





"""simple docstring"""






from __future__ import annotations

import queue




class  __lowercase	:







					'''simple docstring'''




					def __init__(  self		,	_UpperCAmelCase   ):
									__a       :  str					=     data
									__a       :  List[Any]					=     None
									__a       :  Tuple					=     None






def 			__A					(	)						->						TreeNode:
				print('''\n********Press N to stop entering at any point of time********\n''')
				__a       :  int					=     input('''Enter the value of the root node: ''').strip().lower()
				__a       :  List[str]					=     queue.Queue()
				__a       :  List[str]					=     TreeNode(int(A__))
				q.put(A__)
				while not q.empty():
								__a       :  Dict					=     q.get()
								__a       :  Union[str, Any]					=     F"""Enter the left node of {node_found.data}: """
								__a       :  Union[str, Any]					=     input(A__).strip().lower() or '''n'''
								if check == "n":
												return tree_node
								__a       :  Dict					=     TreeNode(int(A__))
								__a       :  Tuple					=     left_node
								q.put(A__)
								__a       :  Any					=     F"""Enter the right node of {node_found.data}: """
								__a       :  Dict					=     input(A__).strip().lower() or '''n'''
								if check == "n":
												return tree_node
								__a       :  List[Any]					=     TreeNode(int(A__))
								__a       :  Tuple					=     right_node
								q.put(A__)
				raise

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				print(node.data      ,				end=''',''')
				pre_order(node.left)
				pre_order(node.right)

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				in_order(node.left)
				print(node.data      ,				end=''',''')
				in_order(node.right)

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				post_order(node.left)
				post_order(node.right)
				print(node.data      ,				end=''',''')

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				__a       :  Optional[Any]					=     queue.Queue()
				q.put(A__)
				while not q.empty():
								__a       :  str					=     q.get()
								print(node_dequeued.data      ,				end=''',''')
								if node_dequeued.left:
												q.put(node_dequeued.left)
								if node_dequeued.right:
												q.put(node_dequeued.right)

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				__a       :  str					=     queue.Queue()
				q.put(A__)
				while not q.empty():
								__a       :  List[Any]					=     []
								while not q.empty():
												__a       :  Any					=     q.get()
												print(node_dequeued.data      ,				end=''',''')
												if node_dequeued.left:
																list_.append(node_dequeued.left)
												if node_dequeued.right:
																list_.append(node_dequeued.right)
								print()
								for node in list_:
												q.put(A__)

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				__a       :  List[str]					=     []
				__a       :  Optional[int]					=     node
				while n or stack:
								while n:  # start from root node, find its left child
												print(n.data      ,				end=''',''')
												stack.append(A__)
												__a       :  Optional[int]					=     n.left
								# end of while means current node doesn't have left child
								__a       :  int					=     stack.pop()
								# start to traverse its right child
								__a       :  List[Any]					=     n.right

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				__a       :  str					=     []
				__a       :  Union[str, Any]					=     node
				while n or stack:
								while n:
												stack.append(A__)
												__a       :  Optional[int]					=     n.left
								__a       :  Tuple					=     stack.pop()
								print(n.data      ,				end=''',''')
								__a       :  Tuple					=     n.right

def 			__A					(	a_  :TreeNode)						->						None:



				if not isinstance(A__      ,				A__) or not node:
								return
				__a				,  __a       :  int					=     [], []
				__a       :  Optional[int]					=     node
				stacka.append(A__)
				while stacka:  # to find the reversed order of post order, store it in stack2
								__a       :  Union[str, Any]					=     stacka.pop()
								if n.left:
												stacka.append(n.left)
								if n.right:
												stacka.append(n.right)
								stacka.append(A__)
				while stacka:  # pop up from stack2 will be the post order
								print(stacka.pop().data      ,				end=''',''')

def 			__A					(	a_  :str = ""      ,				a_  :Tuple=50      ,				a_  :List[str]="*")						->						str:



				if not s:
								return "\n" + width * char
				__a				,  __a       :  List[Any]					=     divmod(width - len(A__) - 2      ,				2)
				return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(prompt('''Binary Tree Traversals'''))

		A       	=     build_tree()
		print(prompt('''Pre Order Traversal'''))
		pre_order(node)
		print(prompt() + '''\n''')

		print(prompt('''In Order Traversal'''))
		in_order(node)
		print(prompt() + '''\n''')

		print(prompt('''Post Order Traversal'''))
		post_order(node)
		print(prompt() + '''\n''')

		print(prompt('''Level Order Traversal'''))
		level_order(node)
		print(prompt() + '''\n''')

		print(prompt('''Actual Level Order Traversal'''))
		level_order_actual(node)
		print('''*''' * 50 + '''\n''')

		print(prompt('''Pre Order Traversal - Iteration Version'''))
		pre_order_iter(node)
		print(prompt() + '''\n''')

		print(prompt('''In Order Traversal - Iteration Version'''))
		in_order_iter(node)
		print(prompt() + '''\n''')

		print(prompt('''Post Order Traversal - Iteration Version'''))
		post_order_iter(node)
		print(prompt())


# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import torch





class  __lowerCAmelCase     (					TensorFormatter[Mapping, """torch.Tensor""", Mapping]							):




  def __init__(				self							,   lowerCAmelCase=None							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        super().__init__(features=lowerCAmelCase					)
        _lowercase	       =torch_tensor_kwargs
        import torch  # noqa import torch at initialization




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






        import torch

        if isinstance(lowerCAmelCase							,   lowerCAmelCase					) and column:
              if all(
                  isinstance(lowerCAmelCase							,   torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
                  for x in column					):
                    return torch.stack(lowerCAmelCase					)
        return column




  def   A__     (				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        import torch

        if isinstance(lowerCAmelCase							,   (str, bytes, type(lowerCAmelCase					))					):
              return value
        elif isinstance(lowerCAmelCase							,   (np.character, np.ndarray)					) and np.issubdtype(value.dtype							,   np.character					):
              return value.tolist()

        _lowercase	       ={}

        if isinstance(lowerCAmelCase							,   (np.number, np.ndarray)					) and np.issubdtype(value.dtype							,   np.integer					):
              _lowercase	       ={'dtype': torch.intaa}
        elif isinstance(lowerCAmelCase							,   (np.number, np.ndarray)					) and np.issubdtype(value.dtype							,   np.floating					):
              _lowercase	       ={'dtype': torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(lowerCAmelCase							,   PIL.Image.Image					):
                    _lowercase	       =np.asarray(lowerCAmelCase					)
        return torch.tensor(lowerCAmelCase							,   **{**default_dtype, **self.torch_tensor_kwargs}					)




  def   A__     (				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        import torch

        # support for torch, tf, jax etc.
        if hasattr(lowerCAmelCase							,   '__array__'					) and not isinstance(lowerCAmelCase							,   torch.Tensor					):
              _lowercase	       =data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(lowerCAmelCase							,   np.ndarray					):
              if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                    return self._consolidate([self.recursive_tensorize(lowerCAmelCase					) for substruct in data_struct]					)
        elif isinstance(lowerCAmelCase							,   (list, tuple)					):
              return self._consolidate([self.recursive_tensorize(lowerCAmelCase					) for substruct in data_struct]					)
        return self._tensorize(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        return map_nested(self._recursive_tensorize							,   lowerCAmelCase							,   map_list=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> Mapping:
        '''simple docstring'''






        _lowercase	       =self.numpy_arrow_extractor().extract_row(lowerCAmelCase					)
        _lowercase	       =self.python_features_decoder.decode_row(lowerCAmelCase					)
        return self.recursive_tensorize(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> "torch.Tensor":
        '''simple docstring'''






        _lowercase	       =self.numpy_arrow_extractor().extract_column(lowerCAmelCase					)
        _lowercase	       =self.python_features_decoder.decode_column(lowerCAmelCase							,   pa_table.column_names[0]					)
        _lowercase	       =self.recursive_tensorize(lowerCAmelCase					)
        _lowercase	       =self._consolidate(lowerCAmelCase					)
        return column




  def   A__     (				self							,   lowerCAmelCase					)  -> Mapping:
        '''simple docstring'''






        _lowercase	       =self.numpy_arrow_extractor().extract_batch(lowerCAmelCase					)
        _lowercase	       =self.python_features_decoder.decode_batch(lowerCAmelCase					)
        _lowercase	       =self.recursive_tensorize(lowerCAmelCase					)
        for column_name in batch:
              _lowercase	       =self._consolidate(batch[column_name]					)
        return batch


