



from collections.abc import Iterable
from typing import Any



class 		_lowercase		:

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       int | None = None							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		value
    a						=		None  # Added in order to delete a node easier
    a						=		None
    a						=		None




   def __repr__(      self			:       str							)       ->				str:


    """simple docstring"""






    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			:       int							,   __lowerCAmelCase			:       Node | None = None							)       ->				str:


    """simple docstring"""






    a						=		root

   def __str__(      self			:       str							)       ->				str:


    """simple docstring"""






    return str(self.root							)

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       Node							,   __lowerCAmelCase			:       Node | None							)       ->				None:


    """simple docstring"""






    if new_children is not None:  # reset its kids
     a						=		node.parent
    if node.parent is not None:  # reset its parent
     if self.is_right(__lowerCAmelCase							):  # If it is the right children
      a						=		new_children
     else:
      a						=		new_children
    else:
     a						=		new_children

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Node							)       ->				bool:


    """simple docstring"""






    if node.parent and node.parent.right:
     return node == node.parent.right
    return False

   def 		A							(      self			:       str							)       ->				bool:


    """simple docstring"""






    return self.root is None

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       List[Any]							)       ->				None:


    """simple docstring"""






    a						=		Node(__lowerCAmelCase							)  # create a new Node
    if self.empty():  # if Tree is empty
     a						=		new_node  # set its root
    else:  # Tree is not empty
     a						=		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						=		new_node  # We insert the new node in a leaf
        break
       else:
        a						=		parent_node.left
      else:
       if parent_node.right is None:
        a						=		new_node
        break
       else:
        a						=		parent_node.right
     a						=		parent_node

   def 		A							(      self			:       Any							,   *__lowerCAmelCase			:       Any							)       ->				None:


    """simple docstring"""






    for value in values:
     self.__insert(__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							)       ->				Node | None:


    """simple docstring"""






    if self.empty():
     raise IndexError("Warning: Tree is empty! please use another."							)
    else:
     a						=		self.root
     # use lazy evaluation here to avoid NoneType Attribute error
     while node is not None and node.value is not value:
      a						=		node.left if value < node.value else node.right
     return node

   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       Node | None = None							)       ->				Node | None:


    """simple docstring"""






    if node is None:
     if self.root is None:
      return None
     a						=		self.root

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

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Node | None = None							)       ->				Node | None:


    """simple docstring"""






    if node is None:
     a						=		self.root
    if self.root is None:
     return None
    if not self.empty():
     a						=		self.root
     while node.left is not None:
      a						=		node.left
    return node

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       int							)       ->				None:


    """simple docstring"""






    a						=		self.search(__lowerCAmelCase							)  # 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(__lowerCAmelCase							,   __lowerCAmelCase							)
     elif node.left is None:  # Has only right children
      self.__reassign_nodes(__lowerCAmelCase							,   node.right							)
     elif node.right is None:  # Has only left children
      self.__reassign_nodes(__lowerCAmelCase							,   node.left							)
     else:
      a						=		self.get_max(
          node.left							)  # Gets the max value of the left branch
      self.remove(tmp_node.value							)  # type: ignore
      a						=		(
          tmp_node.value  # type: ignore
      )  # Assigns the value to the node to delete and keep tree structure

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       Node | None							)       ->				Iterable:


    """simple docstring"""






    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			:       Dict							,   __lowerCAmelCase			:       Tuple=None							)       ->				Any:


    """simple docstring"""






    if traversal_function is None:
     return self.preorder_traverse(self.root							)
    else:
     return traversal_function(self.root							)

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       list							,   __lowerCAmelCase			:       Node | None							)       ->				None:


    """simple docstring"""






    if node:
     self.inorder(__lowerCAmelCase							,   node.left							)
     arr.append(node.value							)
     self.inorder(__lowerCAmelCase							,   node.right							)




   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Node							)       ->				int:


    """simple docstring"""






    a						=		[]
    self.inorder(__lowerCAmelCase							,   __lowerCAmelCase							)  # append all values to list using inorder traversal
    return arr[k - 1]


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Node | None					):






 '''simple docstring'''

 a						=		[]
 if curr_node is not None:
  a						=		postorder(curr_node.left					) + postorder(curr_node.right					) + [curr_node]
 return node_list


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		(8, 3, 6, 1, 10, 14, 13, 4, 7)
 a						=		BinarySearchTree()
 for i in testlist:
  t.insert(UpperCAmelCase__					)

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

 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(UpperCAmelCase__					)
  print(UpperCAmelCase__					)


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(UniPCMultistepScheduler,)
   _UpperCAmelCase     	=			(('''num_inference_steps''', 25),)

   def 		A							(      self			:       List[Any]							,   **__lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		{
        "num_train_timesteps": 1000,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
        "solver_order": 2,
        "solver_type": "bh2",
    }

    config.update(**__lowerCAmelCase							)
    return config

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]=0							,   **__lowerCAmelCase			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)
     # copy over dummy past residuals
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      new_scheduler.set_timesteps(__lowerCAmelCase							)
      # copy over dummy past residuals
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a		,  a						=		sample, sample
     for t in range(__lowerCAmelCase							,   time_step + scheduler.config.solver_order + 1							):
      a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
      a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

      assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[Any]=0							,   **__lowerCAmelCase			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)

     # copy over dummy past residuals (must be after setting timesteps)
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      # copy over dummy past residuals
      new_scheduler.set_timesteps(__lowerCAmelCase							)

      # copy over dummy past residual (must be after setting timesteps)
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any=None							,   **__lowerCAmelCase			:       List[str]							)       ->				Any:


    """simple docstring"""






    if scheduler is None:
     a						=		self.scheduler_classes[0]
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(**__lowerCAmelCase							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    return sample

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)

    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)

     a						=		self.dummy_sample
     a						=		0.1 * sample

     if num_inference_steps is not None and hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      scheduler.set_timesteps(__lowerCAmelCase							)
     elif num_inference_steps is not None and not hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      a						=		num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     a						=		scheduler.timesteps[5]
     a						=		scheduler.timesteps[6]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

     self.assertEqual(output_a.shape							,   sample.shape							)
     self.assertEqual(output_a.shape							,   output_a.shape							)

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


    """simple docstring"""






    a						=		UniPCMultistepScheduler(**self.get_scheduler_config()							)
    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

    a						=		DPMSolverSinglestepScheduler.from_config(scheduler.config							)
    a						=		DEISMultistepScheduler.from_config(scheduler.config							)
    a						=		DPMSolverMultistepScheduler.from_config(scheduler.config							)
    a						=		UniPCMultistepScheduler.from_config(scheduler.config							)

    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

   def 		A							(      self			:       List[Any]							)       ->				Dict:


    """simple docstring"""






    for timesteps in [25, 50, 100, 999, 1000]:
     self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Tuple:


    """simple docstring"""






    self.check_over_configs(thresholding=__lowerCAmelCase							)
    for order in [1, 2, 3]:
     for solver_type in ["bh1", "bh2"]:
      for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(
            thresholding=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   sample_max_value=__lowerCAmelCase							,   solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   )

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


    """simple docstring"""






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

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


    """simple docstring"""






    for solver_type in ["bh1", "bh2"]:
     for order in [1, 2, 3]:
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       a						=		self.full_loop(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       assert not torch.isnan(__lowerCAmelCase							).any(), "Samples have nan numbers"

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


    """simple docstring"""






    self.check_over_configs(lower_order_final=__lowerCAmelCase							)
    self.check_over_configs(lower_order_final=__lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
     self.check_over_forward(num_inference_steps=__lowerCAmelCase							,   time_step=0							)

   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop()
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

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


    """simple docstring"""






    a						=		self.full_loop(prediction_type="v_prediction"							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_mean.item() - 0.1_0_1_4							) < 1E-3

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


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(thresholding=__lowerCAmelCase							,   dynamic_thresholding_ratio=0							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter.half()
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    assert sample.dtype == torch.floataa




   def 		A							(      self			:       List[str]							,   **__lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

     scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
     assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps

