




from __future__ import annotations

import queue







class __snake_case			:







   def __init__(      self		:						Dict					,						_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							data
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None







def    A						()   ->      TreeNode:







     """simple docstring"""


     print('''\n********Press N to stop entering at any point of time********\n'''       )
     UpperCAmelCase_          =							input('''Enter the value of the root node: '''       ).strip().lower()
     UpperCAmelCase_          =							queue.Queue()
     UpperCAmelCase_          =							TreeNode(int(__A       )       )
     q.put(__A       )
     while not q.empty():
          UpperCAmelCase_          =							q.get()
          UpperCAmelCase_          =							F"""Enter the left node of {node_found.data}: """
          UpperCAmelCase_          =							input(__A       ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          UpperCAmelCase_          =							TreeNode(int(__A       )       )
          UpperCAmelCase_          =							left_node
          q.put(__A       )
          UpperCAmelCase_          =							F"""Enter the right node of {node_found.data}: """
          UpperCAmelCase_          =							input(__A       ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          UpperCAmelCase_          =							TreeNode(int(__A       )       )
          UpperCAmelCase_          =							right_node
          q.put(__A       )
     raise


def    A						(__A   :    TreeNode       )   ->      None:







     """simple docstring"""


     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:







     """simple docstring"""


     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:







     """simple docstring"""


     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:







     """simple docstring"""


     if not isinstance(__A   ,				__A       ) or not node:
          return
     UpperCAmelCase_          =							queue.Queue()
     q.put(__A       )
     while not q.empty():
          UpperCAmelCase_          =							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:







     """simple docstring"""


     if not isinstance(__A   ,				__A       ) or not node:
          return
     UpperCAmelCase_          =							queue.Queue()
     q.put(__A       )
     while not q.empty():
          UpperCAmelCase_          =							[]
          while not q.empty():
               UpperCAmelCase_          =							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:







     """simple docstring"""


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


def    A						(__A   :    TreeNode       )   ->      None:







     """simple docstring"""


     if not isinstance(__A   ,				__A       ) or not node:
          return
     UpperCAmelCase_          =							[]
     UpperCAmelCase_          =							node
     while n or stack:
          while n:
               stack.append(__A       )
               UpperCAmelCase_          =							n.left
          UpperCAmelCase_          =							stack.pop()
          print(n.data   ,				end=''','''       )
          UpperCAmelCase_          =							n.right


def    A						(__A   :    TreeNode       )   ->      None:







     """simple docstring"""


     if not isinstance(__A   ,				__A       ) or not node:
          return
     UpperCAmelCase_     ,       UpperCAmelCase_          =							[], []
     UpperCAmelCase_          =							node
     stacka.append(__A       )
     while stacka:  # to find the reversed order of post order, store it in stack2
          UpperCAmelCase_          =							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   :    Optional[Any]=50   ,				__A   :    Union[str, Any]="*"       )   ->      str:







     """simple docstring"""


     if not s:
          return "\n" + width * char
     UpperCAmelCase_     ,       UpperCAmelCase_          =							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"))

    snake_case_ : TreeNode    		=       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())












import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__A        =    data_utils.TransfoXLTokenizer
__A        =    data_utils.TransfoXLCorpus
__A        =    data_utils
__A        =    data_utils







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(UpperCamelCase__ ,     'rb'      ) as fp:
												__lowerCamelCase		       =    pickle.load(UpperCamelCase__ ,     encoding='latin1'      )
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
								print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""      )
								__lowerCamelCase		       =    corpus.vocab.__dict__
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

								__lowerCamelCase		       =    corpus.__dict__
								corpus_dict_no_vocab.pop('vocab' ,     UpperCamelCase__      )
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + CORPUS_NAME
								print(F"""Save dataset to {pytorch_dataset_dump_path}"""      )
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )

								print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												__lowerCamelCase		       =    TransfoXLConfig()
								else:
												__lowerCamelCase		       =    TransfoXLConfig.from_json_file(UpperCamelCase__      )
								print(F"""Building PyTorch model from configuration: {config}"""      )
								__lowerCamelCase		       =    TransfoXLLMHeadModel(UpperCamelCase__      )

								__lowerCamelCase		       =    load_tf_weights_in_transfo_xl(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								# Save pytorch-model
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""Save PyTorch model to {os.path.abspath(UpperCamelCase__      )}"""      )
								torch.save(model.state_dict() ,     UpperCamelCase__      )
								print(F"""Save configuration file to {os.path.abspath(UpperCamelCase__      )}"""      )
								with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8'      ) as f:
												f.write(config.to_json_string()      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the folder to store the PyTorch model or dataset/vocab.",
					)
					parser.add_argument(
					    "--tf_checkpoint_path",
					    default="",
					    type=str,
					    help="An optional path to a TensorFlow checkpoint path to be converted.",
					)
					parser.add_argument(
					    "--transfo_xl_config_file",
					    default="",
					    type=str,
					    help=(
					        "An optional config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--transfo_xl_dataset_file",
					    default="",
					    type=str,
					    help="An optional dataset file to be converted in a vocabulary.",
					)
					__A        =    parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)


