"""simple docstring"""






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Optional[Any]							=				(HeunDiscreteScheduler,)
    _UpperCAmelCase			:Any							=				10






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       lowercase__:     Any      =   {
           '''num_train_timesteps''': 1100,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_UpperCAmelCase							)
       return config






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






    def _snake_case    (     self							):
       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=_UpperCAmelCase					,    beta_end=_UpperCAmelCase							)






    def _snake_case    (     self							):
       for schedule in ["linear", "scaled_linear", "exp"]:
          self.check_over_configs(beta_schedule=_UpperCAmelCase							)






    def _snake_case    (     self							):
       for clip_sample_range in [1.0, 2.0, 3.0]:
          self.check_over_configs(clip_sample_range=_UpperCAmelCase					,    clip_sample=_UpperCAmelCase							)






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






    def _snake_case    (     self							):
       lowercase__:     str      =   self.scheduler_classes[0]
       lowercase__:     Tuple      =   self.get_scheduler_config()
       lowercase__:     Optional[Any]      =   scheduler_class(**_UpperCAmelCase							)

       scheduler.set_timesteps(self.num_inference_steps							)

       lowercase__:     Optional[int]      =   self.dummy_model()
       lowercase__:     Optional[Any]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase__:     Optional[Any]      =   sample.to(_UpperCAmelCase							)

       for i, t in enumerate(scheduler.timesteps							):
          lowercase__:     Dict      =   scheduler.scale_model_input(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     str      =   model(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     List[Any]      =   scheduler.step(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)
          lowercase__:     Tuple      =   output.prev_sample

       lowercase__:     int      =   torch.sum(torch.abs(_UpperCAmelCase							)							)
       lowercase__:     Any      =   torch.mean(torch.abs(_UpperCAmelCase							)							)

       if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 0.1_233							) < 1e-2
          assert abs(result_mean.item() - 0.0_002							) < 1e-3
       else:
          # CUDA
          assert abs(result_sum.item() - 0.1_233							) < 1e-2
          assert abs(result_mean.item() - 0.0_002							) < 1e-3






    def _snake_case    (     self							):
       lowercase__:     List[str]      =   self.scheduler_classes[0]
       lowercase__:     Union[str, Any]      =   self.get_scheduler_config(prediction_type='''v_prediction'''							)
       lowercase__:     Tuple      =   scheduler_class(**_UpperCAmelCase							)

       scheduler.set_timesteps(self.num_inference_steps							)

       lowercase__:     Optional[int]      =   self.dummy_model()
       lowercase__:     Union[str, Any]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase__:     Tuple      =   sample.to(_UpperCAmelCase							)

       for i, t in enumerate(scheduler.timesteps							):
          lowercase__:     Optional[int]      =   scheduler.scale_model_input(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     int      =   model(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     Optional[Any]      =   scheduler.step(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)
          lowercase__:     Optional[Any]      =   output.prev_sample

       lowercase__:     Any      =   torch.sum(torch.abs(_UpperCAmelCase							)							)
       lowercase__:     List[str]      =   torch.mean(torch.abs(_UpperCAmelCase							)							)

       if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_9_3_4e-0_7							) < 1e-2
          assert abs(result_mean.item() - 6.1_1_1_2e-1_0							) < 1e-3
       else:
          # CUDA
          assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7							) < 1e-2
          assert abs(result_mean.item() - 0.0_002							) < 1e-3






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.scheduler_classes[0]
       lowercase__:     Any      =   self.get_scheduler_config()
       lowercase__:     List[str]      =   scheduler_class(**_UpperCAmelCase							)

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

       lowercase__:     int      =   self.dummy_model()
       lowercase__:     Union[str, Any]      =   self.dummy_sample_deter.to(_UpperCAmelCase							) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
          lowercase__:     Tuple      =   scheduler.scale_model_input(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     Optional[int]      =   model(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     Optional[int]      =   scheduler.step(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)
          lowercase__:     int      =   output.prev_sample

       lowercase__:     Optional[Any]      =   torch.sum(torch.abs(_UpperCAmelCase							)							)
       lowercase__:     Any      =   torch.mean(torch.abs(_UpperCAmelCase							)							)

       if str(_UpperCAmelCase							).startswith('''cpu'''							):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 0.1_233							) < 1e-2
          assert abs(result_mean.item() - 0.0_002							) < 1e-3
       elif str(_UpperCAmelCase							).startswith('''mps'''							):
          # Larger tolerance on mps
          assert abs(result_mean.item() - 0.0_002							) < 1e-2
       else:
          # CUDA
          assert abs(result_sum.item() - 0.1_233							) < 1e-2
          assert abs(result_mean.item() - 0.0_002							) < 1e-3






    def _snake_case    (     self							):
       lowercase__:     str      =   self.scheduler_classes[0]
       lowercase__:     Optional[int]      =   self.get_scheduler_config()
       lowercase__:     Any      =   scheduler_class(**_UpperCAmelCase					,    use_karras_sigmas=_UpperCAmelCase							)

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

       lowercase__:     Any      =   self.dummy_model()
       lowercase__:     str      =   self.dummy_sample_deter.to(_UpperCAmelCase							) * scheduler.init_noise_sigma
       lowercase__:     Optional[Any]      =   sample.to(_UpperCAmelCase							)

       for t in scheduler.timesteps:
          lowercase__:     Any      =   scheduler.scale_model_input(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     str      =   model(_UpperCAmelCase					,    _UpperCAmelCase							)

          lowercase__:     List[str]      =   scheduler.step(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							)
          lowercase__:     List[Any]      =   output.prev_sample

       lowercase__:     int      =   torch.sum(torch.abs(_UpperCAmelCase							)							)
       lowercase__:     List[Any]      =   torch.mean(torch.abs(_UpperCAmelCase							)							)

       assert abs(result_sum.item() - 0.00_015							) < 1e-2
       assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5e-0_7							) < 1e-2

"""simple docstring"""






def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Optional[int]:
   lowercase__:     str      =   [7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
   for gap in gaps:
      for i in range(__UpperCAmelCase ,					len(__UpperCAmelCase				)				):
         lowercase__:     Any      =   collection[i]
         lowercase__:     Tuple      =   i
         while j >= gap and collection[j - gap] > insert_value:
            lowercase__:     List[str]      =   collection[j - gap]
            j -= gap
         if j != i:
            lowercase__:     List[str]      =   insert_value
   return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       __A         =				input("Enter numbers separated by a comma:\n").strip()
       __A         =				[int(item) for item in user_input.split(",")]
       print(shell_sort(unsorted))
