
"""simple docstring"""




from collections.abc import Iterable
from typing import Any





class  snake_case     :

       def __init__(self       ,       SCREAMING_SNAKE_CASE_ = None    ):

         """simple docstring"""







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







       def __repr__(self    ):

         """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  snake_case     :

       def __init__(self       ,       SCREAMING_SNAKE_CASE_ = None    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = root

       def __str__(self    ):

         """simple docstring"""







         return str(self.root    )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







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

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







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

       def _lowercase					(self    ):

         """simple docstring"""







         return self.root is None

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







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

       def _lowercase					(self       ,       *SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







         for value in values:
           self.__insert(SCREAMING_SNAKE_CASE_    )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







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

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None    ):

         """simple docstring"""







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

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

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None    ):

         """simple docstring"""







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

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







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

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

         """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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=None    ):

         """simple docstring"""







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

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







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







       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







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







def        _lowerCamelCase ( __a    ):
  SCREAMING_SNAKE_CASE_						 = []
  if curr_node is not None:
    SCREAMING_SNAKE_CASE_						 = postorder(curr_node.left    ) + postorder(curr_node.right    ) + [curr_node]
  return node_list



def        _lowerCamelCase ( ):
  SCREAMING_SNAKE_CASE_						 = (8, 3, 6, 1, 10, 14, 13, 4, 7)
  SCREAMING_SNAKE_CASE_						 = BinarySearchTree()
  for i in testlist:
    t.insert(__a    )

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

  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(__a    )
    print(__a    )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

									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    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 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    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE_						 = samplea.shape[0]
									SCREAMING_SNAKE_CASE_						 = torch.stack([samplea, samplea, samplea]       ,       dim=0    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(SCREAMING_SNAKE_CASE_    )[0:3, None].repeat(1       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model(samples.flatten(0       ,       1    )       ,       timesteps.flatten(0       ,       1    )    )
									SCREAMING_SNAKE_CASE_						 = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,       timesteps.flatten(0       ,       1    )       ,       samples.flatten(0       ,       1    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3