheduler.timesteps, torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]     )     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[Any]:
        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=UpperCamelCase__, beta_end=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[str]:
        for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        self.check_over_configs(thresholding=UpperCamelCase__     )
        for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=UpperCamelCase__, prediction_type=UpperCamelCase__, sample_max_value=UpperCamelCase__, )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Union[str, Any]:
        for t in [1, 10, 49]:
             self.check_over_forward(time_step=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			str:
        for t, num_inference_steps in zip([1, 10, 50], [10, 50, 5_00]     ):
             self.check_over_forward(time_step=UpperCamelCase__, num_inference_steps=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]     ):
             self.check_over_forward(time_step=UpperCamelCase__, eta=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[Any]:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        assert torch.sum(torch.abs(scheduler._get_variance(0, 0     ) - 0.0     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_20, 4_00     ) - 0.14_771     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_80, 9_60     ) - 0.32_460     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0, 0     ) - 0.0     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_87, 4_86     ) - 0.00_979     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_99, 9_98     ) - 0.02     )     ) < 1e-5
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        _A    		=  self.scheduler_classes[0]
        _A    		=  self.get_scheduler_config()
        _A    		=  scheduler_class(**UpperCamelCase__     )

        _A		,       _A    		=  10, 0.0
        scheduler.set_timesteps(UpperCamelCase__     )

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

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

        _A    		=  model(samples.flatten(0, 1     ), timesteps.flatten(0, 1     )     )
        _A    		=  scheduler.batch_step_no_noise(UpperCamelCase__, timesteps.flatten(0, 1     ), samples.flatten(0, 1     ), UpperCamelCase__     )

        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 1_147.7_904     ) < 1e-2
        assert abs(result_mean.item() - 0.4_982     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[str]:
        _A    		=  self.full_loop()

        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 172.0_067     ) < 1e-2
        assert abs(result_mean.item() - 0.223_967     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        _A    		=  self.full_loop(prediction_type='v_prediction'     )

        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 52.5_302     ) < 1e-2
        assert abs(result_mean.item() - 0.0_684     ) < 1e-3
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[Any]:
        # We specify different beta, so that the first alpha is 0.99
        _A    		=  self.full_loop(set_alpha_to_one=UpperCamelCase__, beta_start=0.01     )
        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 149.8_295     ) < 1e-2
        assert abs(result_mean.item() - 0.1_951     ) < 1e-3

   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[str]:
        # We specify different beta, so that the first alpha is 0.99
        _A    		=  self.full_loop(set_alpha_to_one=UpperCamelCase__, beta_start=0.01     )
        _A    		=  torch.sum(torch.abs(UpperCamelCase__     )     )
        _A    		=  torch.mean(torch.abs(UpperCamelCase__     )     )

        assert abs(result_sum.item() - 149.0_784     ) < 1e-2
        assert abs(result_mean.item() - 0.1_941     ) < 1e-3







def  a   (lowerCAmelCase__   ):
   __a					=  False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      __a					=  True
      for i in range(0		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False

      for i in range(1		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False
   return input_list


if __name__ == "__main__":
  print('Enter list to be sorted')
  SCREAMING_SNAKE_CASE							    =							[int(x) for x in input().split()]
  # inputing elements of the list in one line
  SCREAMING_SNAKE_CASE							    =							odd_even_sort(input_list)
  print('The sorted list is')
  print(sorted_list)



