
'''simple docstring'''

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCAmelCase__ :			str											=		logging.get_logger(__name__)

UpperCAmelCase__ :			str											=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__ :			Optional[Any]											=		{
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase__ :			List[Any]											=		{
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase__ :			Tuple											=		{
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__ :			Union[str, Any]											=		{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
UpperCAmelCase__ :			str											=		{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
UpperCAmelCase__ :			str											=		{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


UpperCAmelCase__ :			Union[str, Any]											=		{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__ :			int											=		{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__ :			Dict											=		{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :int   =  VOCAB_FILES_NAMES
	snake_case__   :Tuple   =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :List[str]   =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :Dict   =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	snake_case__   :Union[str, Any]   =  DPRContextEncoderTokenizer






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Optional[int]   =  VOCAB_FILES_NAMES
	snake_case__   :str   =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :List[Any]   =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :List[str]   =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	snake_case__   :List[Any]   =  DPRQuestionEncoderTokenizer

UpperCAmelCase__ :			List[str]											=		collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

UpperCAmelCase__ :			Optional[int]											=		collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCAmelCase__ :			Dict											=		R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "






@add_start_docstrings(SCREAMING_SNAKE_CASE__     )
class  A  :





	def __call__(       self      :							Union[str, Any]   ,				__magic_name__      :							int   ,				__magic_name__      :							str = None   ,				__magic_name__      :							Optional[Any] = None   ,				__magic_name__      :							Dict = False   ,				__magic_name__      :							List[str] = False   ,				__magic_name__      :							int = None   ,				__magic_name__      :							Tuple = None   ,				__magic_name__      :							Tuple = None   ,				**__magic_name__      :							Dict   ,				):


				"""simple docstring"""

				if titles is None and texts is None:
							return super().__call__(
							    __magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__   ,				max_length=__magic_name__   ,				return_tensors=__magic_name__   ,				return_attention_mask=__magic_name__   ,				**__magic_name__   ,				)
				elif titles is None or texts is None:
							lowerCAmelCase__										=				titles if texts is None else texts
							return super().__call__(
							    __magic_name__   ,				__magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__   ,				max_length=__magic_name__   ,				return_tensors=__magic_name__   ,				return_attention_mask=__magic_name__   ,				**__magic_name__   ,				)
				lowerCAmelCase__										=				titles if not isinstance(__magic_name__   ,				__magic_name__				) else [titles]
				lowerCAmelCase__										=				texts if not isinstance(__magic_name__   ,				__magic_name__				) else [texts]
				lowerCAmelCase__										=				len(__magic_name__				)
				lowerCAmelCase__										=				questions if not isinstance(__magic_name__   ,				__magic_name__				) else [questions] * n_passages
				assert len(__magic_name__				) == len(
				    __magic_name__				), f"""There should be as many titles than texts but got {len(__magic_name__				)} titles and {len(__magic_name__				)} texts."""
				lowerCAmelCase__										=				super().__call__(__magic_name__   ,				__magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__				)['input_ids']
				lowerCAmelCase__										=				super().__call__(__magic_name__   ,				add_special_tokens=__magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__				)['input_ids']
				lowerCAmelCase__										=				{
				    'input_ids': [
				        (encoded_question_and_title + encoded_text)[:max_length]
				        if max_length is not None and truncation
				        else encoded_question_and_title + encoded_text
				        for encoded_question_and_title, encoded_text in zip(__magic_name__   ,				__magic_name__				)
				    ]
				}
				if return_attention_mask is not False:
							lowerCAmelCase__										=				[]
							for input_ids in encoded_inputs["input_ids"]:
										attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
							lowerCAmelCase__										=				attention_mask
				return self.pad(__magic_name__   ,				padding=__magic_name__   ,				max_length=__magic_name__   ,				return_tensors=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							str = 16   ,				__magic_name__      :							Dict = 64   ,				__magic_name__      :							int = 4   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				reader_input['input_ids']
				lowerCAmelCase__										=				reader_output[:3]
				lowerCAmelCase__										=				len(__magic_name__				)
				lowerCAmelCase__										=				sorted(range(__magic_name__				)   ,				reverse=__magic_name__   ,				key=relevance_logits.__getitem__				)
				lowerCAmelCase__										=				[]
				for doc_id in sorted_docs:
							lowerCAmelCase__										=				list(input_ids[doc_id]				)
							# assuming question & title information is at the beginning of the sequence
							lowerCAmelCase__										=				sequence_ids.index(self.sep_token_id   ,				2				) + 1  # second sep id
							if sequence_ids[-1] == self.pad_token_id:
										lowerCAmelCase__										=				sequence_ids.index(self.pad_token_id				)
							else:
										lowerCAmelCase__										=				len(__magic_name__				)

							lowerCAmelCase__										=				self._get_best_spans(
							    start_logits=start_logits[doc_id][passage_offset:sequence_len]   ,				end_logits=end_logits[doc_id][passage_offset:sequence_len]   ,				max_answer_length=__magic_name__   ,				top_spans=__magic_name__   ,				)
							for start_index, end_index in best_spans:
										start_index += passage_offset
										end_index += passage_offset
										nbest_spans_predictions.append(
										    DPRSpanPrediction(
										        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]   ,				relevance_score=relevance_logits[doc_id]   ,				doc_id=__magic_name__   ,				start_index=__magic_name__   ,				end_index=__magic_name__   ,				text=self.decode(sequence_ids[start_index : end_index + 1]				)   ,				)				)
							if len(__magic_name__				) >= num_spans:
										break
				return nbest_spans_predictions[:num_spans]







	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							Any   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Dict   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				[]
				for start_index, start_score in enumerate(__magic_name__				):
							for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]				):
										scores.append(((start_index, start_index + answer_length), start_score + end_score)				)
				lowerCAmelCase__										=				sorted(__magic_name__   ,				key=lambda __magic_name__				: x[1]   ,				reverse=__magic_name__				)
				lowerCAmelCase__										=				[]
				for (start_index, end_index), score in scores:
							assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
							lowerCAmelCase__										=				end_index - start_index + 1
							assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}"""
							if any(
							    start_index <= prev_start_index <= prev_end_index <= end_index
							    or prev_start_index <= start_index <= end_index <= prev_end_index
							    for (prev_start_index, prev_end_index) in chosen_span_intervals				):
										continue
							chosen_span_intervals.append((start_index, end_index)				)

							if len(__magic_name__				) == top_spans:
										break
				return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE__     )
class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__     ):
	snake_case__   :int   =  VOCAB_FILES_NAMES
	snake_case__   :List[Any]   =  READER_PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :Optional[Any]   =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :Optional[int]   =  READER_PRETRAINED_INIT_CONFIGURATION
	snake_case__   :Dict   =  ['''input_ids''', '''attention_mask''']
	snake_case__   :str   =  DPRReaderTokenizer











'''simple docstring'''



from __future__ import annotations

import queue






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						data
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						None
             SCREAMING_SNAKE_CASE   :      List[str]    					=						None




def   lowercase__(			):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
            while not q.empty():
                  SCREAMING_SNAKE_CASE   :      List[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(__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      list[TreeNode]    					=						[]
      SCREAMING_SNAKE_CASE   :      int    					=						node
      while n or stack:
            while n:
                  stack.append(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						n.left
            SCREAMING_SNAKE_CASE   :      Tuple    					=						stack.pop()
            print(n.data      ,end=','    )
            SCREAMING_SNAKE_CASE   :      str    					=						n.right




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					str = ""      ,__UpperCamelCase:					Dict=50      ,__UpperCamelCase:					Optional[int]="*"    ):

      """simple docstring"""






      if not s:
            return "\n" + width * char
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						divmod(width - len(__UpperCamelCase    ) - 2      ,2    )
      return f"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
      import doctest

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

      UpperCamelCase_    	=				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("*" * 5_0 + "\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())


