



'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__       :	Union[str, Any]    = logging.get_logger(__name__)

UpperCamelCase__       :	Tuple    = {
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		int			    =		'''marian'''
			_A      :		Optional[Any]			    =		['''past_key_values''']
			_A      :		Optional[int]			    =		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

			def __init__(							self     : int							,     lowerCAmelCase__     : Tuple=5_8_1_0_1							,     lowerCAmelCase__     : List[Any]=None							,     lowerCAmelCase__     : Any=1_0_2_4							,     lowerCAmelCase__     : Dict=1_2							,     lowerCAmelCase__     : Tuple=4_0_9_6							,     lowerCAmelCase__     : int=1_6							,     lowerCAmelCase__     : List[Any]=1_2							,     lowerCAmelCase__     : List[Any]=4_0_9_6							,     lowerCAmelCase__     : int=1_6							,     lowerCAmelCase__     : List[str]=0.0							,     lowerCAmelCase__     : Optional[int]=0.0							,     lowerCAmelCase__     : List[Any]=True							,     lowerCAmelCase__     : str=True							,     lowerCAmelCase__     : Optional[Any]="gelu"							,     lowerCAmelCase__     : Dict=1_0_2_4							,     lowerCAmelCase__     : Any=0.1							,     lowerCAmelCase__     : Any=0.0							,     lowerCAmelCase__     : Optional[int]=0.0							,     lowerCAmelCase__     : List[str]=0.02							,     lowerCAmelCase__     : Optional[Any]=5_8_1_0_0							,     lowerCAmelCase__     : Optional[int]=False							,     lowerCAmelCase__     : Tuple=5_8_1_0_0							,     lowerCAmelCase__     : Optional[int]=0							,     lowerCAmelCase__     : int=0							,     lowerCAmelCase__     : Optional[int]=True							,     **lowerCAmelCase__     : str							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		vocab_size
					__SCREAMING_SNAKE_CASE       :		Dict           =		decoder_vocab_size or vocab_size
					__SCREAMING_SNAKE_CASE       :		int           =		max_position_embeddings
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		d_model
					__SCREAMING_SNAKE_CASE       :		Tuple           =		encoder_ffn_dim
					__SCREAMING_SNAKE_CASE       :		int           =		encoder_layers
					__SCREAMING_SNAKE_CASE       :		str           =		encoder_attention_heads
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		decoder_ffn_dim
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		decoder_layers
					__SCREAMING_SNAKE_CASE       :		Dict           =		decoder_attention_heads
					__SCREAMING_SNAKE_CASE       :		str           =		dropout
					__SCREAMING_SNAKE_CASE       :		Tuple           =		attention_dropout
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		activation_dropout
					__SCREAMING_SNAKE_CASE       :		Any           =		activation_function
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		init_std
					__SCREAMING_SNAKE_CASE       :		int           =		encoder_layerdrop
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		decoder_layerdrop
					__SCREAMING_SNAKE_CASE       :		Any           =		use_cache
					__SCREAMING_SNAKE_CASE       :		str           =		encoder_layers
					__SCREAMING_SNAKE_CASE       :		Dict           =		scale_embedding  # scale factor will be sqrt(d_model) if True
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		share_encoder_decoder_embeddings
					super().__init__(
					    pad_token_id=lowerCAmelCase__							,     eos_token_id=lowerCAmelCase__							,     is_encoder_decoder=lowerCAmelCase__							,     decoder_start_token_id=lowerCAmelCase__							,     forced_eos_token_id=lowerCAmelCase__							,     **lowerCAmelCase__							,     )






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					if self.task in ["default", "seq2seq-lm"]:
							__SCREAMING_SNAKE_CASE       :		Any           =		OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
							    ]						)

							if self.use_past:
									__SCREAMING_SNAKE_CASE       :		Dict           =		{0: """batch"""}
									__SCREAMING_SNAKE_CASE       :		int           =		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
							else:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		{0: """batch""", 1: """decoder_sequence"""}
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		{0: """batch""", 1: """decoder_sequence"""}

							if self.use_past:
									self.fill_with_past_key_values_(lowerCAmelCase__							,     direction="""inputs"""						)
					elif self.task == "causal-lm":
							# TODO: figure this case out.
							__SCREAMING_SNAKE_CASE       :		Any           =		OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
							    ]						)
							if self.use_past:
									__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.num_layers
									for i in range(lowerCAmelCase__						):
											__SCREAMING_SNAKE_CASE       :		Tuple           =		{0: """batch""", 2: """past_sequence + sequence"""}
											__SCREAMING_SNAKE_CASE       :		Tuple           =		{0: """batch""", 2: """past_sequence + sequence"""}
					else:
							__SCREAMING_SNAKE_CASE       :		int           =		OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
							        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
							        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
							    ]						)

					return common_inputs

			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					if self.task in ["default", "seq2seq-lm"]:
							__SCREAMING_SNAKE_CASE       :		str           =		super().outputs
					else:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		super(lowerCAmelCase__							,     self						).outputs
							if self.use_past:
									__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[str]           =		self.num_layers
									for i in range(lowerCAmelCase__						):
											__SCREAMING_SNAKE_CASE       :		int           =		{0: """batch""", 2: """past_sequence + sequence"""}
											__SCREAMING_SNAKE_CASE       :		Tuple           =		{0: """batch""", 2: """past_sequence + sequence"""}
					return common_outputs

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : PreTrainedTokenizer							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : Optional[TensorType] = None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		self._generate_dummy_inputs_for_encoder_and_decoder(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

					# Generate decoder inputs
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		seq_length if not self.use_past else 1
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self._generate_dummy_inputs_for_encoder_and_decoder(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
					__SCREAMING_SNAKE_CASE       :		List[str]           =		dict(**lowerCAmelCase__							,     **lowerCAmelCase__						)

					if self.use_past:
							if not is_torch_available():
									raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
							else:
									import torch
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		common_inputs["""input_ids"""].shape
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		common_inputs["""decoder_input_ids"""].shape[1]
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		self.num_attention_heads
							__SCREAMING_SNAKE_CASE       :		Any           =		(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							__SCREAMING_SNAKE_CASE       :		str           =		decoder_seq_length + 3
							__SCREAMING_SNAKE_CASE       :		int           =		(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		torch.cat(
							    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase__							,     lowerCAmelCase__						)]							,     dim=1						)

							__SCREAMING_SNAKE_CASE       :		str           =		[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.num_layers
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		min(lowerCAmelCase__							,     lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		max(lowerCAmelCase__							,     lowerCAmelCase__						) - min_num_layers
							__SCREAMING_SNAKE_CASE       :		int           =		"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

							for _ in range(lowerCAmelCase__						):
									common_inputs["past_key_values"].append(
									    (
									        torch.zeros(lowerCAmelCase__						),
									        torch.zeros(lowerCAmelCase__						),
									        torch.zeros(lowerCAmelCase__						),
									        torch.zeros(lowerCAmelCase__						),
									    )						)
							# TODO: test this.
							__SCREAMING_SNAKE_CASE       :		str           =		encoder_shape if remaining_side_name == """encoder""" else decoder_shape
							for _ in range(lowerCAmelCase__							,     lowerCAmelCase__						):
									common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__						), torch.zeros(lowerCAmelCase__						))						)
					return common_inputs

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : PreTrainedTokenizer							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : Optional[TensorType] = None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self._generate_dummy_inputs_for_encoder_and_decoder(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

					if self.use_past:
							if not is_torch_available():
									raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
							else:
									import torch
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		common_inputs["""input_ids"""].shape
							# Not using the same length for past_key_values
							__SCREAMING_SNAKE_CASE       :		str           =		seqlen + 2
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.num_layers
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		self.num_attention_heads
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(
							    batch,
							    num_encoder_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)

							__SCREAMING_SNAKE_CASE       :		List[str]           =		common_inputs["""attention_mask"""].dtype
							__SCREAMING_SNAKE_CASE       :		int           =		torch.cat(
							    [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase__							,     lowerCAmelCase__							,     dtype=lowerCAmelCase__						)]							,     dim=1						)
							__SCREAMING_SNAKE_CASE       :		Tuple           =		[
							    (torch.zeros(lowerCAmelCase__						), torch.zeros(lowerCAmelCase__						)) for _ in range(lowerCAmelCase__						)
							]
					return common_inputs

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : PreTrainedTokenizer							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : Optional[TensorType] = None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		compute_effective_axis_dimension(
					    lowerCAmelCase__							,     fixed_dimension=OnnxConfig.default_fixed_batch							,     num_token_to_add=0						)

					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		tokenizer.num_special_tokens_to_add(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		compute_effective_axis_dimension(
					    lowerCAmelCase__							,     fixed_dimension=OnnxConfig.default_fixed_sequence							,     num_token_to_add=lowerCAmelCase__						)

					# Generate dummy inputs according to compute batch and sequence
					__SCREAMING_SNAKE_CASE       :		Any           =		[""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		dict(tokenizer(lowerCAmelCase__							,     return_tensors=lowerCAmelCase__						)						)
					return common_inputs

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : PreTrainedTokenizer							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : int = -1							,     lowerCAmelCase__     : bool = False							,     lowerCAmelCase__     : Optional[TensorType] = None							,     ):





					"""simple docstring"""






					if self.task in ["default", "seq2seq-lm"]:
							__SCREAMING_SNAKE_CASE       :		List[str]           =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
							    lowerCAmelCase__							,     batch_size=lowerCAmelCase__							,     seq_length=lowerCAmelCase__							,     is_pair=lowerCAmelCase__							,     framework=lowerCAmelCase__						)

					else:
							__SCREAMING_SNAKE_CASE       :		Dict           =		self._generate_dummy_inputs_for_causal_lm(
							    lowerCAmelCase__							,     batch_size=lowerCAmelCase__							,     seq_length=lowerCAmelCase__							,     is_pair=lowerCAmelCase__							,     framework=lowerCAmelCase__						)

					return common_inputs

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					if self.task in ["default", "seq2seq-lm"]:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		super()._flatten_past_key_values_(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE       :		List[str]           =		super(lowerCAmelCase__							,     self						)._flatten_past_key_values_(
							    lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)



			@property
			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					return 1E-4




'''simple docstring'''




from collections.abc import Iterable
from typing import Any






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : Optional[int]							,     lowerCAmelCase__     : int | None = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		value
					__SCREAMING_SNAKE_CASE       :		Node | None           =		None  # Added in order to delete a node easier
					__SCREAMING_SNAKE_CASE       :		Node | None           =		None
					__SCREAMING_SNAKE_CASE       :		Node | None           =		None



			def __repr__(							self     : Optional[Any]						):





					"""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 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : Optional[int]							,     lowerCAmelCase__     : Node | None = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		root

			def __str__(							self     : Union[str, Any]						):





					"""simple docstring"""






					return str(self.root						)

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Node							,     lowerCAmelCase__     : Node | None						):





					"""simple docstring"""






					if new_children is not None:  # reset its kids
							__SCREAMING_SNAKE_CASE       :		List[str]           =		node.parent
					if node.parent is not None:  # reset its parent
							if self.is_right(lowerCAmelCase__						):  # If it is the right children
									__SCREAMING_SNAKE_CASE       :		Any           =		new_children
							else:
									__SCREAMING_SNAKE_CASE       :		int           =		new_children
					else:
							__SCREAMING_SNAKE_CASE       :		int           =		new_children

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Node						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					return self.root is None

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : str							,     *lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					for value in values:
							self.__insert(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					if self.empty():
							raise IndexError("""Warning: Tree is empty! please use another."""						)
					else:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		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       :		Any           =		node.left if value < node.value else node.right
							return node

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : Node | None = None						):





					"""simple docstring"""






					if node is None:
							if self.root is None:
									return None
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.root

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

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Node | None = None						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		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:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		self.get_max(
									    node.left						)  # Gets the max value of the left branch
									self.remove(tmp_node.value						)  # type: ignore
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		(
									    tmp_node.value  # type: ignore
									)  # Assigns the value to the node to delete and keep tree structure

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Node | None						):





					"""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 UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Optional[Any]=None						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : list							,     lowerCAmelCase__     : Node | None						):





					"""simple docstring"""






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



			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Node						):





					"""simple docstring"""






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


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


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

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

		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(_lowerCamelCase					)
				print(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)