
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class       _lowerCAmelCase  (		a_							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						=(EulerDiscreteScheduler,)
 SCREAMING_SNAKE_CASE_				:					int       						=10

 def      __lowerCAmelCase  (						self			:			int					, **SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'num_train_timesteps': 11_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**_SCREAMING_SNAKE_CASE   )
      return config

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					, [0.0002, 0.002, 0.02]   ):
           self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE					, beta_end=_SCREAMING_SNAKE_CASE   )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



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

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






      """simple docstring"""



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

      scheduler.set_timesteps(self.num_inference_steps   )

      UpperCamelCase						    =					torch.manual_seed(0   )

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

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

           UpperCamelCase						    =					model(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE   )

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

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

      assert abs(result_sum.item() - 10.0807   ) < 1e-2
      assert abs(result_mean.item() - 0.0131   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



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

      scheduler.set_timesteps(self.num_inference_steps   )

      UpperCamelCase						    =					torch.manual_seed(0   )

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

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

           UpperCamelCase						    =					model(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE   )

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

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

      assert abs(result_sum.item() - 0.0002   ) < 1e-2
      assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



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

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

      UpperCamelCase						    =					torch.manual_seed(0   )

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

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

           UpperCamelCase						    =					model(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE   )

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

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

      assert abs(result_sum.item() - 10.0807   ) < 1e-2
      assert abs(result_mean.item() - 0.0131   ) < 1e-3






 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



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

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

      UpperCamelCase						    =					torch.manual_seed(0   )

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

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

           UpperCamelCase						    =					model(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE   )

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

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

      assert abs(result_sum.item() - 124.52299499511719   ) < 1e-2
      assert abs(result_mean.item() - 0.16213932633399963   ) < 1e-3





'''simple docstring'''



import operator


def       snake_case   (	snake_case      : list					,		snake_case      : bool = False					,		snake_case      : list | None = None			) ->  list:


    """simple docstring"""


    lowerCAmelCase 						=							operator.lt if reverse else operator.gt
    lowerCAmelCase 						=							solution or []

    if not arr:
        return solution

    lowerCAmelCase 						=							[arr.pop(0			)]
    for i, item in enumerate(snake_case			):
        if _operator(snake_case					,		sublist[-1]			):
            sublist.append(snake_case			)
            arr.pop(snake_case			)

    #  merging sublist into solution list
    if not solution:
        solution.extend(snake_case			)
    else:
        while sublist:
            lowerCAmelCase 						=							sublist.pop(0			)
            for i, xx in enumerate(snake_case			):
                if not _operator(snake_case					,		snake_case			):
                    solution.insert(snake_case					,		snake_case			)
                    break
            else:
                solution.append(snake_case			)

    strand_sort(snake_case					,		snake_case					,		snake_case			)
    return solution


if __name__ == "__main__":
     assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
     assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]





