



'''simple docstring'''





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	(EulerAncestralDiscreteScheduler,)
    __UpperCAmelCase							=	10





    def 					__snake_case       (    self    :    Tuple,    **_snake_case    :    Optional[Any]     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  ={
           '''num_train_timesteps''': 1_100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_snake_case     )
       return config





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       for timesteps in [10, 50, 100, 1_000]:
          self.check_over_configs(num_train_timesteps=_snake_case     )





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       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=_snake_case,    beta_end=_snake_case     )





    def 					__snake_case       (    self    :    int     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    Dict     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =self.scheduler_classes[0]
       snake_case   :							int						  =self.get_scheduler_config()
       snake_case   :							Any						  =scheduler_class(**_snake_case     )

       scheduler.set_timesteps(self.num_inference_steps     )

       snake_case   :							Dict						  =torch.manual_seed(0     )

       snake_case   :							int						  =self.dummy_model()
       snake_case   :							List[str]						  =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case   :							Dict						  =sample.to(_snake_case     )

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

          snake_case   :							Optional[int]						  =model(_snake_case,    _snake_case     )

          snake_case   :							str						  =scheduler.step(_snake_case,    _snake_case,    _snake_case,    generator=_snake_case     )
          snake_case   :							str						  =output.prev_sample

       snake_case   :							Dict						  =torch.sum(torch.abs(_snake_case     )     )
       snake_case   :							Optional[Any]						  =torch.mean(torch.abs(_snake_case     )     )

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





    def 					__snake_case       (    self    :    Tuple     ):



       '''simple docstring'''







       snake_case   :							str						  =self.scheduler_classes[0]
       snake_case   :							List[Any]						  =self.get_scheduler_config(prediction_type='''v_prediction'''     )
       snake_case   :							Optional[Any]						  =scheduler_class(**_snake_case     )

       scheduler.set_timesteps(self.num_inference_steps     )

       snake_case   :							List[Any]						  =torch.manual_seed(0     )

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

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

          snake_case   :							Tuple						  =model(_snake_case,    _snake_case     )

          snake_case   :							str						  =scheduler.step(_snake_case,    _snake_case,    _snake_case,    generator=_snake_case     )
          snake_case   :							Tuple						  =output.prev_sample

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

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






    def 					__snake_case       (    self    :    Any     ):



       '''simple docstring'''







       snake_case   :							str						  =self.scheduler_classes[0]
       snake_case   :							Union[str, Any]						  =self.get_scheduler_config()
       snake_case   :							List[Any]						  =scheduler_class(**_snake_case     )

       scheduler.set_timesteps(self.num_inference_steps,    device=_snake_case     )
       snake_case   :							List[str]						  =torch.manual_seed(0     )

       snake_case   :							Optional[int]						  =self.dummy_model()
       snake_case   :							int						  =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case   :							str						  =sample.to(_snake_case     )

       for t in scheduler.timesteps:
          snake_case   :							List[str]						  =scheduler.scale_model_input(_snake_case,    _snake_case     )

          snake_case   :							int						  =model(_snake_case,    _snake_case     )

          snake_case   :							str						  =scheduler.step(_snake_case,    _snake_case,    _snake_case,    generator=_snake_case     )
          snake_case   :							Optional[int]						  =output.prev_sample

       snake_case   :							Union[str, Any]						  =torch.sum(torch.abs(_snake_case     )     )
       snake_case   :							List[Any]						  =torch.mean(torch.abs(_snake_case     )     )

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






'''simple docstring'''





import doctest

import projectq
from projectq.ops import H, Measure
def 		_a   (  lowerCamelCase_							):
   snake_case   :							str						  =quantum_engine.allocate_qubit()
   H | qubit
   Measure | qubit
   return int(lowerCamelCase_							)


if __name__ == "__main__":
      doctest.testmod()

      # initialises a new quantum backend
      A			:   Dict			=      projectq.MainEngine()

      # Generate a list of 10 random numbers
      A			:   Dict			=      [get_random_number(quantum_engine) for _ in range(10)]

      # Flushes the quantum engine from memory
      quantum_engine.flush()

      print("""Random numbers""", random_numbers_list)





