

'''simple docstring'''



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     (EulerDiscreteScheduler,)
  SCREAMING_SNAKE_CASE			:       int				=     10

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 11_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for timesteps in [10, 50, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''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=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      scheduler.set_timesteps(self.num_inference_steps	)

      lowerCAmelCase 						=							torch.manual_seed(0	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter * scheduler.init_noise_sigma
      lowerCAmelCase 						=							sample.to(_SCREAMING_SNAKE_CASE	)

      for i, t in enumerate(scheduler.timesteps	):
          lowerCAmelCase 						=							scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							output.prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 10.0_807	) < 1e-2
      assert abs(result_mean.item() - 0.0_131	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      scheduler.set_timesteps(self.num_inference_steps	)

      lowerCAmelCase 						=							torch.manual_seed(0	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter * scheduler.init_noise_sigma
      lowerCAmelCase 						=							sample.to(_SCREAMING_SNAKE_CASE	)

      for i, t in enumerate(scheduler.timesteps	):
          lowerCAmelCase 						=							scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							output.prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 0.0_002	) < 1e-2
      assert abs(result_mean.item() - 2.2676e-06	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      scheduler.set_timesteps(self.num_inference_steps      , device=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							torch.manual_seed(0	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      lowerCAmelCase 						=							sample.to(_SCREAMING_SNAKE_CASE	)

      for t in scheduler.timesteps:
          lowerCAmelCase 						=							scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							output.prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 10.0_807	) < 1e-2
      assert abs(result_mean.item() - 0.0_131	) < 1e-3


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE      , use_karras_sigmas=_SCREAMING_SNAKE_CASE	)

      scheduler.set_timesteps(self.num_inference_steps      , device=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							torch.manual_seed(0	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      lowerCAmelCase 						=							sample.to(_SCREAMING_SNAKE_CASE	)

      for t in scheduler.timesteps:
          lowerCAmelCase 						=							scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							output.prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 124.52_299_499_511_719	) < 1e-2
      assert abs(result_mean.item() - 0.16_213_932_633_399_963	) < 1e-3








'''simple docstring'''



def       snake_case   (	snake_case      : list			) ->  list:


    """simple docstring"""


    if any(not isinstance(snake_case					,		snake_case			) or x < 0 for x in sequence			):
        raise TypeError('Sequence must be list of non-negative integers'			)
    for _ in range(len(snake_case			)			):
        for i, (rod_upper, rod_lower) in enumerate(zip(snake_case					,		sequence[1:]			)			):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





