




from collections.abc import Iterable
from typing import Any







class 						lowercase							:

     def __init__(  self		,     _a = None				)						->							Dict:
           _A							:     Any			=						value
           _A							:     Node | None			=						None  # Added in order to delete a node easier
           _A							:     Node | None			=						None
           _A							:     Node | None			=						None



     def __repr__(  self				)						->							str:
           from pprint import pformat

           if self.left is None and self.right is None:
                 return str(self.value				)
           return pformat({F'''{self.value}''': (self.left, self.right)}		,     indent=1				)







class 						lowercase							:

     def __init__(  self		,     _a = None				)						->							Optional[Any]:
           _A							:     Dict			=						root

     def __str__(  self				)						->							str:
           return str(self.root				)

     def 					a__	(  self		,     _a		,     _a				)						->							None:
           if new_children is not None:  # reset its kids
                 _A							:     Tuple			=						node.parent
           if node.parent is not None:  # reset its parent
                 if self.is_right(_a				):  # If it is the right children
                       _A							:     Dict			=						new_children
                 else:
                       _A							:     int			=						new_children
           else:
                 _A							:     List[str]			=						new_children

     def 					a__	(  self		,     _a				)						->							bool:
           if node.parent and node.parent.right:
                 return node == node.parent.right
           return False

     def 					a__	(  self				)						->							bool:
           return self.root is None

     def 					a__	(  self		,     _a				)						->							None:
           _A							:     List[Any]			=						Node(_a				)  # create a new Node
           if self.empty():  # if Tree is empty
                 _A							:     Union[str, Any]			=						new_node  # set its root
           else:  # Tree is not empty
                 _A							:     int			=						self.root  # from root
                 if parent_node is None:
                       return
                 while True:  # While we don't get to a leaf
                       if value < parent_node.value:  # We go left
                             if parent_node.left is None:
                                   _A							:     List[Any]			=						new_node  # We insert the new node in a leaf
                                   break
                             else:
                                   _A							:     Union[str, Any]			=						parent_node.left
                       else:
                             if parent_node.right is None:
                                   _A							:     List[Any]			=						new_node
                                   break
                             else:
                                   _A							:     int			=						parent_node.right
                 _A							:     Optional[int]			=						parent_node

     def 					a__	(  self		,     *_a				)						->							None:
           for value in values:
                 self.__insert(_a				)

     def 					a__	(  self		,     _a				)						->							Node | None:
           if self.empty():
                 raise IndexError("""Warning: Tree is empty! please use another."""				)
           else:
                 _A							:     Union[str, Any]			=						self.root
                 # use lazy evaluation here to avoid NoneType Attribute error
                 while node is not None and node.value is not value:
                       _A							:     int			=						node.left if value < node.value else node.right
                 return node

     def 					a__	(  self		,     _a = None				)						->							Node | None:
           if node is None:
                 if self.root is None:
                       return None
                 _A							:     Union[str, Any]			=						self.root

           if not self.empty():
                 while node.right is not None:
                       _A							:     str			=						node.right
           return node

     def 					a__	(  self		,     _a = None				)						->							Node | None:
           if node is None:
                 _A							:     Dict			=						self.root
           if self.root is None:
                 return None
           if not self.empty():
                 _A							:     str			=						self.root
                 while node.left is not None:
                       _A							:     int			=						node.left
           return node

     def 					a__	(  self		,     _a				)						->							None:
           _A							:     Tuple			=						self.search(_a				)  # Look for the node with that label
           if node is not None:
                 if node.left is None and node.right is None:  # If it has no children
                       self.__reassign_nodes(_a		,     _a				)
                 elif node.left is None:  # Has only right children
                       self.__reassign_nodes(_a		,     node.right				)
                 elif node.right is None:  # Has only left children
                       self.__reassign_nodes(_a		,     node.left				)
                 else:
                       _A							:     Tuple			=						self.get_max(
                           node.left				)  # Gets the max value of the left branch
                       self.remove(tmp_node.value				)  # type: ignore
                       _A							:     Union[str, Any]			=						(
                           tmp_node.value  # type: ignore
                       )  # Assigns the value to the node to delete and keep tree structure

     def 					a__	(  self		,     _a				)						->							Iterable:
           if node is not None:
                 yield node  # Preorder Traversal
                 yield from self.preorder_traverse(node.left				)
                 yield from self.preorder_traverse(node.right				)

     def 					a__	(  self		,     _a=None				)						->							Any:
           if traversal_function is None:
                 return self.preorder_traverse(self.root				)
           else:
                 return traversal_function(self.root				)

     def 					a__	(  self		,     _a		,     _a				)						->							None:
           if node:
                 self.inorder(_a		,     node.left				)
                 arr.append(node.value				)
                 self.inorder(_a		,     node.right				)



     def 					a__	(  self		,     _a		,     _a				)						->							int:
           _A							:     list[int]			=						[]
           self.inorder(_a		,     _a				)  # append all values to list using inorder traversal
           return arr[k - 1]







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[]
      if curr_node is not None:
            _A							:     Union[str, Any]			=						postorder(curr_node.left     ) + postorder(curr_node.right     ) + [curr_node]
      return node_list







def 				lowerCAmelCase_	(     ):
      _A							:     int			=						(8, 3, 6, 1, 10, 14, 13, 4, 7)
      _A							:     List[Any]			=						BinarySearchTree()
      for i in testlist:
            t.insert(snake_case_     )

      # Prints all the elements of the list in order traversal
      print(snake_case_     )

      if t.search(6     ) is not None:
            print("""The value 6 exists"""     )
      else:
            print("""The value 6 doesn't exist"""     )

      if t.search(-1     ) is not None:
            print("""The value -1 exists"""     )
      else:
            print("""The value -1 doesn't exist"""     )

      if not t.empty():
            print("""Max Value: """,t.get_max().value     )  # type: ignore
            print("""Min Value: """,t.get_min().value     )  # type: ignore

      for i in testlist:
            t.remove(snake_case_     )
            print(snake_case_     )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						(DPMSolverSDEScheduler,)
     _a							 =						1_0

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           _A							:     str			=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """noise_sampler_seed""": 0,
           }

           config.update(**_a				)
           return config

     def 					a__	(  self				)						->							Tuple:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           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				)						->							Any:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Any			=						self.scheduler_classes[0]
           _A							:     List[str]			=						self.get_scheduler_config()
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Dict			=						self.dummy_model()
           _A							:     Any			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Dict			=						sample.to(_a				)

           for i, t in enumerate(scheduler.timesteps				):
                 _A							:     Optional[int]			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     str			=						model(_a		,     _a				)

                 _A							:     List[Any]			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Dict			=						torch.sum(torch.abs(_a				)				)
           _A							:     Dict			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.47821044921875				) < 1e-2
                 assert abs(result_mean.item() - 0.2178705964565277				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59352111816406				) < 1e-2
                 assert abs(result_mean.item() - 0.22342906892299652				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     Optional[int]			=						self.get_scheduler_config(prediction_type="""v_prediction"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Tuple			=						self.dummy_model()
           _A							:     int			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Tuple			=						sample.to(_a				)

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

                 _A							:     Tuple			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Optional[Any]			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[Any]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 124.77149200439453				) < 1e-2
                 assert abs(result_mean.item() - 0.16226289014816284				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 128.1663360595703				) < 1e-2
                 assert abs(result_mean.item() - 0.16688326001167297				) < 1e-3
           else:
                 assert abs(result_sum.item() - 119.8487548828125				) < 1e-2
                 assert abs(result_mean.item() - 0.1560530662536621				) < 1e-3

     def 					a__	(  self				)						->							List[str]:
           _A							:     Union[str, Any]			=						self.scheduler_classes[0]
           _A							:     List[Any]			=						self.get_scheduler_config()
           _A							:     List[str]			=						scheduler_class(**_a				)

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

           _A							:     Union[str, Any]			=						self.dummy_model()
           _A							:     Optional[Any]			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma

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

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Dict			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     str			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.46957397460938				) < 1e-2
                 assert abs(result_mean.item() - 0.21805934607982635				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59353637695312				) < 1e-2
                 assert abs(result_mean.item() - 0.22342908382415771				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.scheduler_classes[0]
           _A							:     Optional[Any]			=						self.get_scheduler_config()
           _A							:     int			=						scheduler_class(**_a		,     use_karras_sigmas=_a				)

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

           _A							:     Optional[Any]			=						self.dummy_model()
           _A							:     Dict			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma
           _A							:     str			=						sample.to(_a				)

           for t in scheduler.timesteps:
                 _A							:     Optional[int]			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     List[str]			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[str]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 176.66974135742188				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 177.63653564453125				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           else:
                 assert abs(result_sum.item() - 170.3135223388672				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2

