

import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	Dict		   =       (EulerAncestralDiscreteScheduler,)
      UpperCamelCase_    :	int		   =       1_0



      def 		UpperCamelCase_	(    self   :       str    ,      **A__   :       Optional[Any]	)					->  Any:
        _snake_case		=							{
            '''num_train_timesteps''': 1100,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
        }

        config.update(**A__	)
        return config



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  Optional[int]:
        for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=A__	)



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  List[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=A__    ,      beta_end=A__	)



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Optional[Any]:
        for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=A__	)



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



      def 		UpperCamelCase_	(    self   :       Any	)					->  Optional[Any]:
        _snake_case		=							self.scheduler_classes[0]
        _snake_case		=							self.get_scheduler_config()
        _snake_case		=							scheduler_class(**A__	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _snake_case		=							torch.manual_seed(0	)

        _snake_case		=							self.dummy_model()
        _snake_case		=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _snake_case		=							sample.to(A__	)

        for i, t in enumerate(scheduler.timesteps	):
          _snake_case		=							scheduler.scale_model_input(A__    ,      A__	)

          _snake_case		=							model(A__    ,      A__	)

          _snake_case		=							scheduler.step(A__    ,      A__    ,      A__    ,      generator=A__	)
          _snake_case		=							output.prev_sample

        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _snake_case		=							torch.mean(torch.abs(A__	)	)

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



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Union[str, Any]:
        _snake_case		=							self.scheduler_classes[0]
        _snake_case		=							self.get_scheduler_config(prediction_type='''v_prediction'''	)
        _snake_case		=							scheduler_class(**A__	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _snake_case		=							torch.manual_seed(0	)

        _snake_case		=							self.dummy_model()
        _snake_case		=							self.dummy_sample_deter * scheduler.init_noise_sigma
        _snake_case		=							sample.to(A__	)

        for i, t in enumerate(scheduler.timesteps	):
          _snake_case		=							scheduler.scale_model_input(A__    ,      A__	)

          _snake_case		=							model(A__    ,      A__	)

          _snake_case		=							scheduler.step(A__    ,      A__    ,      A__    ,      generator=A__	)
          _snake_case		=							output.prev_sample

        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _snake_case		=							torch.mean(torch.abs(A__	)	)

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







      def 		UpperCamelCase_	(    self   :       int	)					->  Optional[int]:
        _snake_case		=							self.scheduler_classes[0]
        _snake_case		=							self.get_scheduler_config()
        _snake_case		=							scheduler_class(**A__	)

        scheduler.set_timesteps(self.num_inference_steps    ,      device=A__	)
        _snake_case		=							torch.manual_seed(0	)

        _snake_case		=							self.dummy_model()
        _snake_case		=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _snake_case		=							sample.to(A__	)

        for t in scheduler.timesteps:
          _snake_case		=							scheduler.scale_model_input(A__    ,      A__	)

          _snake_case		=							model(A__    ,      A__	)

          _snake_case		=							scheduler.step(A__    ,      A__    ,      A__    ,      generator=A__	)
          _snake_case		=							output.prev_sample

        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _snake_case		=							torch.mean(torch.abs(A__	)	)

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




from collections import namedtuple

__A    	=			namedtuple('''from_to''', '''from_ to''')

__A    	=			{
    '''atm''': from_to(1, 1),
    '''pascal''': from_to(0.000_0098, 10_13_25),
    '''bar''': from_to(0.98_6923, 1.0_1325),
    '''kilopascal''': from_to(0.0098_6923, 101.325),
    '''megapascal''': from_to(9.8_6923, 0.10_1325),
    '''psi''': from_to(0.06_8046, 14.6959),
    '''inHg''': from_to(0.033_4211, 29.9213),
    '''torr''': from_to(0.0013_1579, 7_60),
}



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			float:



  """simple docstring"""






  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

