
"""simple docstring"""







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 				UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 UpperCamelCase_						:     Any				   =    (DDIMScheduler,)
 UpperCamelCase_						:     Dict				   =    (("""eta""", 0.0), ("""num_inference_steps""", 50))




 def   a      ( self       : List[Any]     ,   **a_       : int )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  {
          """num_train_timesteps""": 10_00,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
          """clip_sample""": True,
      }

      config.update(**a_ )
      return config




 def   a      ( self       : int     ,   **a_       : Union[str, Any] )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.scheduler_classes[0]
      UpperCAmelCase_						:    Union[str, Any]    					=  self.get_scheduler_config(**a_ )
      UpperCAmelCase_						:    Optional[Any]    					=  scheduler_class(**a_ )

      UpperCAmelCase_						:    Any    					=  10, 0.0

      UpperCAmelCase_						:    Any    					=  self.dummy_model()
      UpperCAmelCase_						:    Tuple    					=  self.dummy_sample_deter

      scheduler.set_timesteps(a_ )

      for t in scheduler.timesteps:
           UpperCAmelCase_						:    List[str]    					=  model(a_     ,   a_ )
           UpperCAmelCase_						:    Dict    					=  scheduler.step(a_     ,   a_     ,   a_     ,   a_ ).prev_sample

      return sample




 def   a      ( self       : Union[str, Any] )->					Dict:



      """simple docstring"""

      for timesteps in [1_00, 5_00, 10_00]:
           self.check_over_configs(num_train_timesteps=a_ )




 def   a      ( self       : List[Any] )->					Union[str, Any]:



      """simple docstring"""

      for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=a_ )

      UpperCAmelCase_						:    int    					=  self.scheduler_classes[0]
      UpperCAmelCase_						:    List[Any]    					=  self.get_scheduler_config(steps_offset=1 )
      UpperCAmelCase_						:    Union[str, Any]    					=  scheduler_class(**a_ )
      scheduler.set_timesteps(5 )
      assert torch.equal(scheduler.timesteps     ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )




 def   a      ( self       : List[Any] )->					List[str]:



      """simple docstring"""

      for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,   [0.002, 0.02, 0.2, 2] ):
           self.check_over_configs(beta_start=a_     ,   beta_end=a_ )




 def   a      ( self       : Optional[int] )->					List[str]:



      """simple docstring"""

      for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=a_ )




 def   a      ( self       : List[Any] )->					Optional[Any]:



      """simple docstring"""

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




 def   a      ( self       : Optional[int] )->					List[Any]:



      """simple docstring"""

      for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=a_ )




 def   a      ( self       : Dict )->					Any:



      """simple docstring"""

      for timestep_spacing in ["trailing", "leading"]:
           self.check_over_configs(timestep_spacing=a_ )




 def   a      ( self       : Union[str, Any] )->					int:



      """simple docstring"""

      for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=a_ )




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



      """simple docstring"""

      self.check_over_configs(thresholding=a_ )
      for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=a_     ,   prediction_type=a_     ,   sample_max_value=a_     ,   )




 def   a      ( self       : Dict )->					List[str]:



      """simple docstring"""

      for t in [1, 10, 49]:
           self.check_over_forward(time_step=a_ )




 def   a      ( self       : List[Any] )->					List[str]:



      """simple docstring"""

      for t, num_inference_steps in zip([1, 10, 50]     ,   [10, 50, 5_00] ):
           self.check_over_forward(time_step=a_     ,   num_inference_steps=a_ )




 def   a      ( self       : Any )->					Dict:



      """simple docstring"""

      for t, eta in zip([1, 10, 49]     ,   [0.0, 0.5, 1.0] ):
           self.check_over_forward(time_step=a_     ,   eta=a_ )




 def   a      ( self       : int )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  self.scheduler_classes[0]
      UpperCAmelCase_						:    List[str]    					=  self.get_scheduler_config()
      UpperCAmelCase_						:    Any    					=  scheduler_class(**a_ )

      assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0 ) - 0.0 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_20     ,   4_00 ) - 0.14771 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_80     ,   9_60 ) - 0.32460 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0 ) - 0.0 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87     ,   4_86 ) - 0.00979 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99     ,   9_98 ) - 0.02 ) ) < 1E-5




 def   a      ( self       : Tuple )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.full_loop()

      UpperCAmelCase_						:    Union[str, Any]    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    Union[str, Any]    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 172.0067 ) < 1E-2
      assert abs(result_mean.item() - 0.223967 ) < 1E-3




 def   a      ( self       : Any )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.full_loop(prediction_type="""v_prediction""" )

      UpperCAmelCase_						:    Dict    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    List[Any]    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 52.5302 ) < 1E-2
      assert abs(result_mean.item() - 0.0684 ) < 1E-3




 def   a      ( self       : Tuple )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  self.full_loop(set_alpha_to_one=a_     ,   beta_start=0.01 )
      UpperCAmelCase_						:    Dict    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    int    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 149.8295 ) < 1E-2
      assert abs(result_mean.item() - 0.1951 ) < 1E-3






 def   a      ( self       : Dict )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.full_loop(set_alpha_to_one=a_     ,   beta_start=0.01 )
      UpperCAmelCase_						:    Union[str, Any]    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    Optional[int]    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 149.0784 ) < 1E-2
      assert abs(result_mean.item() - 0.1941 ) < 1E-3



"""simple docstring"""







from __future__ import annotations



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : int = 0 )->					None:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  value
      UpperCAmelCase_						:    Node | None    					=  None
      UpperCAmelCase_						:    Node | None    					=  None




def 			A_			(					lowercase						, lowercase      )     ->							Node | None:


     """simple docstring"""







     if treea is None:
          return treea
     if treea is None:
          return treea

     UpperCAmelCase_						:    int    					=  treea.value + treea.value
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.left						, treea.left      )
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.right						, treea.right      )
     return treea




def 			A_			(					lowercase      )     ->							None:


     """simple docstring"""







     if root:
          print(root.value      )
          print_preorder(root.left      )
          print_preorder(root.right      )


if __name__ == "__main__":
       lowercase_						    =					Node(1)
       lowercase_						    =					Node(2)
       lowercase_						    =					Node(3)
       lowercase_						    =					Node(4)

       lowercase_						    =					Node(2)
       lowercase_						    =					Node(4)
       lowercase_						    =					Node(6)
       lowercase_						    =					Node(9)
       lowercase_						    =					Node(5)

       print("Tree1 is: ")
       print_preorder(treea)
       print("Tree2 is: ")
       print_preorder(treea)
       lowercase_						    =					merge_two_binary_trees(treea, treea)
       print("Merged Tree is: ")
       print_preorder(merged_tree)

