'''simple docstring'''





from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass



@dataclass
class    UpperCamelCase :







    """simple docstring"""





    A			:  int
    A			:  TreeNode | None            =       None
    A			:  TreeNode | None            =       None






def SCREAMING_SNAKE_CASE__   (							)					->							TreeNode:







     """simple docstring"""
     return TreeNode(3							,     TreeNode(9   )							,     TreeNode(20							,     TreeNode(15   )							,     TreeNode(7   )   )   )






def SCREAMING_SNAKE_CASE__   (							snake_case    :							TreeNode   )					->							list[int]:







     """simple docstring"""



     def depth_first_search(
         snake_case    :							TreeNode | None							,     snake_case    :							int							,     snake_case    :							list[int]   ) -> None:
          if not root:
               return

          if depth == len(lowerCAmelCase_   ):
               right_view.append(root.val   )

          depth_first_search(root.right							,     depth + 1							,     lowerCAmelCase_   )
          depth_first_search(root.left							,     depth + 1							,     lowerCAmelCase_   )

     a :				List[str]				=      []
     if not root:
          return right_view

     depth_first_search(lowerCAmelCase_							,     0							,     lowerCAmelCase_   )
     return right_view






def SCREAMING_SNAKE_CASE__   (							snake_case    :							TreeNode   )					->							list[int]:







     """simple docstring"""



     def depth_first_search(
         snake_case    :							TreeNode | None							,     snake_case    :							int							,     snake_case    :							list[int]   ) -> None:
          if not root:
               return

          if depth == len(lowerCAmelCase_   ):
               left_view.append(root.val   )

          depth_first_search(root.left							,     depth + 1							,     lowerCAmelCase_   )
          depth_first_search(root.right							,     depth + 1							,     lowerCAmelCase_   )

     a :				Optional[int]				=      []
     if not root:
          return left_view

     depth_first_search(lowerCAmelCase_							,     0							,     lowerCAmelCase_   )
     return left_view






def SCREAMING_SNAKE_CASE__   (							snake_case    :							TreeNode   )					->							list[int]:







     """simple docstring"""



     def breadth_first_search(snake_case    :							TreeNode							,     snake_case    :							list[int]   ) -> None:
          a :				Optional[int]				=      [(root, 0)]
          a :				Any				=      defaultdict(lowerCAmelCase_   )

          while queue:
               a :				Tuple				=      queue.pop(0   )
               a				,       a :				Union[str, Any]				=      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 snake_case   : each[0]   ):
               top_view.append(pair[1][0]   )

     a :				Optional[Any]				=      []
     if not root:
          return top_view

     breadth_first_search(lowerCAmelCase_							,     lowerCAmelCase_   )
     return top_view






def SCREAMING_SNAKE_CASE__   (							snake_case    :							TreeNode   )					->							list[int]:







     """simple docstring"""
     from collections import defaultdict

     def breadth_first_search(snake_case    :							TreeNode							,     snake_case    :							list[int]   ) -> None:
          a :				int				=      [(root, 0)]
          a :				Tuple				=      defaultdict(lowerCAmelCase_   )

          while queue:
               a :				Dict				=      queue.pop(0   )
               a				,       a :				Any				=      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 snake_case   : each[0]   ):
               bottom_view.append(pair[1][-1]   )

     a :				Union[str, Any]				=      []
     if not root:
          return bottom_view

     breadth_first_search(lowerCAmelCase_							,     lowerCAmelCase_   )
     return bottom_view


if __name__ == "__main__":
      import doctest

      doctest.testmod()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				    =						{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						['''MPNetTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MPNetForMaskedLM''',
	    '''MPNetForMultipleChoice''',
	    '''MPNetForQuestionAnswering''',
	    '''MPNetForSequenceClassification''',
	    '''MPNetForTokenClassification''',
	    '''MPNetLayer''',
	    '''MPNetModel''',
	    '''MPNetPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFMPNetEmbeddings''',
	    '''TFMPNetForMaskedLM''',
	    '''TFMPNetForMultipleChoice''',
	    '''TFMPNetForQuestionAnswering''',
	    '''TFMPNetForSequenceClassification''',
	    '''TFMPNetForTokenClassification''',
	    '''TFMPNetMainLayer''',
	    '''TFMPNetModel''',
	    '''TFMPNetPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
	from .tokenization_mpnet import MPNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mpnet_fast import MPNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mpnet import (
		    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetLayer,
		    MPNetModel,
		    MPNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mpnet import (
		    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFMPNetEmbeddings,
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetMainLayer,
		    TFMPNetModel,
		    TFMPNetPreTrainedModel,
		)

else:
	import sys

	__A				    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




