

'''simple docstring'''






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

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


UpperCamelCase__:       str							     =      logging.get_logger(__name__)

UpperCamelCase__:       Optional[int]							     =      {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	"""blenderbot"""
	lowerCamelCase__      			=	["""past_key_values"""]
	lowerCamelCase__      			=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







	def __init__(						self      :					int		,      __snake_case      :					Dict=8008		,      __snake_case      :					List[Any]=128		,      __snake_case      :					Union[str, Any]=2		,      __snake_case      :					int=10240		,      __snake_case      :					Optional[int]=32		,      __snake_case      :					Any=24		,      __snake_case      :					Optional[int]=10240		,      __snake_case      :					Union[str, Any]=32		,      __snake_case      :					Tuple=0.0		,      __snake_case      :					Tuple=0.0		,      __snake_case      :					Optional[int]=True		,      __snake_case      :					Tuple=True		,      __snake_case      :					int="gelu"		,      __snake_case      :					Dict=2560		,      __snake_case      :					Union[str, Any]=0.1		,      __snake_case      :					Optional[int]=0.0		,      __snake_case      :					Optional[Any]=0.0		,      __snake_case      :					Optional[Any]=0.02		,      __snake_case      :					Dict=1		,      __snake_case      :					List[str]=False		,      __snake_case      :					Optional[int]=0		,      __snake_case      :					Optional[Any]=1		,      __snake_case      :					Optional[int]=2		,      __snake_case      :					Dict=3		,      __snake_case      :					Tuple=2		,      **__snake_case      :					Tuple		,      )      ->		Any:
								UpperCAmelCase			:	List[str]										=		vocab_size
								UpperCAmelCase			:	Any										=		max_position_embeddings
								UpperCAmelCase			:	Tuple										=		d_model
								UpperCAmelCase			:	Optional[Any]										=		encoder_ffn_dim
								UpperCAmelCase			:	List[str]										=		encoder_layers
								UpperCAmelCase			:	Dict										=		encoder_attention_heads
								UpperCAmelCase			:	List[str]										=		decoder_ffn_dim
								UpperCAmelCase			:	Optional[Any]										=		decoder_layers
								UpperCAmelCase			:	Optional[Any]										=		decoder_attention_heads
								UpperCAmelCase			:	int										=		dropout
								UpperCAmelCase			:	Tuple										=		attention_dropout
								UpperCAmelCase			:	List[str]										=		activation_dropout
								UpperCAmelCase			:	str										=		activation_function
								UpperCAmelCase			:	str										=		init_std
								UpperCAmelCase			:	Dict										=		encoder_layerdrop
								UpperCAmelCase			:	Dict										=		decoder_layerdrop
								UpperCAmelCase			:	Any										=		use_cache
								UpperCAmelCase			:	List[str]										=		encoder_layers
								UpperCAmelCase			:	str										=		scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    pad_token_id=__snake_case		,      bos_token_id=__snake_case		,      eos_token_id=__snake_case		,      is_encoder_decoder=__snake_case		,      decoder_start_token_id=__snake_case		,      encoder_no_repeat_ngram_size=__snake_case		,      forced_eos_token_id=__snake_case		,      **__snake_case		,      )

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	@property
	def 					A     (						self      :					Dict		)      ->		Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
															UpperCAmelCase			:	Dict										=		OrderedDict(
															    [
															        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
															        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
															    ]		)
															if self.use_past:
																						UpperCAmelCase			:	Tuple										=		{0: '''batch'''}
																						UpperCAmelCase			:	Any										=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
															else:
																						UpperCAmelCase			:	str										=		{0: '''batch''', 1: '''decoder_sequence'''}
																						UpperCAmelCase			:	Optional[int]										=		{0: '''batch''', 1: '''decoder_sequence'''}
															if self.use_past:
																						self.fill_with_past_key_values_(__snake_case		,      direction='''inputs'''		)
								elif self.task == "causal-lm":
															UpperCAmelCase			:	Dict										=		OrderedDict(
															    [
															        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
															        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
															    ]		)
															if self.use_past:
																						UpperCAmelCase			:	Dict										=		self.num_layers
																						for i in range(__snake_case		):
																													UpperCAmelCase			:	str										=		{0: '''batch''', 2: '''past_sequence + sequence'''}
																													UpperCAmelCase			:	Optional[Any]										=		{0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
															UpperCAmelCase			:	Optional[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 					A     (						self      :					Optional[Any]		)      ->		Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
															UpperCAmelCase			:	Dict										=		super().outputs
								else:
															UpperCAmelCase			:	str										=		super(__snake_case		,      self		).outputs
															if self.use_past:
																						UpperCAmelCase			:	List[str]										=		self.num_layers
																						for i in range(__snake_case		):
																													UpperCAmelCase			:	List[Any]										=		{0: '''batch''', 2: '''past_sequence + sequence'''}
																													UpperCAmelCase			:	List[Any]										=		{0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs







	def 					A     (						self      :					Optional[int]		,      __snake_case      :					PreTrainedTokenizer		,      __snake_case      :					int = -1		,      __snake_case      :					int = -1		,      __snake_case      :					bool = False		,      __snake_case      :					Optional[TensorType] = None		,      )      ->		Mapping[str, Any]:
								UpperCAmelCase			:	Union[str, Any]										=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __snake_case		,      __snake_case		,      __snake_case		,      __snake_case		,      __snake_case		)
								# Generate decoder inputs
								UpperCAmelCase			:	List[str]										=		seq_length if not self.use_past else 1
								UpperCAmelCase			:	List[Any]										=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __snake_case		,      __snake_case		,      __snake_case		,      __snake_case		,      __snake_case		)
								UpperCAmelCase			:	str										=		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								UpperCAmelCase			:	List[str]										=		dict(**__snake_case		,      **__snake_case		)

								if self.use_past:
															if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
															else:
																						import torch
															UpperCAmelCase			:	str										=		common_inputs['''input_ids'''].shape
															UpperCAmelCase			:	Dict										=		common_inputs['''decoder_input_ids'''].shape[1]
															UpperCAmelCase			:	Optional[int]										=		self.num_attention_heads
															UpperCAmelCase			:	str										=		(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															UpperCAmelCase			:	int										=		decoder_seq_length
															UpperCAmelCase			:	List[str]										=		(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)
															UpperCAmelCase			:	Any										=		torch.cat(
															    [common_inputs['''decoder_attention_mask'''], torch.ones(__snake_case		,      __snake_case		)]		,      dim=1		)
															UpperCAmelCase			:	Optional[Any]										=		[]
															UpperCAmelCase			:	Any										=		self.num_layers

															for _ in range(__snake_case		):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(__snake_case		),
																						        torch.zeros(__snake_case		),
																						        torch.zeros(__snake_case		),
																						        torch.zeros(__snake_case		),
																						    )		)
								return common_inputs







	def 					A     (						self      :					Dict		,      __snake_case      :					PreTrainedTokenizer		,      __snake_case      :					int = -1		,      __snake_case      :					int = -1		,      __snake_case      :					bool = False		,      __snake_case      :					Optional[TensorType] = None		,      )      ->		Mapping[str, Any]:
								UpperCAmelCase			:	List[str]										=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __snake_case		,      __snake_case		,      __snake_case		,      __snake_case		,      __snake_case		)

								if self.use_past:
															if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
															else:
																						import torch
															UpperCAmelCase			:	int										=		common_inputs['''input_ids'''].shape
															UpperCAmelCase			:	Optional[int]										=		seqlen
															UpperCAmelCase			:	Optional[int]										=		self.num_layers
															UpperCAmelCase			:	Optional[int]										=		self.num_attention_heads
															UpperCAmelCase			:	Union[str, Any]										=		(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															UpperCAmelCase			:	Optional[Any]										=		common_inputs['''attention_mask'''].dtype
															UpperCAmelCase			:	Optional[int]										=		torch.cat(
															    [common_inputs['''attention_mask'''], torch.ones(__snake_case		,      __snake_case		,      dtype=__snake_case		)]		,      dim=1		)
															UpperCAmelCase			:	List[str]										=		[
															    (torch.zeros(__snake_case		), torch.zeros(__snake_case		)) for _ in range(__snake_case		)
															]
								return common_inputs







	def 					A     (						self      :					str		,      __snake_case      :					PreTrainedTokenizer		,      __snake_case      :					int = -1		,      __snake_case      :					int = -1		,      __snake_case      :					bool = False		,      __snake_case      :					Optional[TensorType] = None		,      )      ->		Mapping[str, Any]:
								# Copied from OnnxConfig.generate_dummy_inputs
								# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								UpperCAmelCase			:	str										=		compute_effective_axis_dimension(
								    __snake_case		,      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
								UpperCAmelCase			:	List[str]										=		tokenizer.num_special_tokens_to_add(__snake_case		)
								UpperCAmelCase			:	Optional[int]										=		compute_effective_axis_dimension(
								    __snake_case		,      fixed_dimension=OnnxConfig.default_fixed_sequence		,      num_token_to_add=__snake_case		)

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







	def 					A     (						self      :					Optional[int]		,      __snake_case      :					PreTrainedTokenizer		,      __snake_case      :					int = -1		,      __snake_case      :					int = -1		,      __snake_case      :					bool = False		,      __snake_case      :					Optional[TensorType] = None		,      )      ->		Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
															UpperCAmelCase			:	Any										=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    __snake_case		,      batch_size=__snake_case		,      seq_length=__snake_case		,      is_pair=__snake_case		,      framework=__snake_case		)

								elif self.task == "causal-lm":
															UpperCAmelCase			:	List[Any]										=		self._generate_dummy_inputs_for_causal_lm(
															    __snake_case		,      batch_size=__snake_case		,      seq_length=__snake_case		,      is_pair=__snake_case		,      framework=__snake_case		)
								else:
															UpperCAmelCase			:	Any										=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    __snake_case		,      batch_size=__snake_case		,      seq_length=__snake_case		,      is_pair=__snake_case		,      framework=__snake_case		)

								return common_inputs







	def 					A     (						self      :					Optional[Any]		,      __snake_case      :					str		,      __snake_case      :					Any		,      __snake_case      :					str		,      __snake_case      :					int		)      ->		Optional[Any]:
								if self.task in ["default", "seq2seq-lm"]:
															UpperCAmelCase			:	Tuple										=		super()._flatten_past_key_values_(__snake_case		,      __snake_case		,      __snake_case		,      __snake_case		)
								else:
															UpperCAmelCase			:	Optional[Any]										=		super(__snake_case		,      self		)._flatten_past_key_values_(
															    __snake_case		,      __snake_case		,      __snake_case		,      __snake_case		)







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					Mapping[str, Mapping[int, str]]		,      __snake_case      :					str		)      ->		Dict:
								if direction not in ["inputs", "outputs"]:
															raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""		)

								UpperCAmelCase			:	Dict										=		'''past_key_values''' if direction == '''inputs''' else '''present'''
								UpperCAmelCase			:	Tuple										=		self.num_layers

								UpperCAmelCase			:	List[str]										=		'''past_encoder_sequence'''
								UpperCAmelCase			:	int										=		'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

								for i in range(__snake_case		):
															UpperCAmelCase			:	int										=		{0: '''batch''', 2: decoder_sequence}
															UpperCAmelCase			:	List[str]										=		{0: '''batch''', 2: decoder_sequence}
															UpperCAmelCase			:	Tuple										=		{0: '''batch''', 2: encoder_sequence}
															UpperCAmelCase			:	List[Any]										=		{0: '''batch''', 2: encoder_sequence}










'''simple docstring'''






class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[Any]		,      __snake_case      :					Dict		)      ->		str:
								UpperCAmelCase			:	str										=		val
								# Number of nodes in left subtree
								UpperCAmelCase			:	Optional[int]										=		0
								UpperCAmelCase			:	str										=		None
								UpperCAmelCase			:	Optional[Any]										=		None
								UpperCAmelCase			:	Dict										=		None







	def 					A     (						self      :					Tuple		,      __snake_case      :					Dict		)      ->		Dict:
								assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

								if self.val < other.val:
															UpperCAmelCase			:	Optional[int]										=		self.right
															UpperCAmelCase			:	Optional[Any]										=		None
															if self.right:
																						UpperCAmelCase			:	Tuple										=		other
															UpperCAmelCase			:	int										=		other
															UpperCAmelCase			:	Dict										=		self.left_tree_size * 2 + 1
															return self
								else:
															UpperCAmelCase			:	int										=		other.right
															UpperCAmelCase			:	List[str]										=		None
															if other.right:
																						UpperCAmelCase			:	Dict										=		self
															UpperCAmelCase			:	Optional[Any]										=		self
															UpperCAmelCase			:	Optional[int]										=		other.left_tree_size * 2 + 1
															return other

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					Union[str, Any]		,      __snake_case      :					str=None		,      __snake_case      :					Any=None		,      __snake_case      :					Any=0		)      ->		Dict:
								UpperCAmelCase			:	str										=		heap_size
								UpperCAmelCase			:	Tuple										=		bottom_root
								UpperCAmelCase			:	str										=		min_node







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

								# Empty heaps corner cases
								if other.size == 0:
															return None
								if self.size == 0:
															UpperCAmelCase			:	List[str]										=		other.size
															UpperCAmelCase			:	Union[str, Any]										=		other.bottom_root
															UpperCAmelCase			:	Optional[int]										=		other.min_node
															return None
								# Update size
								UpperCAmelCase			:	Tuple										=		self.size + other.size

								# Update min.node
								if self.min_node.val > other.min_node.val:
															UpperCAmelCase			:	Union[str, Any]										=		other.min_node
								# Merge

								# Order roots by left_subtree_size
								UpperCAmelCase			:	List[str]										=		[]
								UpperCAmelCase							,		UpperCAmelCase			:	Optional[int]										=		self.bottom_root, other.bottom_root
								while i or j:
															if i and ((not j) or i.left_tree_size < j.left_tree_size):
																						combined_roots_list.append((i, True)		)
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
															else:
																						combined_roots_list.append((j, False)		)
																						UpperCAmelCase			:	List[str]										=		j.parent
        # Insert links between them
								for i in range(len(__snake_case		) - 1		):
															if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																						UpperCAmelCase			:	Union[str, Any]										=		combined_roots_list[i + 1][0]
																						UpperCAmelCase			:	Optional[Any]										=		combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
								UpperCAmelCase			:	Optional[int]										=		combined_roots_list[0][0]
								while i.parent:
															if (
															    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
															) or (
															    i.left_tree_size == i.parent.left_tree_size
															    and i.left_tree_size != i.parent.parent.left_tree_size
															):
																						# Neighbouring Nodes
																						UpperCAmelCase			:	Optional[int]										=		i.left
																						UpperCAmelCase			:	List[Any]										=		i.parent.parent

																						# Merging trees
																						UpperCAmelCase			:	Optional[int]										=		i.merge_trees(i.parent		)

																						# Updating links
																						UpperCAmelCase			:	Any										=		previous_node
																						UpperCAmelCase			:	str										=		next_node
																						if previous_node:
																													UpperCAmelCase			:	Union[str, Any]										=		i
																						if next_node:
																													UpperCAmelCase			:	Optional[Any]										=		i
															else:
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
        # Updating self.bottom_root
								while i.left:
															UpperCAmelCase			:	str										=		i.left
								UpperCAmelCase			:	Optional[int]										=		i

								# Update other
								UpperCAmelCase			:	str										=		self.size
								UpperCAmelCase			:	Union[str, Any]										=		self.bottom_root
								UpperCAmelCase			:	Union[str, Any]										=		self.min_node

								# Return the merged heap
								return self







	def 					A     (						self      :					List[Any]		,      __snake_case      :					List[str]		)      ->		List[str]:
								if self.size == 0:
															UpperCAmelCase			:	List[Any]										=		Node(__snake_case		)
															UpperCAmelCase			:	List[str]										=		1
															UpperCAmelCase			:	str										=		self.bottom_root
								else:
															# Create new node
															UpperCAmelCase			:	int										=		Node(__snake_case		)

															# Update size
															self.size += 1

															# update min_node
															if val < self.min_node.val:
																						UpperCAmelCase			:	Optional[int]										=		new_node
															# Put new_node as a bottom_root in heap
															UpperCAmelCase			:	Optional[Any]										=		new_node
															UpperCAmelCase			:	Optional[Any]										=		self.bottom_root
															UpperCAmelCase			:	Union[str, Any]										=		new_node

															# Consecutively merge roots with same left_tree_size
															while (
															    self.bottom_root.parent
															    and self.bottom_root.left_tree_size
															    == self.bottom_root.parent.left_tree_size
															):
																						# Next node
																						UpperCAmelCase			:	List[str]										=		self.bottom_root.parent.parent

																						# Merge
																						UpperCAmelCase			:	int										=		self.bottom_root.merge_trees(self.bottom_root.parent		)

																						# Update Links
																						UpperCAmelCase			:	str										=		next_node
																						UpperCAmelCase			:	int										=		None
																						if next_node:
																													UpperCAmelCase			:	Dict										=		self.bottom_root







	def 					A     (						self      :					Tuple		)      ->		List[str]:
								return self.min_node.val







	def 					A     (						self      :					Optional[Any]		)      ->		int:
								return self.size == 0







	def 					A     (						self      :					List[str]		)      ->		int:
								UpperCAmelCase			:	Tuple										=		self.min_node.val

								# Last element in heap corner case
								if self.size == 1:
															# Update size
															UpperCAmelCase			:	Tuple										=		0

															# Update bottom root
															UpperCAmelCase			:	Optional[int]										=		None

															# Update min_node
															UpperCAmelCase			:	int										=		None

															return min_value
								# No right subtree corner case
								# The structure of the tree implies that this should be the bottom root
								# and there is at least one other root
								if self.min_node.right is None:
															# Update size
															self.size -= 1

															# Update bottom root
															UpperCAmelCase			:	Optional[Any]										=		self.bottom_root.parent
															UpperCAmelCase			:	List[Any]										=		None

															# Update min_node
															UpperCAmelCase			:	int										=		self.bottom_root
															UpperCAmelCase			:	Any										=		self.bottom_root.parent
															while i:
																						if i.val < self.min_node.val:
																													UpperCAmelCase			:	Dict										=		i
																						UpperCAmelCase			:	Union[str, Any]										=		i.parent
															return min_value
								# General case
								# Find the BinomialHeap of the right subtree of min_node
								UpperCAmelCase			:	List[str]										=		self.min_node.right
								UpperCAmelCase			:	Tuple										=		None
								UpperCAmelCase			:	Any										=		bottom_of_new
								UpperCAmelCase			:	str										=		1

								# Size, min_node and bottom_root
								while bottom_of_new.left:
															UpperCAmelCase			:	Tuple										=		size_of_new * 2 + 1
															UpperCAmelCase			:	Union[str, Any]										=		bottom_of_new.left
															if bottom_of_new.val < min_of_new.val:
																						UpperCAmelCase			:	Optional[int]										=		bottom_of_new
        # Corner case of single root on top left path
								if (not self.min_node.left) and (not self.min_node.parent):
															UpperCAmelCase			:	str										=		size_of_new
															UpperCAmelCase			:	str										=		bottom_of_new
															UpperCAmelCase			:	List[str]										=		min_of_new
															# print("Single root, multiple nodes case")
															return min_value
								# Remaining cases
								# Construct heap of right subtree
								UpperCAmelCase			:	int										=		BinomialHeap(
								    bottom_root=__snake_case		,      min_node=__snake_case		,      heap_size=__snake_case		)

								# Update size
								UpperCAmelCase			:	Union[str, Any]										=		self.size - 1 - size_of_new

								# Neighbour nodes
								UpperCAmelCase			:	Any										=		self.min_node.left
								UpperCAmelCase			:	Tuple										=		self.min_node.parent

								# Initialize new bottom_root and min_node
								UpperCAmelCase			:	Optional[int]										=		previous_node or next_node
								UpperCAmelCase			:	int										=		next_node

								# Update links of previous_node and search below for new min_node and
								# bottom_root
								if previous_node:
															UpperCAmelCase			:	List[Any]										=		next_node

															# Update bottom_root and search for min_node below
															UpperCAmelCase			:	Optional[Any]										=		previous_node
															UpperCAmelCase			:	Optional[Any]										=		previous_node
															while self.bottom_root.left:
																						UpperCAmelCase			:	Any										=		self.bottom_root.left
																						if self.bottom_root.val < self.min_node.val:
																													UpperCAmelCase			:	Tuple										=		self.bottom_root
								if next_node:
															UpperCAmelCase			:	Tuple										=		previous_node

															# Search for new min_node above min_node
															UpperCAmelCase			:	Tuple										=		next_node
															while i:
																						if i.val < self.min_node.val:
																													UpperCAmelCase			:	List[str]										=		i
																						UpperCAmelCase			:	Optional[int]										=		i.parent
        # Merge heaps
								self.merge_heaps(__snake_case		)

								return min_value







	def 					A     (						self      :					Union[str, Any]		)      ->		Union[str, Any]:
								UpperCAmelCase			:	Any										=		self.bottom_root
								while top_root.parent:
															UpperCAmelCase			:	Optional[Any]										=		top_root.parent
								# preorder
								UpperCAmelCase			:	List[Any]										=		[]
								self.__traversal(__snake_case		,      __snake_case		)
								return heap_pre_order







	def 					A     (						self      :					List[str]		,      __snake_case      :					Tuple		,      __snake_case      :					int		,      __snake_case      :					Union[str, Any]=0		)      ->		Optional[int]:
								if curr_node:
															preorder.append((curr_node.val, level)		)
															self.__traversal(curr_node.left		,      __snake_case		,      level + 1		)
															self.__traversal(curr_node.right		,      __snake_case		,      level + 1		)
								else:
															preorder.append(('''#''', level)		)







	def __str__(						self      :					Tuple		)      ->		Union[str, Any]:
								if self.is_empty():
															return ""
								UpperCAmelCase			:	Dict										=		self.pre_order()

								return "\n".join(('''-''' * level + str(__snake_case		)) for value, level in preorder_heap		)


# Unit Tests
if __name__ == "__main__":
						import doctest

						doctest.testmod()





