"""simple docstring"""





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 	UpperCAmelCase_      (       UpperCamelCase__):
      lowerCamelCase__						:       Optional[int]					    =    (DPMSolverSDEScheduler,)
      lowerCamelCase__						:       Optional[int]					    =    1_0


      def 						_UpperCAmelCase			(  self						,     **a				)			->   Dict:
             lowercase__     :						Any         =      {
                 'num_train_timesteps': 1_1_0_0,
                 'beta_start': 0.0_001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'noise_sampler_seed': 0,
             }

             config.update(**lowercase_				)
             return config


      def 						_UpperCAmelCase			(  self				)			->   Dict:
             for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=lowercase_				)


      def 						_UpperCAmelCase			(  self				)			->   str:
             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=lowercase_						,     beta_end=lowercase_				)


      def 						_UpperCAmelCase			(  self				)			->   Optional[int]:
             for schedule in ["linear", "scaled_linear"]:
                    self.check_over_configs(beta_schedule=lowercase_				)


      def 						_UpperCAmelCase			(  self				)			->   List[str]:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=lowercase_				)


      def 						_UpperCAmelCase			(  self				)			->   List[str]:
             lowercase__     :						Union[str, Any]         =      self.scheduler_classes[0]
             lowercase__     :						str         =      self.get_scheduler_config()
             lowercase__     :						Any         =      scheduler_class(**lowercase_				)

             scheduler.set_timesteps(self.num_inference_steps				)

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

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

                    lowercase__     :						Any         =      model(lowercase_						,     lowercase_				)

                    lowercase__     :						Tuple         =      scheduler.step(lowercase_						,     lowercase_						,     lowercase_				)
                    lowercase__     :						Union[str, Any]         =      output.prev_sample

             lowercase__     :						Any         =      torch.sum(torch.abs(lowercase_				)				)
             lowercase__     :						str         =      torch.mean(torch.abs(lowercase_				)				)

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5				) < 1e-2
                    assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1e-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6				) < 1e-2
                    assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1e-3
             else:
                    assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2				) < 1e-2
                    assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


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

             scheduler.set_timesteps(self.num_inference_steps				)

             lowercase__     :						str         =      self.dummy_model()
             lowercase__     :						int         =      self.dummy_sample_deter * scheduler.init_noise_sigma
             lowercase__     :						List[Any]         =      sample.to(lowercase_				)

             for i, t in enumerate(scheduler.timesteps				):
                    lowercase__     :						Any         =      scheduler.scale_model_input(lowercase_						,     lowercase_				)

                    lowercase__     :						Optional[Any]         =      model(lowercase_						,     lowercase_				)

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

             lowercase__     :						int         =      torch.sum(torch.abs(lowercase_				)				)
             lowercase__     :						Union[str, Any]         =      torch.mean(torch.abs(lowercase_				)				)

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3				) < 1e-2
                    assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1e-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3				) < 1e-2
                    assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1e-3
             else:
                    assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5				) < 1e-2
                    assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1e-3


      def 						_UpperCAmelCase			(  self				)			->   List[Any]:
             lowercase__     :						int         =      self.scheduler_classes[0]
             lowercase__     :						List[str]         =      self.get_scheduler_config()
             lowercase__     :						Tuple         =      scheduler_class(**lowercase_				)

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

             lowercase__     :						Any         =      self.dummy_model()
             lowercase__     :						Any         =      self.dummy_sample_deter.to(lowercase_				) * scheduler.init_noise_sigma

             for t in scheduler.timesteps:
                    lowercase__     :						List[Any]         =      scheduler.scale_model_input(lowercase_						,     lowercase_				)

                    lowercase__     :						List[str]         =      model(lowercase_						,     lowercase_				)

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

             lowercase__     :						Tuple         =      torch.sum(torch.abs(lowercase_				)				)
             lowercase__     :						int         =      torch.mean(torch.abs(lowercase_				)				)

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8				) < 1e-2
                    assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1e-3
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2				) < 1e-2
                    assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1e-3
             else:
                    assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2				) < 1e-2
                    assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             lowercase__     :						Dict         =      self.scheduler_classes[0]
             lowercase__     :						Union[str, Any]         =      self.get_scheduler_config()
             lowercase__     :						Optional[int]         =      scheduler_class(**lowercase_						,     use_karras_sigmas=lowercase_				)

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

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

             for t in scheduler.timesteps:
                    lowercase__     :						Dict         =      scheduler.scale_model_input(lowercase_						,     lowercase_				)

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

                    lowercase__     :						Tuple         =      scheduler.step(lowercase_						,     lowercase_						,     lowercase_				)
                    lowercase__     :						str         =      output.prev_sample

             lowercase__     :						Dict         =      torch.sum(torch.abs(lowercase_				)				)
             lowercase__     :						Tuple         =      torch.mean(torch.abs(lowercase_				)				)

             if torch_device in ["mps"]:
                    assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8				) < 1e-2
                    assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
             elif torch_device in ["cuda"]:
                    assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5				) < 1e-2
                    assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
             else:
                    assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2				) < 1e-2
                    assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2








'''simple docstring'''

from string import ascii_uppercase

__lowerCAmelCase				:      Union[str, Any]						      ={str(ord(c) - 55): c for c in ascii_uppercase}





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("int() can't convert non-string with explicit base"			)
    if num < 0:
        raise ValueError("parameter must be positive int"			)
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if base in (0, 1):
        raise ValueError("base must be >= 2"			)
    if base > 36:
        raise ValueError("base must be <= 36"			)
    A__							=     ""
    A__							=     0
    A__							=     0
    while div != 1:
        A__,      A__							=     divmod(_lowerCamelCase		,			_lowerCamelCase			)
        if base >= 11 and 9 < mod < 36:
            A__							=     ALPHABET_VALUES[str(_lowerCamelCase			)]
        else:
            A__							=     str(_lowerCamelCase			)
        new_value += actual_value
        A__							=     num // base
        A__							=     div
        if div == 0:
            return str(new_value[::-1]			)
        elif div == 1:
            new_value += str(_lowerCamelCase			)
            return str(new_value[::-1]			)

    return new_value[::-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for base in range(2, 37):
    for num in range(1000):
      assert int(decimal_to_any(num, base), base) == num, (
          num,
          base,
          decimal_to_any(num, base),
          int(decimal_to_any(num, base), base),
      )



