



'''simple docstring'''





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: int    = (EulerDiscreteScheduler,)
 __lowerCamelCase					: str    = 10



 def       a__						(self				,     **A )					->  Optional[int]:


      """simple docstring"""



      _a											=						{
          '''num_train_timesteps''': 1_100,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
      }

      config.update(**A )
      return config



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



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



 def       a__						(self )					->  Dict:


      """simple docstring"""



      for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,     [0.0002, 0.002, 0.02] ):
           self.check_over_configs(beta_start=A				,     beta_end=A )



 def       a__						(self )					->  str:


      """simple docstring"""



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



 def       a__						(self )					->  Dict:


      """simple docstring"""



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



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						self.scheduler_classes[0]
      _a											=						self.get_scheduler_config()
      _a											=						scheduler_class(**A )

      scheduler.set_timesteps(self.num_inference_steps )

      _a											=						torch.manual_seed(0 )

      _a											=						self.dummy_model()
      _a											=						self.dummy_sample_deter * scheduler.init_noise_sigma
      _a											=						sample.to(A )

      for i, t in enumerate(scheduler.timesteps ):
           _a											=						scheduler.scale_model_input(A				,     A )

           _a											=						model(A				,     A )

           _a											=						scheduler.step(A				,     A				,     A				,     generator=A )
           _a											=						output.prev_sample

      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 10.0807 ) < 1E-2
      assert abs(result_mean.item() - 0.0131 ) < 1E-3



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						self.scheduler_classes[0]
      _a											=						self.get_scheduler_config(prediction_type='''v_prediction''' )
      _a											=						scheduler_class(**A )

      scheduler.set_timesteps(self.num_inference_steps )

      _a											=						torch.manual_seed(0 )

      _a											=						self.dummy_model()
      _a											=						self.dummy_sample_deter * scheduler.init_noise_sigma
      _a											=						sample.to(A )

      for i, t in enumerate(scheduler.timesteps ):
           _a											=						scheduler.scale_model_input(A				,     A )

           _a											=						model(A				,     A )

           _a											=						scheduler.step(A				,     A				,     A				,     generator=A )
           _a											=						output.prev_sample

      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 0.0002 ) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06 ) < 1E-3



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.scheduler_classes[0]
      _a											=						self.get_scheduler_config()
      _a											=						scheduler_class(**A )

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

      _a											=						torch.manual_seed(0 )

      _a											=						self.dummy_model()
      _a											=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _a											=						sample.to(A )

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

           _a											=						model(A				,     A )

           _a											=						scheduler.step(A				,     A				,     A				,     generator=A )
           _a											=						output.prev_sample

      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 10.0807 ) < 1E-2
      assert abs(result_mean.item() - 0.0131 ) < 1E-3



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						self.scheduler_classes[0]
      _a											=						self.get_scheduler_config()
      _a											=						scheduler_class(**A				,     use_karras_sigmas=A )

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

      _a											=						torch.manual_seed(0 )

      _a											=						self.dummy_model()
      _a											=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _a											=						sample.to(A )

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

           _a											=						model(A				,     A )

           _a											=						scheduler.step(A				,     A				,     A				,     generator=A )
           _a											=						output.prev_sample

      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 124.52299499511719 ) < 1E-2
      assert abs(result_mean.item() - 0.16213932633399963 ) < 1E-3








'''simple docstring'''





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

lowercase_	 =						typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
lowercase_	 =						typing.Union[np.floataa, int, float]  # noqa: UP007




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return np.sqrt(np.sum((np.asarray(__A) - np.asarray(__A)) ** 2))




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return sum((va - va) ** 2 for va, va in zip(__A						,			__A)) ** (1 / 2)


if __name__ == "__main__":




  def lowerCAmelCase						():



       """simple docstring"""



       from timeit import timeit

       print('''Without Numpy''')
       print(
           timeit(
               '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
       print('''With Numpy''')
       print(
           timeit(
               '''euclidean_distance([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
  benchmark()



