



from __future__ import annotations

_lowercase		:				str      		=tuple[int, int, int]
_lowercase		:				List[str]      		=tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
_lowercase		:				Optional[int]      		="ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
_lowercase		:				Union[str, Any]      		="EGZWVONAHDCLFQMSIPJBYUKXTR"
_lowercase		:				Optional[int]      		="FOBHMDKEXQNRAULPGSJVTYICZW"
_lowercase		:				Optional[int]      		="ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
_lowercase		:				List[Any]      		={
    "A": "N",
    "N": "A",
    "B": "O",
    "O": "B",
    "C": "P",
    "P": "C",
    "D": "Q",
    "Q": "D",
    "E": "R",
    "R": "E",
    "F": "S",
    "S": "F",
    "G": "T",
    "T": "G",
    "H": "U",
    "U": "H",
    "I": "V",
    "V": "I",
    "J": "W",
    "W": "J",
    "K": "X",
    "X": "K",
    "L": "Y",
    "Y": "L",
    "M": "Z",
    "Z": "M",
}

# -------------------------- extra rotors --------------------------
_lowercase		:				str      		="RMDJXFUWGISLHVTCQNKYPBEZOA"
_lowercase		:				int      		="SGLCPQWZHKXAREONTFBVIYJUDM"
_lowercase		:				Any      		="HVSICLTYKQUBXDWAJZOMFGPREN"
_lowercase		:				Union[str, Any]      		="RZWQHFMVDBKICJLNTUXAGYPSOE"
_lowercase		:				int      		="LFKIJODBEGAMQPXVUHYSTCZRWN"
_lowercase		:				int      		="KOAEGVDHXPQZMLFTYWJNBRCIUS"

def      lowerCAmelCase_					(  _lowercase    :       RotorPositionT						,       _lowercase    :       RotorSelectionT						,       _lowercase    :       str)							->	tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:







			"""simple docstring"""
			# Checks if there are 3 unique rotors

			if (unique_rotsel := len(set(_lowercase))) < 3:
						a__       :						Dict							=  F'''Please use 3 unique rotors (not {unique_rotsel})'''
						raise Exception(_lowercase)

			# Checks if rotor positions are valid
			a__	,						a__	,						a__       :						Optional[int]							=  rotpos
			if not 0 < rotorposa <= len(_lowercase):
						a__       :						Tuple							=  F'''First rotor position is not within range of 1..26 ({rotorposa}'''
						raise ValueError(_lowercase)
			if not 0 < rotorposa <= len(_lowercase):
						a__       :						Union[str, Any]							=  F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
						raise ValueError(_lowercase)
			if not 0 < rotorposa <= len(_lowercase):
						a__       :						Any							=  F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
						raise ValueError(_lowercase)

			# Validates string and returns dict
			a__       :						List[Any]							=  _plugboard(_lowercase)

			return rotpos, rotsel, pbdict

def      lowerCAmelCase_					(  _lowercase    :       str)							->	dict[str, str]:







			"""simple docstring"""

			# tests the input string if it
			# a) is type string
			# b) has even length (so pairs can be made)
			if not isinstance(_lowercase						,       _lowercase):
						a__       :						str							=  F'''Plugboard setting isn\'t type string ({type(_lowercase)})'''
						raise TypeError(_lowercase)
			elif len(_lowercase) % 2 != 0:
						a__       :						Any							=  F'''Odd number of symbols ({len(_lowercase)})'''
						raise Exception(_lowercase)
			elif pbstring == "":
						return {}

			pbstring.replace(""" """						,       """""")

			# Checks if all characters are unique
			a__       :						Union[str, Any]							=  set()
			for i in pbstring:
						if i not in abc:
									a__       :						str							=  F'''\'{i}\' not in list of symbols'''
									raise Exception(_lowercase)
						elif i in tmppbl:
									a__       :						Optional[Any]							=  F'''Duplicate symbol ({i})'''
									raise Exception(_lowercase)
						else:
									tmppbl.add(_lowercase)
			del tmppbl

			# Created the dictionary
			a__       :						Dict							=  {}
			for j in range(0						,       len(_lowercase) - 1						,       2):
						a__       :						Optional[Any]							=  pbstring[j + 1]
						a__       :						Optional[Any]							=  pbstring[j]

			return pb

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       RotorPositionT						,       _lowercase    :       RotorSelectionT = (rotora, rotora, rotora)						,       _lowercase    :       str = ""						,       )							->	str:







			"""simple docstring"""
			a__       :						str							=  text.upper()
			a__	,						a__	,						a__       :						str							=  _validator(
			    _lowercase						,       _lowercase						,       plugb.upper())

			a__	,						a__	,						a__       :						List[str]							=  rotor_position
			a__	,						a__	,						a__       :						Tuple							=  rotor_selection
			rotorposa -= 1
			rotorposa -= 1
			rotorposa -= 1

			a__       :						Union[str, Any]							=  []

			# encryption/decryption process --------------------------
			for symbol in text:
						if symbol in abc:
									# 1st plugboard --------------------------
									if symbol in plugboard:
												a__       :						List[str]							=  plugboard[symbol]

									# rotor ra --------------------------
									a__       :						Union[str, Any]							=  abc.index(_lowercase) + rotorposa
									a__       :						List[str]							=  rotora[index % len(_lowercase)]

									# rotor rb --------------------------
									a__       :						Dict							=  abc.index(_lowercase) + rotorposa
									a__       :						Union[str, Any]							=  rotora[index % len(_lowercase)]

									# rotor rc --------------------------
									a__       :						Optional[int]							=  abc.index(_lowercase) + rotorposa
									a__       :						List[str]							=  rotora[index % len(_lowercase)]

									# reflector --------------------------
									# this is the reason you don't need another machine to decipher

									a__       :						int							=  reflector[symbol]

									# 2nd rotors
									a__       :						Optional[Any]							=  abc[rotora.index(_lowercase) - rotorposa]
									a__       :						List[str]							=  abc[rotora.index(_lowercase) - rotorposa]
									a__       :						str							=  abc[rotora.index(_lowercase) - rotorposa]

									# 2nd plugboard
									if symbol in plugboard:
												a__       :						Any							=  plugboard[symbol]

									# moves/resets rotor positions
									rotorposa += 1
									if rotorposa >= len(_lowercase):
												a__       :						Dict							=  0
												rotorposa += 1
									if rotorposa >= len(_lowercase):
												a__       :						Union[str, Any]							=  0
												rotorposa += 1
									if rotorposa >= len(_lowercase):
												a__       :						Union[str, Any]							=  0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
						result.append(_lowercase)

			return "".join(_lowercase)


if __name__ == "__main__":
		_lowercase		:				Optional[int]      		="This is my Python script that emulates the Enigma machine from WWII."
		_lowercase		:				Optional[int]      		=(1, 1, 1)
		_lowercase		:				Union[str, Any]      		="pictures"
		_lowercase		:				str      		=(rotora, rotora, rotora)
		_lowercase		:				Any      		=enigma(message, rotor_pos, rotor_sel, pb)

		print("Encrypted message:", en)
		print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))









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
_lowercase		:				Union[str, Any]      		=data_utils.TransfoXLTokenizer
_lowercase		:				Tuple      		=data_utils.TransfoXLCorpus
_lowercase		:				Optional[int]      		=data_utils
_lowercase		:				int      		=data_utils

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       List[Any]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Optional[Any])							->	List[Any]:







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

						a__       :						Optional[int]							=  corpus.__dict__
						corpus_dict_no_vocab.pop("""vocab"""						,       _lowercase)
						a__       :						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
						a__       :						Optional[Any]							=  os.path.abspath(_lowercase)
						a__       :						str							=  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 == "":
									a__       :						List[str]							=  TransfoXLConfig()
						else:
									a__       :						Any							=  TransfoXLConfig.from_json_file(_lowercase)
						print(F'''Building PyTorch model from configuration: {config}''')
						a__       :						Any							=  TransfoXLLMHeadModel(_lowercase)

						a__       :						Optional[Any]							=  load_tf_weights_in_transfo_xl(_lowercase						,       _lowercase						,       _lowercase)
						# Save pytorch-model
						a__       :						Tuple							=  os.path.join(_lowercase						,       _lowercase)
						a__       :						Optional[int]							=  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__":
		_lowercase		:				Tuple      		=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.",
		)
		_lowercase		:				Dict      		=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,
		)




