






"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  __lowercase	):
    __magic_name__   					=					(KDPMaDiscreteScheduler,)
    __magic_name__   					=					10





    def     lowerCamelCase__				(							self     : int    ,		**A     : List[str]      ):


      '''simple docstring'''




      a					:      Any    =       {
          '''num_train_timesteps''': 1_1_0_0,
          '''beta_start''': 0.00_01,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
      }

      config.update(**_a      )
      return config





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=_a      )





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]    ,		[0.00_02, 0.0_02, 0.02]      ):
        self.check_over_configs(beta_start=_a    ,		beta_end=_a      )





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      int    =       self.scheduler_classes[0]
      a					:      Dict    =       self.get_scheduler_config(prediction_type='v_prediction'      )
      a					:      List[str]    =       scheduler_class(**_a      )

      scheduler.set_timesteps(self.num_inference_steps      )

      a					:      Optional[Any]    =       self.dummy_model()
      a					:      str    =       self.dummy_sample_deter * scheduler.init_noise_sigma
      a					:      Dict    =       sample.to(_a      )

      for i, t in enumerate(scheduler.timesteps      ):
        a					:      Tuple    =       scheduler.scale_model_input(_a    ,		_a      )

        a					:      Dict    =       model(_a    ,		_a      )

        a					:      str    =       scheduler.step(_a    ,		_a    ,		_a      )
        a					:      Dict    =       output.prev_sample

      a					:      Optional[Any]    =       torch.sum(torch.abs(_a      )      )
      a					:      Any    =       torch.mean(torch.abs(_a      )      )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 4.6934E-07      ) < 1E-2
        assert abs(result_mean.item() - 6.1112E-10      ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 4.693_4286_5017_0972E-07      ) < 1E-2
        assert abs(result_mean.item() - 0.00_02      ) < 1E-3





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      if torch_device == "mps":
        return
      a					:      Optional[int]    =       self.scheduler_classes[0]
      a					:      int    =       self.get_scheduler_config()
      a					:      Optional[int]    =       scheduler_class(**_a      )

      scheduler.set_timesteps(self.num_inference_steps      )

      a					:      Any    =       self.dummy_model()
      a					:      Any    =       self.dummy_sample_deter * scheduler.init_noise_sigma
      a					:      Optional[Any]    =       sample.to(_a      )

      for i, t in enumerate(scheduler.timesteps      ):
        a					:      Optional[Any]    =       scheduler.scale_model_input(_a    ,		_a      )

        a					:      List[Any]    =       model(_a    ,		_a      )

        a					:      Optional[Any]    =       scheduler.step(_a    ,		_a    ,		_a      )
        a					:      Optional[Any]    =       output.prev_sample

      a					:      Dict    =       torch.sum(torch.abs(_a      )      )
      a					:      Tuple    =       torch.mean(torch.abs(_a      )      )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 20.41_25      ) < 1E-2
        assert abs(result_mean.item() - 0.02_66      ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 20.41_25      ) < 1E-2
        assert abs(result_mean.item() - 0.02_66      ) < 1E-3





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      if torch_device == "mps":
        return
      a					:      List[str]    =       self.scheduler_classes[0]
      a					:      str    =       self.get_scheduler_config()
      a					:      List[Any]    =       scheduler_class(**_a      )

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

      a					:      Union[str, Any]    =       self.dummy_model()
      a					:      Tuple    =       self.dummy_sample_deter.to(_a      ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
        a					:      str    =       scheduler.scale_model_input(_a    ,		_a      )

        a					:      int    =       model(_a    ,		_a      )

        a					:      int    =       scheduler.step(_a    ,		_a    ,		_a      )
        a					:      Dict    =       output.prev_sample

      a					:      Tuple    =       torch.sum(torch.abs(_a      )      )
      a					:      Union[str, Any]    =       torch.mean(torch.abs(_a      )      )

      if str(_a      ).startswith('cpu'      ):
        # The following sum varies between 148 and 156 on mps. Why?
        assert abs(result_sum.item() - 20.41_25      ) < 1E-2
        assert abs(result_mean.item() - 0.02_66      ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 20.41_25      ) < 1E-2
        assert abs(result_mean.item() - 0.02_66      ) < 1E-3












"""simple docstring"""


def       snake_case  (A_				:int				):







  '''simple docstring'''

  if isinstance(A_     ,      A_				):
    raise TypeError('\'float\' object cannot be interpreted as an integer'				)
  if isinstance(A_     ,      A_				):
    raise TypeError('\'str\' object cannot be interpreted as an integer'				)

  if num == 0:
    return "0b0"

  a					:      List[Any]    =       False

  if num < 0:
    a					:      Optional[int]    =       True
    a					:      Dict    =       -num

  a					:      list[int]    =       []
  while num > 0:
    binary.insert(0     ,      num % 2				)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_				) for e in binary				)

  return "0b" + "".join(str(A_				) for e in binary				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




