code: from __future__ import annotations
  
  from collections import defaultdict
  from dataclasses import dataclass
  
  
  @dataclass
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					42
  							lowerCAmelCase__       =					None
  							lowerCAmelCase__       =					None
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							return TreeNode(3 ,TreeNode(9					) ,TreeNode(20 ,TreeNode(15					) ,TreeNode(7					)					)					)
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							def depth_first_search(
  							    _A ,_A ,_A					) -> None:
  														if not root:
  																					return
  
  														if depth == len(_A					):
  																					right_view.append(root.val					)
  
  														depth_first_search(root.right ,depth + 1 ,_A					)
  														depth_first_search(root.left ,depth + 1 ,_A					)
  
  							_lowercase				=	[]
  							if not root:
  														return right_view
  
  							depth_first_search(_A ,0 ,_A					)
  							return right_view
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							def depth_first_search(
  							    _A ,_A ,_A					) -> None:
  														if not root:
  																					return
  
  														if depth == len(_A					):
  																					left_view.append(root.val					)
  
  														depth_first_search(root.left ,depth + 1 ,_A					)
  														depth_first_search(root.right ,depth + 1 ,_A					)
  
  							_lowercase				=	[]
  							if not root:
  														return left_view
  
  							depth_first_search(_A ,0 ,_A					)
  							return left_view
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							def breadth_first_search(_A ,_A					) -> None:
  														_lowercase				=	[(root, 0)]
  														_lowercase				=	defaultdict(_A					)
  
  														while queue:
  																					_lowercase				=	queue.pop(0					)
  																					_lowercase				,       _lowercase				=	first
  
  																					lookup[hd].append(node.val					)
  
  																					if node.left:
  																												queue.append((node.left, hd - 1)					)
  																					if node.right:
  																												queue.append((node.right, hd + 1)					)
  
  														for pair in sorted(lookup.items() ,key=lambda _A					: each[0]					):
  																					top_view.append(pair[1][0]					)
  
  							_lowercase				=	[]
  							if not root:
  														return top_view
  
  							breadth_first_search(_A ,_A					)
  							return top_view
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							from collections import defaultdict
  
  							def breadth_first_search(_A ,_A					) -> None:
  														_lowercase				=	[(root, 0)]
  														_lowercase				=	defaultdict(_A					)
  
  														while queue:
  																					_lowercase				=	queue.pop(0					)
  																					_lowercase				,       _lowercase				=	first
  																					lookup[hd].append(node.val					)
  
  																					if node.left:
  																												queue.append((node.left, hd - 1)					)
  																					if node.right:
  																												queue.append((node.right, hd + 1)					)
  
  														for pair in sorted(lookup.items() ,key=lambda _A					: each[0]					):
  																					bottom_view.append(pair[1][-1]					)
  
  							_lowercase				=	[]
  							if not root:
  														return bottom_view
  
  							breadth_first_search(_A ,_A					)
  							return bottom_view
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  