


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class    lowerCamelCase__     ( lowerCAmelCase):
     SCREAMING_SNAKE_CASE__       =    (EulerAncestralDiscreteScheduler,)
     SCREAMING_SNAKE_CASE__       =    10





     def   __A      (self	,  **UpperCAmelCase )  ->     str:
      _lowercase							   ={
          '''num_train_timesteps''': 1_1_0_0,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
      }

      config.update(**UpperCAmelCase )
      return config





     def   __A      (self )  ->     int:
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase )





     def   __A      (self )  ->     str:
      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,  [0.0002, 0.002, 0.02] ):
       self.check_over_configs(beta_start=UpperCAmelCase	,  beta_end=UpperCAmelCase )





     def   __A      (self )  ->     str:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCAmelCase )





     def   __A      (self )  ->     Any:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCAmelCase )





     def   __A      (self )  ->     Optional[int]:
      _lowercase							   =self.scheduler_classes[0]
      _lowercase							   =self.get_scheduler_config()
      _lowercase							   =scheduler_class(**UpperCAmelCase )

      scheduler.set_timesteps(self.num_inference_steps )

      _lowercase							   =torch.manual_seed(0 )

      _lowercase							   =self.dummy_model()
      _lowercase							   =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _lowercase							   =sample.to(UpperCAmelCase )

      for i, t in enumerate(scheduler.timesteps ):
       _lowercase							   =scheduler.scale_model_input(UpperCAmelCase	,  UpperCAmelCase )

       _lowercase							   =model(UpperCAmelCase	,  UpperCAmelCase )

       _lowercase							   =scheduler.step(UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  generator=UpperCAmelCase )
       _lowercase							   =output.prev_sample

      _lowercase							   =torch.sum(torch.abs(UpperCAmelCase ) )
      _lowercase							   =torch.mean(torch.abs(UpperCAmelCase ) )

      assert abs(result_sum.item() - 152.3192 ) < 1e-2
      assert abs(result_mean.item() - 0.1983 ) < 1e-3





     def   __A      (self )  ->     List[Any]:
      _lowercase							   =self.scheduler_classes[0]
      _lowercase							   =self.get_scheduler_config(prediction_type='''v_prediction''' )
      _lowercase							   =scheduler_class(**UpperCAmelCase )

      scheduler.set_timesteps(self.num_inference_steps )

      _lowercase							   =torch.manual_seed(0 )

      _lowercase							   =self.dummy_model()
      _lowercase							   =self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowercase							   =sample.to(UpperCAmelCase )

      for i, t in enumerate(scheduler.timesteps ):
       _lowercase							   =scheduler.scale_model_input(UpperCAmelCase	,  UpperCAmelCase )

       _lowercase							   =model(UpperCAmelCase	,  UpperCAmelCase )

       _lowercase							   =scheduler.step(UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  generator=UpperCAmelCase )
       _lowercase							   =output.prev_sample

      _lowercase							   =torch.sum(torch.abs(UpperCAmelCase ) )
      _lowercase							   =torch.mean(torch.abs(UpperCAmelCase ) )

      assert abs(result_sum.item() - 108.4439 ) < 1e-2
      assert abs(result_mean.item() - 0.1412 ) < 1e-3





     def   __A      (self )  ->     List[str]:
      _lowercase							   =self.scheduler_classes[0]
      _lowercase							   =self.get_scheduler_config()
      _lowercase							   =scheduler_class(**UpperCAmelCase )

      scheduler.set_timesteps(self.num_inference_steps	,  device=UpperCAmelCase )
      _lowercase							   =torch.manual_seed(0 )

      _lowercase							   =self.dummy_model()
      _lowercase							   =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _lowercase							   =sample.to(UpperCAmelCase )

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

       _lowercase							   =model(UpperCAmelCase	,  UpperCAmelCase )

       _lowercase							   =scheduler.step(UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  generator=UpperCAmelCase )
       _lowercase							   =output.prev_sample

      _lowercase							   =torch.sum(torch.abs(UpperCAmelCase ) )
      _lowercase							   =torch.mean(torch.abs(UpperCAmelCase ) )

      assert abs(result_sum.item() - 152.3192 ) < 1e-2
      assert abs(result_mean.item() - 0.1983 ) < 1e-3





def        UpperCAmelCase_      (							__snake_case		)  ->    bool:







 """simple docstring"""


 if not isinstance(__snake_case  ,		__snake_case		):
  _lowercase							   =F"Input value of [number={number}] must be an integer"
  raise TypeError(__snake_case		)
 if number < 0 or number % 2 == 1:
  return False
 _lowercase							   =int(number**0.5		)
 return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

