





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_						   =    logging.getLogger(__name__)



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       str      					=field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
  a_							:       Optional[str]      					=field(
      default="""NER"""							,    metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
  a_							:       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.
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,    )




@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       str      					=field(
      metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}							,    )
  a_							:       int      					=field(
      default=128							,    metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }							,    )
  a_							:       bool      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)




def     lowerCamelCase_	(      )->		str:
  # 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   :				List[str]														=	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   :				str														=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
  else:
    _snake_case   :				Tuple														=	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   :				int														=	import_module('tasks'				)
  try:
    _snake_case   :				List[Any]														=	getattr(lowerCAmelCase    ,							model_args.task_type				)
    _snake_case   :				TokenClassificationTask														=	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'    ,							lowerCAmelCase				)

  # Set seed
  set_seed(training_args.seed				)

  # Prepare CONLL-2003 task
  _snake_case   :				Optional[int]														=	token_classification_task.get_labels(data_args.labels				)
  _snake_case   :				Dict[int, str]														=	dict(enumerate(lowerCAmelCase				)				)
  _snake_case   :				Optional[Any]														=	len(lowerCAmelCase				)

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

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

  # Get datasets
  _snake_case   :				List[Any]														=	(
      TokenClassificationDataset(
          token_classification_task=lowerCAmelCase    ,							data_dir=data_args.data_dir    ,							tokenizer=lowerCAmelCase    ,							labels=lowerCAmelCase    ,							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   :				int														=	(
      TokenClassificationDataset(
          token_classification_task=lowerCAmelCase    ,							data_dir=data_args.data_dir    ,							tokenizer=lowerCAmelCase    ,							labels=lowerCAmelCase    ,							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(lowerCAmelCase:	np.ndarray    ,							lowerCAmelCase:	np.ndarray				) -> Tuple[List[int], List[int]]:
    _snake_case   :				Tuple														=	np.argmax(lowerCAmelCase    ,							axis=2				)

    _snake_case   :				Union[str, Any]														=	preds.shape

    _snake_case   :				int														=	[[] for _ in range(lowerCAmelCase				)]
    _snake_case   :				Any														=	[[] for _ in range(lowerCAmelCase				)]

    for i in range(lowerCAmelCase				):
      for j in range(lowerCAmelCase				):
        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(lowerCAmelCase:	EvalPrediction				) -> Dict:
    _snake_case   :				Any														=	align_predictions(p.predictions    ,							p.label_ids				)
    return {
        "accuracy_score": accuracy_score(lowerCAmelCase    ,							lowerCAmelCase				),
        "precision": precision_score(lowerCAmelCase    ,							lowerCAmelCase				),
        "recall": recall_score(lowerCAmelCase    ,							lowerCAmelCase				),
        "f1": fa_score(lowerCAmelCase    ,							lowerCAmelCase				),
    }

  # Data collator
  _snake_case   :				List[str]														=	DataCollatorWithPadding(lowerCAmelCase    ,							pad_to_multiple_of=8				) if training_args.fpaa else None

  # Initialize our Trainer
  _snake_case   :				Dict														=	Trainer(
      model=lowerCAmelCase    ,							args=lowerCAmelCase    ,							train_dataset=lowerCAmelCase    ,							eval_dataset=lowerCAmelCase    ,							compute_metrics=lowerCAmelCase    ,							data_collator=lowerCAmelCase    ,							)

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

    _snake_case   :				Tuple														=	trainer.evaluate()

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

      results.update(lowerCAmelCase				)

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

    _snake_case   :				Union[str, Any]														=	trainer.predict(lowerCAmelCase				)
    _snake_case   :				Optional[int]														=	align_predictions(lowerCAmelCase    ,							lowerCAmelCase				)

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

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

  return results


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		List[Any]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()








from functools import reduce

lowerCAmelCase_						   =    (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)


def     lowerCamelCase_	(      lowerCAmelCase:	str = N				)->		int:
  return max(
      # mypy cannot properly interpret reduce
      int(reduce(lambda lowerCAmelCase    ,							lowerCAmelCase				: str(int(lowerCAmelCase				) * int(lowerCAmelCase				)				)    ,							n[i : i + 13]				)				)
      for i in range(len(lowerCAmelCase				) - 12				)				)


if __name__ == "__main__":
      print(F"""{solution() = }""")




