






'''simple docstring'''







from collections.abc import Iterable
from typing import Any





class 	SCREAMING_SNAKE_CASE					:






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int | None = None					)			->			Optional[int]:
							a_		:  Optional[int]											=      value
							a_		:  Node | None											=      None  # Added in order to delete a node easier
							a_		:  Node | None											=      None
							a_		:  Node | None											=      None







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






			def __init__( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Node | None = None					)			->			Tuple:
							a_		:  Optional[int]											=      root






			def __str__( self      :			Optional[int]					)			->			str:
							return str(self.root					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Node  ,						__SCREAMING_SNAKE_CASE      :			Node | None					)			->			None:
							if new_children is not None:  # reset its kids
											a_		:  Union[str, Any]											=      node.parent
							if node.parent is not None:  # reset its parent
											if self.is_right(__SCREAMING_SNAKE_CASE					):  # If it is the right children
															a_		:  Dict											=      new_children
											else:
															a_		:  Tuple											=      new_children
							else:
											a_		:  List[Any]											=      new_children






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Node					)			->			bool:
							if node.parent and node.parent.right:
											return node == node.parent.right
							return False






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]					)			->			None:
							a_		:  Union[str, Any]											=      Node(__SCREAMING_SNAKE_CASE					)  # 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_		:  Dict											=      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_		:  Optional[Any]											=      parent_node.left
															else:
																			if parent_node.right is None:
																							a_		:  Dict											=      new_node
																							break
																			else:
																							a_		:  str											=      parent_node.right
											a_		:  Dict											=      parent_node






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						*__SCREAMING_SNAKE_CASE      :			List[str]					)			->			None:
							for value in values:
											self.__insert(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Node | None:
							if self.empty():
											raise IndexError('''Warning: Tree is empty! please use another.'''					)
							else:
											a_		:  Any											=      self.root
											# use lazy evaluation here to avoid NoneType Attribute error
											while node is not None and node.value is not value:
															a_		:  Any											=      node.left if value < node.value else node.right
											return node






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Node | None = 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_		:  Tuple											=      node.right
							return node






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Node | None = None					)			->			Node | None:
							if node is None:
											a_		:  str											=      self.root
							if self.root is None:
											return None
							if not self.empty():
											a_		:  Any											=      self.root
											while node.left is not None:
															a_		:  Optional[Any]											=      node.left
							return node






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			None:
							a_		:  Union[str, Any]											=      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:
															a_		:  Dict											=      self.get_max(
															    node.left					)  # Gets the max value of the left branch
															self.remove(tmp_node.value					)  # type: ignore
															a_		:  List[str]											=      (
															    tmp_node.value  # type: ignore
															)  # Assigns the value to the node to delete and keep tree structure






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Node | None					)			->			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 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[str]=None					)			->			Any:
							if traversal_function is None:
											return self.preorder_traverse(self.root					)
							else:
											return traversal_function(self.root					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			list  ,						__SCREAMING_SNAKE_CASE      :			Node | None					)			->			None:
							if node:
											self.inorder(__SCREAMING_SNAKE_CASE  ,						node.left					)
											arr.append(node.value					)
											self.inorder(__SCREAMING_SNAKE_CASE  ,						node.right					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			Node					)			->			int:
							a_		:  list[int]											=      []
							self.inorder(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)  # append all values to list using inorder traversal
							return arr[k - 1]





def 		_UpperCAmelCase   (							__A							:   Node | None      ):
				a_		:  Optional[Any]											=      []
				if curr_node is not None:
								a_		:  Optional[int]											=      postorder(curr_node.left      ) + postorder(curr_node.right      ) + [curr_node]
				return node_list





def 		_UpperCAmelCase   (							):
				a_		:  Tuple											=      (8, 3, 6, 1, 10, 14, 13, 4, 7)
				a_		:  int											=      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 gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Tuple											=      UNetaDModel(
							    sample_size=(32, 64)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Any											=      UNetaDConditionModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')  ,						cross_attention_dim=10  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							torch.manual_seed(0					)
							a_		:  List[Any]											=      AutoencoderKL(
							    sample_size=(128, 64)  ,						in_channels=1  ,						out_channels=1  ,						latent_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')  ,						up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')  ,						)
							a_		:  List[Any]											=      UNetaDModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return vqvae, unet







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:  Union[str, Any]											=      Mel(
							    x_res=self.dummy_unet.config.sample_size[1]  ,						y_res=self.dummy_unet.config.sample_size[0]  ,						)

							a_		:  Any											=      DDPMScheduler()
							a_		:  str											=      AudioDiffusionPipeline(vqvae=__SCREAMING_SNAKE_CASE  ,						unet=self.dummy_unet  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[int]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4					)
							a_		:  List[Any]											=      output.audios[0]
							a_		:  Dict											=      output.images[0]

							a_		:  Dict											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  Optional[Any]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  str											=      np.frombuffer(image_from_tuple.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

							a_		:  str											=      Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]  ,						y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]  ,						)

							a_		:  int											=      DDIMScheduler()
							a_		:  Dict											=      self.dummy_vqvae_and_unet
							a_		:  List[str]											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=dummy_vqvae_and_unet[1]  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  List[str]											=      np.random.uniform(-1  ,						1  ,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
							a_		:  int											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  int											=      pipe(raw_audio=__SCREAMING_SNAKE_CASE  ,						generator=__SCREAMING_SNAKE_CASE  ,						start_step=5  ,						steps=10					)
							a_		:  List[str]											=      output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							a_		:  Optional[Any]											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

							a_		:  List[str]											=      self.dummy_unet_condition
							a_		:  Dict											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=__SCREAMING_SNAKE_CASE  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  Any											=      torch.rand((1, 1, 10)					)
							a_		:  Tuple											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						encoding=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.images[0]
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							a_		:  Any											=      torch_device

							a_		:  Optional[int]											=      DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''					)
							a_		:  Dict											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.audios[0]
							a_		:  Tuple											=      output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							a_		:  str											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  Tuple											=      np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0




