


'''simple docstring'''



from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass





@dataclass
class      UpperCAmelCase_						:





    """simple docstring"""






    lowerCamelCase						:      int
    lowerCamelCase						:      TreeNode | None							  =    None
    lowerCamelCase						:      TreeNode | None							  =    None







def     UpperCAmelCase__      (			)  ->	TreeNode:
     return TreeNode(3   ,					TreeNode(9							)   ,					TreeNode(20   ,					TreeNode(15							)   ,					TreeNode(7							)							)							)



def     UpperCAmelCase__      (			UpperCAmelCase_					:       TreeNode							)  ->	list[int]:

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

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

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

     __lowerCamelCase     :			list              =	[]
     if not root:
          return right_view

     depth_first_search(UpperCAmelCase_   ,					0   ,					UpperCAmelCase_							)
     return right_view



def     UpperCAmelCase__      (			UpperCAmelCase_					:       TreeNode							)  ->	list[int]:

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

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

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

     __lowerCamelCase     :			list              =	[]
     if not root:
          return left_view

     depth_first_search(UpperCAmelCase_   ,					0   ,					UpperCAmelCase_							)
     return left_view



def     UpperCAmelCase__      (			UpperCAmelCase_					:       TreeNode							)  ->	list[int]:

     def breadth_first_search(UpperCAmelCase_					:       TreeNode   ,					UpperCAmelCase_					:       list[int]							) -> None:
          __lowerCamelCase     :			List[str]              =	[(root, 0)]
          __lowerCamelCase     :			Tuple              =	defaultdict(UpperCAmelCase_							)

          while queue:
               __lowerCamelCase     :			str              =	queue.pop(0							)
               __lowerCamelCase       , __lowerCamelCase     :			int              =	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 UpperCAmelCase_							: each[0]							):
               top_view.append(pair[1][0]							)

     __lowerCamelCase     :			list              =	[]
     if not root:
          return top_view

     breadth_first_search(UpperCAmelCase_   ,					UpperCAmelCase_							)
     return top_view



def     UpperCAmelCase__      (			UpperCAmelCase_					:       TreeNode							)  ->	list[int]:
     from collections import defaultdict

     def breadth_first_search(UpperCAmelCase_					:       TreeNode   ,					UpperCAmelCase_					:       list[int]							) -> None:
          __lowerCamelCase     :			Dict              =	[(root, 0)]
          __lowerCamelCase     :			int              =	defaultdict(UpperCAmelCase_							)

          while queue:
               __lowerCamelCase     :			Optional[int]              =	queue.pop(0							)
               __lowerCamelCase       , __lowerCamelCase     :			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 UpperCAmelCase_							: each[0]							):
               bottom_view.append(pair[1][-1]							)

     __lowerCamelCase     :			list              =	[]
     if not root:
          return bottom_view

     breadth_first_search(UpperCAmelCase_   ,					UpperCAmelCase_							)
     return bottom_view


if __name__ == "__main__":
      import doctest

      doctest.testmod()










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A__	:      Optional[Any]				=						{
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      Optional[Any]				=						[
          """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """XLMProphetNetDecoder""",
          """XLMProphetNetEncoder""",
          """XLMProphetNetForCausalLM""",
          """XLMProphetNetForConditionalGeneration""",
          """XLMProphetNetModel""",
          """XLMProphetNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






