





"""simple docstring"""
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__	(     UpperCamelCase_					):
   __UpperCAmelCase										=       (PNDMScheduler,)
   __UpperCAmelCase										=       (('num_inference_steps', 50),)



   def  _UpperCAmelCase						(							self   , **snake_case						)			->     str:







      """simple docstring"""
      lowercase     :      List[Any]	=							{
          'num_train_timesteps': 1_0_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**lowercase_						)
      return config



   def  _UpperCAmelCase						(							self   , snake_case=0   , **snake_case						)			->     Optional[int]:







      """simple docstring"""
      lowercase     :      Optional[Any]	=							dict(self.forward_default_kwargs						)
      lowercase     :      Optional[Any]	=							kwargs.pop("""num_inference_steps"""   , lowercase_						)
      lowercase     :      Tuple	=							self.dummy_sample
      lowercase     :      int	=							0.1 * sample
      lowercase     :      int	=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
         lowercase     :      str	=							self.get_scheduler_config(**lowercase_						)
         lowercase     :      str	=							scheduler_class(**lowercase_						)
         scheduler.set_timesteps(lowercase_						)
         # copy over dummy past residuals
         lowercase     :      Any	=							dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowercase_						)
            lowercase     :      Optional[Any]	=							scheduler_class.from_pretrained(lowercase_						)
            new_scheduler.set_timesteps(lowercase_						)
            # copy over dummy past residuals
            lowercase     :      Any	=							dummy_past_residuals[:]

         lowercase     :      Union[str, Any]	=							scheduler.step_prk(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample
         lowercase     :      List[str]	=							new_scheduler.step_prk(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample

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

         lowercase     :      Optional[int]	=							scheduler.step_plms(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample
         lowercase     :      Optional[Any]	=							new_scheduler.step_plms(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample

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



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""
      pass



   def  _UpperCAmelCase						(							self   , snake_case=0   , **snake_case						)			->     int:







      """simple docstring"""
      lowercase     :      Any	=							dict(self.forward_default_kwargs						)
      lowercase     :      Dict	=							kwargs.pop("""num_inference_steps"""   , lowercase_						)
      lowercase     :      Union[str, Any]	=							self.dummy_sample
      lowercase     :      Optional[int]	=							0.1 * sample
      lowercase     :      List[str]	=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
         lowercase     :      Any	=							self.get_scheduler_config()
         lowercase     :      int	=							scheduler_class(**lowercase_						)
         scheduler.set_timesteps(lowercase_						)

         # copy over dummy past residuals (must be after setting timesteps)
         lowercase     :      Any	=							dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowercase_						)
            lowercase     :      Optional[Any]	=							scheduler_class.from_pretrained(lowercase_						)
            # copy over dummy past residuals
            new_scheduler.set_timesteps(lowercase_						)

            # copy over dummy past residual (must be after setting timesteps)
            lowercase     :      Optional[int]	=							dummy_past_residuals[:]

         lowercase     :      List[str]	=							scheduler.step_prk(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample
         lowercase     :      str	=							new_scheduler.step_prk(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample

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

         lowercase     :      Optional[Any]	=							scheduler.step_plms(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample
         lowercase     :      List[str]	=							new_scheduler.step_plms(lowercase_   , lowercase_   , lowercase_   , **lowercase_						).prev_sample

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



   def  _UpperCAmelCase						(							self   , **snake_case						)			->     Tuple:







      """simple docstring"""
      lowercase     :      Union[str, Any]	=							self.scheduler_classes[0]
      lowercase     :      Optional[Any]	=							self.get_scheduler_config(**lowercase_						)
      lowercase     :      Any	=							scheduler_class(**lowercase_						)

      lowercase     :      Union[str, Any]	=							1_0
      lowercase     :      str	=							self.dummy_model()
      lowercase     :      Union[str, Any]	=							self.dummy_sample_deter
      scheduler.set_timesteps(lowercase_						)

      for i, t in enumerate(scheduler.prk_timesteps						):
         lowercase     :      Union[str, Any]	=							model(lowercase_   , lowercase_						)
         lowercase     :      Any	=							scheduler.step_prk(lowercase_   , lowercase_   , lowercase_						).prev_sample

      for i, t in enumerate(scheduler.plms_timesteps						):
         lowercase     :      List[Any]	=							model(lowercase_   , lowercase_						)
         lowercase     :      int	=							scheduler.step_plms(lowercase_   , lowercase_   , lowercase_						).prev_sample

      return sample



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







      """simple docstring"""
      lowercase     :      Any	=							dict(self.forward_default_kwargs						)

      lowercase     :      Optional[int]	=							kwargs.pop("""num_inference_steps"""   , lowercase_						)

      for scheduler_class in self.scheduler_classes:
         lowercase     :      List[str]	=							self.get_scheduler_config()
         lowercase     :      List[Any]	=							scheduler_class(**lowercase_						)

         lowercase     :      Union[str, Any]	=							self.dummy_sample
         lowercase     :      Optional[int]	=							0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_   , """set_timesteps"""						):
            scheduler.set_timesteps(lowercase_						)
         elif num_inference_steps is not None and not hasattr(lowercase_   , """set_timesteps"""						):
            lowercase     :      Tuple	=							num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         lowercase     :      List[Any]	=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
         lowercase     :      Union[str, Any]	=							dummy_past_residuals[:]

         lowercase     :      List[Any]	=							scheduler.step_prk(lowercase_   , 0   , lowercase_   , **lowercase_						).prev_sample
         lowercase     :      str	=							scheduler.step_prk(lowercase_   , 1   , lowercase_   , **lowercase_						).prev_sample

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

         lowercase     :      Tuple	=							scheduler.step_plms(lowercase_   , 0   , lowercase_   , **lowercase_						).prev_sample
         lowercase     :      Optional[Any]	=							scheduler.step_plms(lowercase_   , 1   , lowercase_   , **lowercase_						).prev_sample

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



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""




      for timesteps in [1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=lowercase_						)



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







      """simple docstring"""




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

      lowercase     :      Optional[int]	=							self.scheduler_classes[0]
      lowercase     :      Dict	=							self.get_scheduler_config(steps_offset=1						)
      lowercase     :      List[Any]	=							scheduler_class(**lowercase_						)
      scheduler.set_timesteps(1_0						)
      assert torch.equal(
          scheduler.timesteps   , torch.LongTensor(
              [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]						)   , )



   def  _UpperCAmelCase						(							self						)			->     str:







      """simple docstring"""




      for beta_start, beta_end in zip([0.00_01, 0.0_01]   , [0.0_02, 0.02]						):
         self.check_over_configs(beta_start=lowercase_   , beta_end=lowercase_						)



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







      """simple docstring"""




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



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""




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



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







      """simple docstring"""




      for t in [1, 5, 1_0]:
         self.check_over_forward(time_step=lowercase_						)



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







      """simple docstring"""




      for t, num_inference_steps in zip([1, 5, 1_0]   , [1_0, 5_0, 1_0_0]						):
         self.check_over_forward(num_inference_steps=lowercase_						)



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







      """simple docstring"""
      lowercase     :      Optional[int]	=							2_7

      for scheduler_class in self.scheduler_classes:
         lowercase     :      Dict	=							self.dummy_sample
         lowercase     :      List[Any]	=							0.1 * sample

         lowercase     :      List[Any]	=							self.get_scheduler_config()
         lowercase     :      List[str]	=							scheduler_class(**lowercase_						)

         scheduler.set_timesteps(lowercase_						)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]						):
            lowercase     :      Tuple	=							scheduler.step_prk(lowercase_   , lowercase_   , lowercase_						).prev_sample



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







      """simple docstring"""




      with self.assertRaises(lowercase_						):
         lowercase     :      Dict	=							self.scheduler_classes[0]
         lowercase     :      Optional[Any]	=							self.get_scheduler_config()
         lowercase     :      List[Any]	=							scheduler_class(**lowercase_						)

         scheduler.step_plms(self.dummy_sample   , 1   , self.dummy_sample						).prev_sample



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







      """simple docstring"""
      lowercase     :      Tuple	=							self.full_loop()
      lowercase     :      Optional[Any]	=							torch.sum(torch.abs(lowercase_						)						)
      lowercase     :      Union[str, Any]	=							torch.mean(torch.abs(lowercase_						)						)

      assert abs(result_sum.item() - 1_98.13_18						) < 1E-2
      assert abs(result_mean.item() - 0.25_80						) < 1E-3



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







      """simple docstring"""
      lowercase     :      int	=							self.full_loop(prediction_type="""v_prediction"""						)
      lowercase     :      Dict	=							torch.sum(torch.abs(lowercase_						)						)
      lowercase     :      str	=							torch.mean(torch.abs(lowercase_						)						)

      assert abs(result_sum.item() - 67.39_86						) < 1E-2
      assert abs(result_mean.item() - 0.08_78						) < 1E-3



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







      """simple docstring"""
      lowercase     :      Tuple	=							self.full_loop(set_alpha_to_one=lowercase_   , beta_start=0.01						)
      lowercase     :      Optional[Any]	=							torch.sum(torch.abs(lowercase_						)						)
      lowercase     :      Union[str, Any]	=							torch.mean(torch.abs(lowercase_						)						)

      assert abs(result_sum.item() - 2_30.03_99						) < 1E-2
      assert abs(result_mean.item() - 0.29_95						) < 1E-3





   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""
      lowercase     :      str	=							self.full_loop(set_alpha_to_one=lowercase_   , beta_start=0.01						)
      lowercase     :      Union[str, Any]	=							torch.sum(torch.abs(lowercase_						)						)
      lowercase     :      str	=							torch.mean(torch.abs(lowercase_						)						)

      assert abs(result_sum.item() - 1_86.94_82						) < 1E-2
      assert abs(result_mean.item() - 0.24_34						) < 1E-3






from collections.abc import Iterable
from typing import Any




class A    :






      def __init__(   self		:	Dict     ,  lowercase_		:	int | None = None					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =value
             _lowerCamelCase			:						Node | None            =None  # Added in order to delete a node easier
             _lowerCamelCase			:						Node | None            =None
             _lowerCamelCase			:						Node | None            =None






      def __repr__(   self		:	Dict					)   ->		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 A    :






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	Node | None = None					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =root






      def __str__(   self		:	Tuple					)   ->		str:
             """simple docstring"""




             return str(self.root					)






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node     ,  lowercase_		:	Node | None					)   ->		None:
             """simple docstring"""




             if new_children is not None:  # reset its kids
                    _lowerCamelCase			:						Optional[int]            =node.parent
             if node.parent is not None:  # reset its parent
                    if self.is_right(lowercase_					):  # If it is the right children
                           _lowerCamelCase			:						int            =new_children
                    else:
                           _lowerCamelCase			:						Dict            =new_children
             else:
                    _lowerCamelCase			:						Tuple            =new_children






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node					)   ->		bool:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	int					)   ->		bool:
             """simple docstring"""




             return self.root is None






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	Union[str, Any]					)   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =Node(lowercase_					)  # create a new Node
             if self.empty():  # if Tree is empty
                    _lowerCamelCase			:						Union[str, Any]            =new_node  # set its root
             else:  # Tree is not empty
                    _lowerCamelCase			:						Optional[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:
                                         _lowerCamelCase			:						Optional[int]            =new_node  # We insert the new node in a leaf
                                         break
                                  else:
                                         _lowerCamelCase			:						Optional[Any]            =parent_node.left
                           else:
                                  if parent_node.right is None:
                                         _lowerCamelCase			:						Optional[Any]            =new_node
                                         break
                                  else:
                                         _lowerCamelCase			:						Optional[int]            =parent_node.right
                    _lowerCamelCase			:						Optional[Any]            =parent_node






      def 			lowerCamelCase			(   self		:	Any     ,  *lowercase_		:	Union[str, Any]					)   ->		None:
             """simple docstring"""




             for value in values:
                    self.__insert(lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	List[Any]					)   ->		Node | None:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	Node | None = None					)   ->		Node | None:
             """simple docstring"""




             if node is None:
                    if self.root is None:
                           return None
                    _lowerCamelCase			:						Union[str, Any]            =self.root

             if not self.empty():
                    while node.right is not None:
                           _lowerCamelCase			:						Optional[int]            =node.right
             return node






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node | None = None					)   ->		Node | None:
             """simple docstring"""




             if node is None:
                    _lowerCamelCase			:						Union[str, Any]            =self.root
             if self.root is None:
                    return None
             if not self.empty():
                    _lowerCamelCase			:						Optional[int]            =self.root
                    while node.left is not None:
                           _lowerCamelCase			:						List[Any]            =node.left
             return node






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	int					)   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.search(lowercase_					)  # 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(lowercase_     ,  lowercase_					)
                    elif node.left is None:  # Has only right children
                           self.__reassign_nodes(lowercase_     ,  node.right					)
                    elif node.right is None:  # Has only left children
                           self.__reassign_nodes(lowercase_     ,  node.left					)
                    else:
                           _lowerCamelCase			:						List[str]            =self.get_max(
                               node.left					)  # Gets the max value of the left branch
                           self.remove(tmp_node.value					)  # type: ignore
                           _lowerCamelCase			:						Union[str, Any]            =(
                               tmp_node.value  # type: ignore
                           )  # Assigns the value to the node to delete and keep tree structure






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	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 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Dict=None					)   ->		Any:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	list     ,  lowercase_		:	Node | None					)   ->		None:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	int     ,  lowercase_		:	Node					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						list[int]            =[]
             self.inorder(lowercase_     ,  lowercase_					)  # append all values to list using inorder traversal
             return arr[k - 1]




def       a_ (       SCREAMING_SNAKE_CASE__   : Node | None	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =[]
       if curr_node is not None:
              _lowerCamelCase			:						List[Any]            =postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
       return node_list




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =(8, 3, 6, 1, 10, 14, 13, 4, 7)
       _lowerCamelCase			:						int            =BinarySearchTree()
       for i in testlist:
              t.insert(SCREAMING_SNAKE_CASE__	)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)


