





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


UpperCAmelCase	:	List[Any]		     =       logging.getLogger(__name__)







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







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					str						=			field(
          metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""})
      _lowercase				:					Optional[str]						=			field(
          default=UpperCamelCase__						,				metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}						,				)
      _lowercase				:					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."""
              )
          }						,				)
      _lowercase				:					bool						=			field(
          default=UpperCamelCase__						,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
def      _A       (		):





  """simple docstring"""



  a__	:					Tuple										=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.
    a__					,							a__					,							a__	:					int										=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    a__					,							a__					,							a__	:					Optional[Any]										=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."						)

  a__	:					List[Any]										=import_module("tasks"						)
  try:
    a__	:					Dict										=getattr(SCREAMING_SNAKE_CASE       ,       model_args.task_type						)
    a__	:					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"       ,       SCREAMING_SNAKE_CASE						)

  # Set seed
  set_seed(training_args.seed						)

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

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

  a__	:					List[Any]										=AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,       num_labels=SCREAMING_SNAKE_CASE       ,       idalabel=SCREAMING_SNAKE_CASE       ,       labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE						)}       ,       cache_dir=model_args.cache_dir       ,       )
  a__	:					Tuple										=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       ,       )
  a__	:					Optional[int]										=AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path       ,       from_tf=bool(".ckpt" in model_args.model_name_or_path						)       ,       config=SCREAMING_SNAKE_CASE       ,       cache_dir=model_args.cache_dir       ,       )

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

    a__					,							a__	:					Dict										=preds.shape

    a__	:					List[str]										=[[] for _ in range(SCREAMING_SNAKE_CASE						)]
    a__	:					Optional[int]										=[[] for _ in range(SCREAMING_SNAKE_CASE						)]

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

  # Data collator
  a__	:					Any										=DataCollatorWithPadding(SCREAMING_SNAKE_CASE       ,       pad_to_multiple_of=8						) if training_args.fpaa else None

  # Initialize our Trainer
  a__	:					str										=Trainer(
      model=SCREAMING_SNAKE_CASE       ,       args=SCREAMING_SNAKE_CASE       ,       train_dataset=SCREAMING_SNAKE_CASE       ,       eval_dataset=SCREAMING_SNAKE_CASE       ,       compute_metrics=SCREAMING_SNAKE_CASE       ,       data_collator=SCREAMING_SNAKE_CASE       ,       )

  # 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
  a__	:					int										={}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"						)

    a__	:					str										=trainer.evaluate()

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

      results.update(SCREAMING_SNAKE_CASE						)

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

    a__					,							a__					,							a__	:					str										=trainer.predict(SCREAMING_SNAKE_CASE						)
    a__					,							a__	:					str										=align_predictions(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

    a__	:					Optional[int]										=os.path.join(training_args.output_dir       ,       "test_results.txt"						)
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE       ,       "w"						) as writer:
        for key, value in metrics.items():
          logger.info("  %s = %s"       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
          writer.write("%s = %s\n" % (key, value)						)

        # Save predictions
    a__	:					Union[str, Any]										=os.path.join(training_args.output_dir       ,       "test_predictions.txt"						)
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE       ,       "w"						) as writer:
        with open(os.path.join(data_args.data_dir       ,       "test.txt"						)       ,       "r"						) as f:
          token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  return results
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]						):





  """simple docstring"""



  main()


if __name__ == "__main__":
     main()













from math import loga






def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   if a < 0:
      raise ValueError('''Input value must be a positive integer'''       )
   elif isinstance(snake_case_       ,      snake_case_       ):
      raise TypeError('''Input value must be a \'int\' type'''       )
   return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

