



"""simple docstring"""
from __future__ import annotations

__a      =				tuple[int, int, int]
__a      =				tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
__a      =				"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
__a      =				"EGZWVONAHDCLFQMSIPJBYUKXTR"
__a      =				"FOBHMDKEXQNRAULPGSJVTYICZW"
__a      =				"ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
__a      =				{
    "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 --------------------------
__a      =				"RMDJXFUWGISLHVTCQNKYPBEZOA"
__a      =				"SGLCPQWZHKXAREONTFBVIYJUDM"
__a      =				"HVSICLTYKQUBXDWAJZOMFGPREN"
__a      =				"RZWQHFMVDBKICJLNTUXAGYPSOE"
__a      =				"LFKIJODBEGAMQPXVUHYSTCZRWN"
__a      =				"KOAEGVDHXPQZMLFTYWJNBRCIUS"
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						if (unique_rotsel := len(set(_lowercase	)	)) < 3:
												snake_case_ :Any						=			f"""Please use 3 unique rotors (not {unique_rotsel})"""
												raise Exception(_lowercase	)

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

						# Validates string and returns dict
						snake_case_ :Optional[Any]						=			_plugboard(_lowercase	)

						return rotpos, rotsel, pbdict
def    A_  (			_lowercase	):







						'''simple docstring'''


						if not isinstance(_lowercase,					_lowercase	):
												snake_case_ :int						=			f"""Plugboard setting isn't type string ({type(_lowercase	)})"""
												raise TypeError(_lowercase	)
						elif len(_lowercase	) % 2 != 0:
												snake_case_ :List[Any]						=			f"""Odd number of symbols ({len(_lowercase	)})"""
												raise Exception(_lowercase	)
						elif pbstring == "":
												return {}

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

						# Checks if all characters are unique
						snake_case_ :List[str]						=			set()
						for i in pbstring:
												if i not in abc:
																		snake_case_ :Dict						=			f"""'{i}' not in list of symbols"""
																		raise Exception(_lowercase	)
												elif i in tmppbl:
																		snake_case_ :Dict						=			f"""Duplicate symbol ({i})"""
																		raise Exception(_lowercase	)
												else:
																		tmppbl.add(_lowercase	)
						del tmppbl

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

						return pb
def    A_  (			_lowercase,					_lowercase,					_lowercase = (rotora, rotora, rotora),					_lowercase = "",					):







						'''simple docstring'''


						snake_case_ :Tuple						=			text.upper()
						snake_case_,       snake_case_,       snake_case_ :Tuple						=			_validator(
						    _lowercase,					_lowercase,					plugb.upper()	)

						snake_case_,       snake_case_,       snake_case_ :int						=			rotor_position
						snake_case_,       snake_case_,       snake_case_ :Tuple						=			rotor_selection
						rotorposa -= 1
						rotorposa -= 1
						rotorposa -= 1

						snake_case_ :int						=			[]

						# encryption/decryption process --------------------------
						for symbol in text:
												if symbol in abc:
																		# 1st plugboard --------------------------
																		if symbol in plugboard:
																								snake_case_ :Any						=			plugboard[symbol]

																		# rotor ra --------------------------
																		snake_case_ :Optional[int]						=			abc.index(_lowercase	) + rotorposa
																		snake_case_ :Any						=			rotora[index % len(_lowercase	)]

																		# rotor rb --------------------------
																		snake_case_ :List[Any]						=			abc.index(_lowercase	) + rotorposa
																		snake_case_ :int						=			rotora[index % len(_lowercase	)]

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

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

																		snake_case_ :Union[str, Any]						=			reflector[symbol]

																		# 2nd rotors
																		snake_case_ :int						=			abc[rotora.index(_lowercase	) - rotorposa]
																		snake_case_ :Dict						=			abc[rotora.index(_lowercase	) - rotorposa]
																		snake_case_ :Union[str, Any]						=			abc[rotora.index(_lowercase	) - rotorposa]

																		# 2nd plugboard
																		if symbol in plugboard:
																								snake_case_ :int						=			plugboard[symbol]

																		# moves/resets rotor positions
																		rotorposa += 1
																		if rotorposa >= len(_lowercase	):
																								snake_case_ :List[Any]						=			0
																								rotorposa += 1
																		if rotorposa >= len(_lowercase	):
																								snake_case_ :str						=			0
																								rotorposa += 1
																		if rotorposa >= len(_lowercase	):
																								snake_case_ :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__":
						__a      =				"This is my Python script that emulates the Enigma machine from WWII."
						__a      =				(1, 1, 1)
						__a      =				"pictures"
						__a      =				(rotora, rotora, rotora)
						__a      =				enigma(message, rotor_pos, rotor_sel, pb)

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





"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__a      =				logging.getLogger(__name__)
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     """token-classification"""






					def __init__(       self:							Any						,     snake_case:							Tuple						)   ->	List[Any]:
											if type(snake_case						) == dict:
																	snake_case_ :Optional[int]						=			Namespace(**snake_case						)
											snake_case_ :Optional[int]						=			import_module("""tasks"""						)
											try:
																	snake_case_ :Any						=			getattr(snake_case						,     hparams.task_type						)
																	snake_case_ :TokenClassificationTask						=			token_classification_task_clazz()
											except AttributeError:
																	raise ValueError(
																	    f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
																	    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""						)
											snake_case_ :Any						=			self.token_classification_task.get_labels(hparams.labels						)
											snake_case_ :str						=			CrossEntropyLoss().ignore_index
											super().__init__(snake_case						,     len(self.labels						)						,     self.mode						)






					def    lowerCAmelCase_				(       self:							Dict						,     **snake_case:							List[Any]						)   ->	Any:
											return self.model(**snake_case						)






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Tuple						,     snake_case:							List[Any]						)   ->	Optional[int]:
											snake_case_ :List[str]						=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
											if self.config.model_type != "distilbert":
																	snake_case_ :List[str]						=			(
																	    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
																	)  # XLM and RoBERTa don"t use token_type_ids

											snake_case_ :Optional[Any]						=			self(**snake_case						)
											snake_case_ :List[str]						=			outputs[0]
											# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
											return {"loss": loss}






					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											snake_case_ :List[Any]						=			self.hparams
											for mode in ["train", "dev", "test"]:
																	snake_case_ :Optional[int]						=			self._feature_file(snake_case						)
																	if os.path.exists(snake_case						) and not args.overwrite_cache:
																							logger.info("""Loading features from cached file %s"""						,     snake_case						)
																							snake_case_ :Optional[int]						=			torch.load(snake_case						)
																	else:
																							logger.info("""Creating features from dataset file at %s"""						,     args.data_dir						)
																							snake_case_ :Optional[int]						=			self.token_classification_task.read_examples_from_file(args.data_dir						,     snake_case						)
																							snake_case_ :Any						=			self.token_classification_task.convert_examples_to_features(
																							    snake_case						,     self.labels						,     args.max_seq_length						,     self.tokenizer						,     cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]						)						,     cls_token=self.tokenizer.cls_token						,     cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0						,     sep_token=self.tokenizer.sep_token						,     sep_token_extra=snake_case						,     pad_on_left=bool(self.config.model_type in ["""xlnet"""]						)						,     pad_token=self.tokenizer.pad_token_id						,     pad_token_segment_id=self.tokenizer.pad_token_type_id						,     pad_token_label_id=self.pad_token_label_id						,     )
																							logger.info("""Saving features into cached file %s"""						,     snake_case						)
																							torch.save(snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							int						,     snake_case:							int						,     snake_case:							bool = False						)   ->	DataLoader:
											snake_case_ :int						=			self._feature_file(snake_case						)
											logger.info("""Loading features from cached file %s"""						,     snake_case						)
											snake_case_ :str						=			torch.load(snake_case						)
											snake_case_ :Dict						=			torch.tensor([f.input_ids for f in features]						,     dtype=torch.long						)
											snake_case_ :str						=			torch.tensor([f.attention_mask for f in features]						,     dtype=torch.long						)
											if features[0].token_type_ids is not None:
																	snake_case_ :List[Any]						=			torch.tensor([f.token_type_ids for f in features]						,     dtype=torch.long						)
											else:
																	snake_case_ :List[str]						=			torch.tensor([0 for f in features]						,     dtype=torch.long						)
																	# HACK(we will not use this anymore soon)
											snake_case_ :Any						=			torch.tensor([f.label_ids for f in features]						,     dtype=torch.long						)
											return DataLoader(
											    TensorDataset(snake_case						,     snake_case						,     snake_case						,     snake_case						)						,     batch_size=snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Dict						,     snake_case:							Union[str, Any]						)   ->	List[str]:
											"""Compute validation""" ""
											snake_case_ :List[str]						=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
											if self.config.model_type != "distilbert":
																	snake_case_ :Dict						=			(
																	    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
																	)  # XLM and RoBERTa don"t use token_type_ids
											snake_case_ :Dict						=			self(**snake_case						)
											snake_case_,       snake_case_ :Dict						=			outputs[:2]
											snake_case_ :Union[str, Any]						=			logits.detach().cpu().numpy()
											snake_case_ :List[Any]						=			inputs["""labels"""].detach().cpu().numpy()
											return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							int						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			torch.stack([x["""val_loss"""] for x in outputs]						).mean()
											snake_case_ :Tuple						=			np.concatenate([x["""pred"""] for x in outputs]						,     axis=0						)
											snake_case_ :Tuple						=			np.argmax(snake_case						,     axis=2						)
											snake_case_ :List[str]						=			np.concatenate([x["""target"""] for x in outputs]						,     axis=0						)

											snake_case_ :Optional[Any]						=			dict(enumerate(self.labels						)						)
											snake_case_ :Dict						=			[[] for _ in range(out_label_ids.shape[0]						)]
											snake_case_ :Dict						=			[[] for _ in range(out_label_ids.shape[0]						)]

											for i in range(out_label_ids.shape[0]						):
																	for j in range(out_label_ids.shape[1]						):
																							if out_label_ids[i, j] != self.pad_token_label_id:
																													out_label_list[i].append(label_map[out_label_ids[i][j]]						)
																													preds_list[i].append(label_map[preds[i][j]]						)

											snake_case_ :str						=			{
											    """val_loss""": val_loss_mean,
											    """accuracy_score""": accuracy_score(snake_case						,     snake_case						),
											    """precision""": precision_score(snake_case						,     snake_case						),
											    """recall""": recall_score(snake_case						,     snake_case						),
											    """f1""": fa_score(snake_case						,     snake_case						),
											}

											snake_case_ :List[Any]						=			dict(results.items()						)
											snake_case_ :Union[str, Any]						=			results
											return ret, preds_list, out_label_list






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							Dict						)   ->	Optional[Any]:
											# when stable
											snake_case_,       snake_case_,       snake_case_ :Tuple						=			self._eval_end(snake_case						)
											snake_case_ :str						=			ret["""log"""]
											return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							Optional[int]						)   ->	Any:
											# updating to test_epoch_end instead of deprecated test_end
											snake_case_,       snake_case_,       snake_case_ :Any						=			self._eval_end(snake_case						)

											# Converting to the dict required by pl
											# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
											# pytorch_lightning/trainer/logging.py#L139
											snake_case_ :Optional[int]						=			ret["""log"""]
											# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
											return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






					@staticmethod
					def    lowerCAmelCase_				(       snake_case:							Any						,     snake_case:							int						)   ->	Dict:
											# Add NER specific options
											BaseTransformer.add_model_specific_args(snake_case						,     snake_case						)
											parser.add_argument(
											    """--task_type"""						,     default="""NER"""						,     type=snake_case						,     help="""Task type to fine tune in training (e.g. NER, POS, etc)"""						)
											parser.add_argument(
											    """--max_seq_length"""						,     default=128						,     type=snake_case						,     help=(
											        """The maximum total input sequence length after tokenization. Sequences longer """
											        """than this will be truncated, sequences shorter will be padded."""
											    )						,     )

											parser.add_argument(
											    """--labels"""						,     default=""""""						,     type=snake_case						,     help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""						,     )
											parser.add_argument(
											    """--gpus"""						,     default=0						,     type=snake_case						,     help="""The number of GPUs allocated for this, it is by default 0 meaning none"""						,     )

											parser.add_argument(
											    """--overwrite_cache"""						,     action="""store_true"""						,     help="""Overwrite the cached training and evaluation sets"""						)

											return parser


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						add_generic_args(parser, os.getcwd())
						__a      =				NERTransformer.add_model_specific_args(parser, os.getcwd())
						__a      =				parser.parse_args()
						__a      =				NERTransformer(args)
						__a      =				generic_train(model, args)

						if args.do_predict:
												# See https://github.com/huggingface/transformers/issues/3159
												# pl use this default format to create a checkpoint:
												# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
												# /pytorch_lightning/callbacks/model_checkpoint.py#L322
												__a      =				sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
												__a      =				model.load_from_checkpoint(checkpoints[-1])
												trainer.test(model)
