

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


lowercase_									=   logging.getLogger(__name__)





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





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





      """simple docstring"""



      _lowercase	       =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.
            _lowercase       ,       _lowercase       ,       _lowercase	       =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            _lowercase       ,       _lowercase       ,       _lowercase	       =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.'     )

      _lowercase	       =import_module('tasks'     )
      try:
            _lowercase	       =getattr(A__      ,   model_args.task_type     )
            _lowercase	       =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'      ,   A__     )

      # Set seed
      set_seed(training_args.seed     )

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

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

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

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

            _lowercase       ,       _lowercase	       =preds.shape

            _lowercase	       =[[] for _ in range(A__     )]
            _lowercase	       =[[] for _ in range(A__     )]

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

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

      # Initialize our Trainer
      _lowercase	       =Trainer(
          model=A__      ,   args=A__      ,   train_dataset=A__      ,   eval_dataset=A__      ,   compute_metrics=A__      ,   data_collator=A__      ,   )

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

            _lowercase	       =trainer.evaluate()

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

                  results.update(A__     )

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

            _lowercase       ,       _lowercase       ,       _lowercase	       =trainer.predict(A__     )
            _lowercase       ,       _lowercase	       =align_predictions(A__      ,   A__     )

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

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

      return results
def      a      (			A__	:   str     )    ->      Optional[Any]:





      """simple docstring"""



      main()


if __name__ == "__main__":
  main()





def      a      (			A__	:   str      ,   A__	:   bool = False     )    ->      str:





      """simple docstring"""



      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected string as input, found {type(A__     )}'''
            raise ValueError(A__     )
      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected boolean as use_pascal parameter, found {type(A__     )}'''
            raise ValueError(A__     )

      _lowercase	       =input_str.split('_'     )

      _lowercase	       =0 if use_pascal else 1

      _lowercase	       =words[start_index:]

      _lowercase	       =[word[0].upper() + word[1:] for word in words_to_capitalize]

      _lowercase	       ='' if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
  from doctest import testmod

  testmod()


