




from collections.abc import Iterable
from typing import Any







class __snake_case			:







   def __init__(      self		:						Tuple					,						_snake_case		:						int | None = None):

        """simple docstring"""


        UpperCAmelCase_          =							value
        UpperCAmelCase_          =							None  # Added in order to delete a node easier
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None







   def __repr__(      self		:						Dict):

        """simple docstring"""


        from pprint import pformat

        if self.left is None and self.right is None:
             return str(self.value)
        return pformat({F"""{self.value}""": (self.left, self.right)}					,						indent=1)







class __snake_case			:







   def __init__(      self		:						int					,						_snake_case		:						Node | None = None):

        """simple docstring"""


        UpperCAmelCase_          =							root







   def __str__(      self		:						List[str]):

        """simple docstring"""


        return str(self.root)







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Node					,						_snake_case		:						Node | None):

        """simple docstring"""


        if new_children is not None:  # reset its kids
             UpperCAmelCase_          =							node.parent
        if node.parent is not None:  # reset its parent
             if self.is_right(_snake_case):  # If it is the right children
                  UpperCAmelCase_          =							new_children
             else:
                  UpperCAmelCase_          =							new_children
        else:
             UpperCAmelCase_          =							new_children







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Node):

        """simple docstring"""


        if node.parent and node.parent.right:
             return node == node.parent.right
        return False







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        return self.root is None







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							Node(_snake_case)  # create a new Node
        if self.empty():  # if Tree is empty
             UpperCAmelCase_          =							new_node  # set its root
        else:  # Tree is not empty
             UpperCAmelCase_          =							self.root  # from root
             if parent_node is None:
                  return
             while True:  # While we don't get to a leaf
                  if value < parent_node.value:  # We go left
                       if parent_node.left is None:
                            UpperCAmelCase_          =							new_node  # We insert the new node in a leaf
                            break
                       else:
                            UpperCAmelCase_          =							parent_node.left
                  else:
                       if parent_node.right is None:
                            UpperCAmelCase_          =							new_node
                            break
                       else:
                            UpperCAmelCase_          =							parent_node.right
             UpperCAmelCase_          =							parent_node







   def lowerCamelCase						(      self		:						Any					,						*_snake_case		:						Union[str, Any]):

        """simple docstring"""


        for value in values:
             self.__insert(_snake_case)







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						List[str]):

        """simple docstring"""


        if self.empty():
             raise IndexError('''Warning: Tree is empty! please use another.''')
        else:
             UpperCAmelCase_          =							self.root
             # use lazy evaluation here to avoid NoneType Attribute error
             while node is not None and node.value is not value:
                  UpperCAmelCase_          =							node.left if value < node.value else node.right
             return node







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Node | None = None):

        """simple docstring"""


        if node is None:
             if self.root is None:
                  return None
             UpperCAmelCase_          =							self.root

        if not self.empty():
             while node.right is not None:
                  UpperCAmelCase_          =							node.right
        return node







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Node | None = None):

        """simple docstring"""


        if node is None:
             UpperCAmelCase_          =							self.root
        if self.root is None:
             return None
        if not self.empty():
             UpperCAmelCase_          =							self.root
             while node.left is not None:
                  UpperCAmelCase_          =							node.left
        return node







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.search(_snake_case)  # Look for the node with that label
        if node is not None:
             if node.left is None and node.right is None:  # If it has no children
                  self.__reassign_nodes(_snake_case					,						_snake_case)
             elif node.left is None:  # Has only right children
                  self.__reassign_nodes(_snake_case					,						node.right)
             elif node.right is None:  # Has only left children
                  self.__reassign_nodes(_snake_case					,						node.left)
             else:
                  UpperCAmelCase_          =							self.get_max(
                      node.left)  # Gets the max value of the left branch
                  self.remove(tmp_node.value)  # type: ignore
                  UpperCAmelCase_          =							(
                      tmp_node.value  # type: ignore
                  )  # Assigns the value to the node to delete and keep tree structure







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Node | None):

        """simple docstring"""


        if node is not None:
             yield node  # Preorder Traversal
             yield from self.preorder_traverse(node.left)
             yield from self.preorder_traverse(node.right)







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						str=None):

        """simple docstring"""


        if traversal_function is None:
             return self.preorder_traverse(self.root)
        else:
             return traversal_function(self.root)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						list					,						_snake_case		:						Node | None):

        """simple docstring"""


        if node:
             self.inorder(_snake_case					,						node.left)
             arr.append(node.value)
             self.inorder(_snake_case					,						node.right)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						int					,						_snake_case		:						Node):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        self.inorder(_snake_case					,						_snake_case)  # append all values to list using inorder traversal
        return arr[k - 1]







def    A						(__A   :    Node | None       )   ->      list[Node]:







     """simple docstring"""


     UpperCAmelCase_          =							[]
     if curr_node is not None:
          UpperCAmelCase_          =							postorder(curr_node.left       ) + postorder(curr_node.right       ) + [curr_node]
     return node_list


def    A						()   ->      None:







     """simple docstring"""


     UpperCAmelCase_          =							(8, 3, 6, 1, 10, 14, 13, 4, 7)
     UpperCAmelCase_          =							BinarySearchTree()
     for i in testlist:
          t.insert(__A       )

     # Prints all the elements of the list in order traversal
     print(__A       )

     if t.search(6       ) is not None:
          print('''The value 6 exists'''       )
     else:
          print('''The value 6 doesn\'t exist'''       )

     if t.search(-1       ) is not None:
          print('''The value -1 exists'''       )
     else:
          print('''The value -1 doesn\'t exist'''       )

     if not t.empty():
          print('''Max Value: '''   ,				t.get_max().value       )  # type: ignore
          print('''Min Value: '''   ,				t.get_min().value       )  # type: ignore

     for i in testlist:
          t.remove(__A       )
          print(__A       )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)












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
snake_case_ : List[Any]    		=       data_utils.TransfoXLTokenizer
snake_case_ : int    		=       data_utils.TransfoXLCorpus
snake_case_ : List[Any]    		=       data_utils
snake_case_ : int    		=       data_utils


def    A						(__A   :    Dict   ,				__A   :    List[Any]   ,				__A   :    Union[str, Any]   ,				__A   :    Tuple       )   ->      Union[str, Any]:







     """simple docstring"""


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

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

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

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

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


if __name__ == "__main__":
    snake_case_ : List[str]    		=       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.",
    )
    snake_case_ : int    		=       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,
    )






