




"""simple docstring"""

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

				snake_case		:    Any             =					corpus.__dict__
				corpus_dict_no_vocab.pop("vocab"							,  _lowercase )
				snake_case		:    Tuple             =					pytorch_dump_folder_path + '/' + CORPUS_NAME
				print(F'''Save dataset to {pytorch_dataset_dump_path}''' )
				torch.save(_lowercase							,  _lowercase )

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				snake_case		:    List[str]             =					os.path.abspath(_lowercase )
				snake_case		:    Union[str, Any]             =					os.path.abspath(_lowercase )

				print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''' )
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						snake_case		:    int             =					TransfoXLConfig()
				else:
						snake_case		:    Any             =					TransfoXLConfig.from_json_file(_lowercase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				snake_case		:    List[str]             =					TransfoXLLMHeadModel(_lowercase )

				snake_case		:    Union[str, Any]             =					load_tf_weights_in_transfo_xl(_lowercase							,  _lowercase							,  _lowercase )
				# Save pytorch-model
				snake_case		:    str             =					os.path.join(_lowercase							,  _lowercase )
				snake_case		:    Optional[Any]             =					os.path.join(_lowercase							,  _lowercase )
				print(F'''Save PyTorch model to {os.path.abspath(_lowercase )}''' )
				torch.save(model.state_dict()							,  _lowercase )
				print(F'''Save configuration file to {os.path.abspath(_lowercase )}''' )
				with open(_lowercase							,  "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,
							)








from __future__ import annotations

import math
import random
from typing import Any


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						Dict			):
			lowercase__					:      list[Any]      =		[]
			lowercase__					:      int      =		0
			lowercase__					:      int      =		0

	def    snake_case__(						self:						Optional[int]			):
			return self.head == self.tail

	def    snake_case__(						self:						Any,					lowerCamelCase_:						Any			):
			self.data.append(lowerCamelCase_			)
			lowercase__					:      int      =		self.tail + 1

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      Optional[Any]      =		self.data[self.head]
			lowercase__					:      Optional[int]      =		self.head + 1
			return ret

	def    snake_case__(						self:						List[Any]			):
			return self.tail - self.head





	def    snake_case__(						self:						List[Any]			):
			print(self.data			)
			print('**************'			)
			print(self.data[self.head : self.tail]			)


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						Any			):
			lowercase__					:      List[Any]      =		data
			lowercase__					:      MyNode | None      =		None
			lowercase__					:      MyNode | None      =		None
			lowercase__					:      int      =		1

	def    snake_case__(						self:						Optional[Any]			):
			return self.data

	def    snake_case__(						self:						List[str]			):
			return self.left

	def    snake_case__(						self:						Optional[int]			):
			return self.right

	def    snake_case__(						self:						Optional[Any]			):
			return self.height

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		data

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						MyNode | None			):
			lowercase__					:      Dict      =		node

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						MyNode | None			):
			lowercase__					:      Optional[Any]      =		node





	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						int			):
			lowercase__					:      Union[str, Any]      =		height


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode | None			)   ->							int:




		'''simple docstring'''
		if node is None:
				return 0
		return node.get_height()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							int:




		'''simple docstring'''
		if a > b:
				return a
		return b


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		print('left rotation node:'   ,    node.get_data()			)
		lowercase__					:      Optional[int]      =		node.get_left()
		assert ret is not None
		node.set_left(ret.get_right()			)
		ret.set_right(_lowercase			)
		lowercase__					:      List[str]      =		my_max(get_height(node.get_right()			)   ,    get_height(node.get_left()			)			) + 1
		node.set_height(_lowercase			)
		lowercase__					:      Optional[Any]      =		my_max(get_height(ret.get_right()			)   ,    get_height(ret.get_left()			)			) + 1
		ret.set_height(_lowercase			)
		return ret


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		print('right rotation node:'   ,    node.get_data()			)
		lowercase__					:      Union[str, Any]      =		node.get_right()
		assert ret is not None
		node.set_right(ret.get_left()			)
		ret.set_left(_lowercase			)
		lowercase__					:      str      =		my_max(get_height(node.get_right()			)   ,    get_height(node.get_left()			)			) + 1
		node.set_height(_lowercase			)
		lowercase__					:      List[str]      =		my_max(get_height(ret.get_right()			)   ,    get_height(ret.get_left()			)			) + 1
		ret.set_height(_lowercase			)
		return ret


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		lowercase__					:      str      =		node.get_left()
		assert left_child is not None
		node.set_left(left_rotation(_lowercase			)			)
		return right_rotation(_lowercase			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		node.get_right()
		assert right_child is not None
		node.set_right(right_rotation(_lowercase			)			)
		return left_rotation(_lowercase			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode | None   ,    _lowercase     : Any			)   ->							MyNode | None:




		'''simple docstring'''
		if node is None:
				return MyNode(_lowercase			)
		if data < node.get_data():
				node.set_left(insert_node(node.get_left()   ,    _lowercase			)			)
				if (
				    get_height(node.get_left()			) - get_height(node.get_right()			) == 2
				):  # an unbalance detected
						lowercase__					:      Tuple      =		node.get_left()
						assert left_child is not None
						if (
						    data < left_child.get_data()
						):  # new node is the left child of the left child
								lowercase__					:      Optional[Any]      =		right_rotation(_lowercase			)
						else:
								lowercase__					:      List[str]      =		lr_rotation(_lowercase			)
		else:
				node.set_right(insert_node(node.get_right()   ,    _lowercase			)			)
				if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
						lowercase__					:      Dict      =		node.get_right()
						assert right_child is not None
						if data < right_child.get_data():
								lowercase__					:      Dict      =		rl_rotation(_lowercase			)
						else:
								lowercase__					:      Tuple      =		left_rotation(_lowercase			)
		lowercase__					:      List[Any]      =		my_max(get_height(node.get_right()			)   ,    get_height(node.get_left()			)			) + 1
		node.set_height(_lowercase			)
		return node


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							Any:




		'''simple docstring'''
		while True:
				lowercase__					:      List[Any]      =		root.get_right()
				if right_child is None:
						break
				lowercase__					:      List[str]      =		right_child
		return root.get_data()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							Any:




		'''simple docstring'''
		while True:
				lowercase__					:      Any      =		root.get_left()
				if left_child is None:
						break
				lowercase__					:      List[str]      =		left_child
		return root.get_data()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode   ,    _lowercase     : Any			)   ->							MyNode | None:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		root.get_left()
		lowercase__					:      List[Any]      =		root.get_right()
		if root.get_data() == data:
				if left_child is not None and right_child is not None:
						lowercase__					:      Dict      =		get_left_most(_lowercase			)
						root.set_data(_lowercase			)
						root.set_right(del_node(_lowercase   ,    _lowercase			)			)
				elif left_child is not None:
						lowercase__					:      List[Any]      =		left_child
				elif right_child is not None:
						lowercase__					:      Dict      =		right_child
				else:
						return None
		elif root.get_data() > data:
				if left_child is None:
						print('No such data'			)
						return root
				else:
						root.set_left(del_node(_lowercase   ,    _lowercase			)			)
		else:  # root.get_data() < data
				if right_child is None:
						return root
				else:
						root.set_right(del_node(_lowercase   ,    _lowercase			)			)

		if get_height(_lowercase			) - get_height(_lowercase			) == 2:
				assert right_child is not None
				if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
						lowercase__					:      List[Any]      =		left_rotation(_lowercase			)
				else:
						lowercase__					:      Optional[Any]      =		rl_rotation(_lowercase			)
		elif get_height(_lowercase			) - get_height(_lowercase			) == -2:
				assert left_child is not None
				if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
						lowercase__					:      Optional[int]      =		right_rotation(_lowercase			)
				else:
						lowercase__					:      Dict      =		lr_rotation(_lowercase			)
		lowercase__					:      Any      =		my_max(get_height(root.get_right()			)   ,    get_height(root.get_left()			)			) + 1
		root.set_height(_lowercase			)
		return root


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						str			):
			lowercase__					:      MyNode | None      =		None

	def    snake_case__(						self:						Optional[Any]			):
			return get_height(self.root			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						Any			):
			print('insert:' + str(lowerCamelCase_			)			)
			lowercase__					:      int      =		insert_node(self.root,					lowerCamelCase_			)

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Any			):
			print('delete:' + str(lowerCamelCase_			)			)
			if self.root is None:
					print('Tree is empty!'			)
					return
			lowercase__					:      int      =		del_node(self.root,					lowerCamelCase_			)





	def __str__(						self:						str,					):  # a level traversale, gives a more intuitive look on the tree
			lowercase__					:      Optional[int]      =		''
			lowercase__					:      Any      =		MyQueue()
			q.push(self.root			)
			lowercase__					:      Dict      =		self.get_height()
			if layer == 0:
					return output
			lowercase__					:      Optional[int]      =		0
			while not q.is_empty():
					lowercase__					:      Dict      =		q.pop()
					lowercase__					:      Optional[Any]      =		' ' * int(math.pow(2,					layer - 1			)			)
					output += space
					if node is None:
							output += "*"
							q.push(lowerCamelCase_			)
							q.push(lowerCamelCase_			)
					else:
							output += str(node.get_data()			)
							q.push(node.get_left()			)
							q.push(node.get_right()			)
					output += space
					lowercase__					:      Union[str, Any]      =		cnt + 1
					for i in range(100			):
							if cnt == math.pow(2,					lowerCamelCase_			) - 1:
									lowercase__					:      Optional[int]      =		layer - 1
									if layer == 0:
											output += "\n*************************************"
											return output
									output += "\n"
									break
			output += "\n*************************************"
			return output


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		import doctest

		doctest.testmod()


if __name__ == "__main__":
			_test()
			__UpperCamelCase:	int = AVLtree()
			__UpperCamelCase:	int = list(range(1_0))
			random.shuffle(lst)
			for i in lst:
						t.insert(i)
						print(str(t))
			random.shuffle(lst)
			for i in lst:
						t.del_node(i)
						print(str(t))


