
import itertools
import string
from collections.abc import Generator, Iterable






def 	a_		(	__lowercase       :     Iterable[str]	, __lowercase       :     int			)			->  Generator[tuple[str, ...], None, None]:
			_snake_case          =      iter(__lowercase			)
			while True:
						_snake_case          =      tuple(itertools.islice(__lowercase	, __lowercase			)			)
						if not chunk:
									return
						yield chunk






def 	a_		(	__lowercase       :     str			)			->  str:
			_snake_case          =      ''.join([c.upper() for c in dirty if c in string.ascii_letters]			)
			_snake_case          =      ''

			if len(__lowercase			) < 2:
						return dirty

			for i in range(len(__lowercase			) - 1			):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(__lowercase			) & 1:
						clean += "X"

			return clean






def 	a_		(	__lowercase       :     str			)			->  list[str]:
			# I and J are used interchangeably to allow
			# us to use a 5x5 table (25 letters)
			_snake_case          =      'ABCDEFGHIKLMNOPQRSTUVWXYZ'
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			_snake_case          =      []

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(__lowercase			)

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(__lowercase			)

			return table






def 	a_		(	__lowercase       :     str	, __lowercase       :     str			)			->  str:
			_snake_case          =      generate_table(__lowercase			)
			_snake_case          =      prepare_input(__lowercase			)
			_snake_case          =      ''

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowercase	, 2			):
						_snake_case     , _snake_case          =      divmod(table.index(__lowercase			)	, 5			)
						_snake_case     , _snake_case          =      divmod(table.index(__lowercase			)	, 5			)

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def 	a_		(	__lowercase       :     str	, __lowercase       :     str			)			->  str:
			_snake_case          =      generate_table(__lowercase			)
			_snake_case          =      ''

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowercase	, 2			):
						_snake_case     , _snake_case          =      divmod(table.index(__lowercase			)	, 5			)
						_snake_case     , _snake_case          =      divmod(table.index(__lowercase			)	, 5			)

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext

import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCamelCase	:    int          =      logging.getLogger(__name__)




@dataclass
class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	_UpperCAmelCase	:		str          = field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
	_UpperCAmelCase	:		Optional[str]          = field(
	    default=UpperCAmelCase							,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
	_UpperCAmelCase	:		Optional[str]          = field(
	    default="NER"							,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
	_UpperCAmelCase	:		Optional[str]          = field(
	    default=UpperCAmelCase							,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
	_UpperCAmelCase	:		bool          = field(default=UpperCAmelCase							,metadata={"help": "Set this flag to use fast tokenization."} )
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	_UpperCAmelCase	:		Optional[str]          = field(
	    default=UpperCAmelCase							,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,)




@dataclass
class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	_UpperCAmelCase	:		str          = field(
	    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
	_UpperCAmelCase	:		Optional[str]          = field(
	    default=UpperCAmelCase							,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}							,)
	_UpperCAmelCase	:		int          = field(
	    default=1_2_8							,metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }							,)
	_UpperCAmelCase	:		bool          = field(
	    default=UpperCAmelCase							,metadata={"help": "Overwrite the cached training and evaluation sets"} )






def 	a_		(	)			->  Optional[int]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			_snake_case          =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
			if len(sys.argv			) == 2 and sys.argv[1].endswith('.json'			):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						_snake_case     , _snake_case     , _snake_case          =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
			else:
						_snake_case     , _snake_case     , _snake_case          =      parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir			)
			    and os.listdir(training_args.output_dir			)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
						    ' --overwrite_output_dir to overcome.'			)

			_snake_case          =      import_module('tasks'			)
			try:
						_snake_case          =      getattr(__lowercase	, model_args.task_type			)
						_snake_case          =      token_classification_task_clazz()
			except AttributeError:
						raise ValueError(
						    f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
						    f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''			)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	, datefmt='%m/%d/%Y %H:%M:%S'	, level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	, )
			logger.warning(
			    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'	, training_args.local_rank	, training_args.device	, training_args.n_gpu	, bool(training_args.local_rank != -1			)	, training_args.fpaa	, )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank			):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('Training/evaluation parameters %s'	, __lowercase			)

			# Set seed
			set_seed(training_args.seed			)

			# Prepare CONLL-2003 task
			_snake_case          =      token_classification_task.get_labels(data_args.labels			)
			_snake_case          =      dict(enumerate(__lowercase			)			)
			_snake_case          =      len(__lowercase			)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			_snake_case          =      AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path	, num_labels=__lowercase	, idalabel=__lowercase	, labelaid={label: i for i, label in enumerate(__lowercase			)}	, cache_dir=model_args.cache_dir	, )
			_snake_case          =      AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	, cache_dir=model_args.cache_dir	, use_fast=model_args.use_fast	, )
			_snake_case          =      AutoModelForTokenClassification.from_pretrained(
			    model_args.model_name_or_path	, from_tf=bool('.ckpt' in model_args.model_name_or_path			)	, config=__lowercase	, cache_dir=model_args.cache_dir	, )

			# Get datasets
			_snake_case          =      (
			    TokenClassificationDataset(
			        token_classification_task=__lowercase	, data_dir=data_args.data_dir	, tokenizer=__lowercase	, labels=__lowercase	, model_type=config.model_type	, max_seq_length=data_args.max_seq_length	, overwrite_cache=data_args.overwrite_cache	, mode=Split.train	, )
			    if training_args.do_train
			    else None
			)
			_snake_case          =      (
			    TokenClassificationDataset(
			        token_classification_task=__lowercase	, data_dir=data_args.data_dir	, tokenizer=__lowercase	, labels=__lowercase	, model_type=config.model_type	, max_seq_length=data_args.max_seq_length	, overwrite_cache=data_args.overwrite_cache	, mode=Split.dev	, )
			    if training_args.do_eval
			    else None
			)

			def align_predictions(__lowercase       :     np.ndarray	, __lowercase       :     np.ndarray			) -> Tuple[List[int], List[int]]:
						_snake_case          =      np.argmax(__lowercase	, axis=2			)

						_snake_case     , _snake_case          =      preds.shape

						_snake_case          =      [[] for _ in range(__lowercase			)]
						_snake_case          =      [[] for _ in range(__lowercase			)]

						for i in range(__lowercase			):
									for j in range(__lowercase			):
												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
															out_label_list[i].append(label_map[label_ids[i][j]]			)
															preds_list[i].append(label_map[preds[i][j]]			)

						return preds_list, out_label_list

			def compute_metrics(__lowercase       :     EvalPrediction			) -> Dict:
						_snake_case     , _snake_case          =      align_predictions(p.predictions	, p.label_ids			)
						return {
						    "accuracy_score": accuracy_score(__lowercase	, __lowercase			),
						    "precision": precision_score(__lowercase	, __lowercase			),
						    "recall": recall_score(__lowercase	, __lowercase			),
						    "f1": fa_score(__lowercase	, __lowercase			),
						}

			# Data collator
			_snake_case          =      DataCollatorWithPadding(__lowercase	, pad_to_multiple_of=8			) if training_args.fpaa else None

			# Initialize our Trainer
			_snake_case          =      Trainer(
			    model=__lowercase	, args=__lowercase	, train_dataset=__lowercase	, eval_dataset=__lowercase	, compute_metrics=__lowercase	, data_collator=__lowercase	, )

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_process_zero():
									tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
			_snake_case          =      {}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'			)

						_snake_case          =      trainer.evaluate()

						_snake_case          =      os.path.join(training_args.output_dir	, 'eval_results.txt'			)
						if trainer.is_world_process_zero():
									with open(__lowercase	, 'w'			) as writer:
												logger.info('***** Eval results *****'			)
												for key, value in result.items():
															logger.info('  %s = %s'	, __lowercase	, __lowercase			)
															writer.write('%s = %s\n' % (key, value)			)

									results.update(__lowercase			)

    # Predict
			if training_args.do_predict:
						_snake_case          =      TokenClassificationDataset(
						    token_classification_task=__lowercase	, data_dir=data_args.data_dir	, tokenizer=__lowercase	, labels=__lowercase	, model_type=config.model_type	, max_seq_length=data_args.max_seq_length	, overwrite_cache=data_args.overwrite_cache	, mode=Split.test	, )

						_snake_case     , _snake_case     , _snake_case          =      trainer.predict(__lowercase			)
						_snake_case     , _snake_case          =      align_predictions(__lowercase	, __lowercase			)

						_snake_case          =      os.path.join(training_args.output_dir	, 'test_results.txt'			)
						if trainer.is_world_process_zero():
									with open(__lowercase	, 'w'			) as writer:
												for key, value in metrics.items():
															logger.info('  %s = %s'	, __lowercase	, __lowercase			)
															writer.write('%s = %s\n' % (key, value)			)

        # Save predictions
						_snake_case          =      os.path.join(training_args.output_dir	, 'test_predictions.txt'			)
						if trainer.is_world_process_zero():
									with open(__lowercase	, 'w'			) as writer:
												with open(os.path.join(data_args.data_dir	, 'test.txt'			)	, 'r'			) as f:
															token_classification_task.write_predictions_to_file(__lowercase	, __lowercase	, __lowercase			)

			return results






def 	a_		(	__lowercase       :     Optional[Any]			)			->  Optional[int]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
			main()