


import math




class    A_   :







 """simple docstring"""





 def __init__(			self					: Union[str, Any]       ,    _a					: Optional[Any]       ,    _a					: Any       ,    _a					: Dict				)    ->			List[str]:
      UpperCAmelCase						:				int		  =			x
      UpperCAmelCase						:				Dict		  =			y
      UpperCAmelCase						:				Optional[int]		  =			z

 def __repr__(			self					: List[Any]				)    ->			str:
      return f'Point({self.x}, {self.y}, {self.z})'







def   A   (		A__		,		A__ ):
     return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2 ) )


if __name__ == "__main__":
      import doctest

      doctest.testmod()







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: Any      =					(EulerAncestralDiscreteScheduler,)
 _UpperCamelCase				: Union[str, Any]      =					10
 def   _UpperCamelCase    (			self					: int       ,    **_a					: str				)    ->			Dict:
      UpperCAmelCase						:				List[Any]		  =			{
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**_a				)
      return config
 def   _UpperCamelCase    (			self					: str				)    ->			Union[str, Any]:
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=_a				)
 def   _UpperCamelCase    (			self					: Tuple				)    ->			int:
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,    [0.0_0_0_2, 0.0_0_2, 0.0_2]				):
           self.check_over_configs(beta_start=_a       ,    beta_end=_a				)
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			List[str]:
      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=_a				)
 def   _UpperCamelCase    (			self					: Dict				)    ->			str:
      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=_a				)
 def   _UpperCamelCase    (			self					: Any				)    ->			Optional[Any]:
      UpperCAmelCase						:				int		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				Optional[int]		  =			self.get_scheduler_config()
      UpperCAmelCase						:				List[Any]		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps				)

      UpperCAmelCase						:				Dict		  =			torch.manual_seed(0				)

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

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

           UpperCAmelCase						:				str		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				int		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				Optional[int]		  =			output.prev_sample

      UpperCAmelCase						:				Tuple		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				str		  =			torch.mean(torch.abs(_a				)				)

      assert abs(result_sum.item() - 1_5_2.3_1_9_2				) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3				) < 1E-3
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			Tuple:
      UpperCAmelCase						:				Optional[int]		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				Tuple		  =			self.get_scheduler_config(prediction_type="v_prediction"				)
      UpperCAmelCase						:				str		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps				)

      UpperCAmelCase						:				Dict		  =			torch.manual_seed(0				)

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

      for i, t in enumerate(scheduler.timesteps				):
           UpperCAmelCase						:				Union[str, Any]		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				Union[str, Any]		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				Union[str, Any]		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				Optional[int]		  =			output.prev_sample

      UpperCAmelCase						:				Dict		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				Any		  =			torch.mean(torch.abs(_a				)				)

      assert abs(result_sum.item() - 1_0_8.4_4_3_9				) < 1E-2
      assert abs(result_mean.item() - 0.1_4_1_2				) < 1E-3

 def   _UpperCamelCase    (			self					: List[str]				)    ->			Optional[Any]:
      UpperCAmelCase						:				str		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				str		  =			self.get_scheduler_config()
      UpperCAmelCase						:				Union[str, Any]		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps       ,    device=_a				)
      UpperCAmelCase						:				str		  =			torch.manual_seed(0				)

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

      for t in scheduler.timesteps:
           UpperCAmelCase						:				Tuple		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				int		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				Tuple		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				str		  =			output.prev_sample

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

      assert abs(result_sum.item() - 1_5_2.3_1_9_2				) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3				) < 1E-3



