
import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


__lowerCAmelCase      :					List[str]   	=       logging.getLogger(__name__)




@dataclass
class 	lowercase_      :


  '''simple docstring'''







  a__   	=		field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
  a__   	=		field(
      default=_A  ,	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=_A  ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}					)
  a__   	=		field(default=_A  ,	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=_A  ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,	)







@dataclass
class 	lowercase_      :


  '''simple docstring'''







  a__   	=		field(
      metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}					)
  a__   	=		field(
      metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}					)
  a__   	=		field(
      default=1_28  ,	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=_A  ,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)







def a__   (	):






  '''simple docstring'''





  __magic_name__    					=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
  __magic_name__					,   __magic_name__					,   __magic_name__    					=	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.""" )

  __magic_name__    					=	import_module("""tasks""" )

  try:
    __magic_name__    					=	getattr(A_,					model_args.task_type )
    __magic_name__    					=	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,					)
  logger.info(
      """n_replicas: %s, distributed training: %s, 16-bits training: %s""",					training_args.n_replicas,					bool(training_args.n_replicas > 1 ),					training_args.fpaa,					)
  logger.info("""Training/evaluation parameters %s""",					A_ )

  # Prepare Token Classification task
  __magic_name__    					=	token_classification_task.get_labels(data_args.labels )
  __magic_name__    					=	dict(enumerate(A_ ) )
  __magic_name__    					=	len(A_ )

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

  __magic_name__    					=	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,					)
  __magic_name__    					=	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,					)

  with training_args.strategy.scope():
    __magic_name__    					=	TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path,					from_pt=bool(""".bin""" in model_args.model_name_or_path ),					config=A_,					cache_dir=model_args.cache_dir,					)

  # Get datasets
  __magic_name__    					=	(
      TFTokenClassificationDataset(
          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
  )
  __magic_name__    					=	(
      TFTokenClassificationDataset(
          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_,					A_ ) -> Tuple[List[int], List[int]]:
    __magic_name__    					=	np.argmax(A_,					axis=2 )
    __magic_name__					,   __magic_name__    					=	preds.shape
    __magic_name__    					=	[[] for _ in range(A_ )]
    __magic_name__    					=	[[] for _ in range(A_ )]

    for i in range(A_ ):
      for j in range(A_ ):
        if label_ids[i, j] != -100:
          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_ ) -> Dict:
    __magic_name__					,   __magic_name__    					=	align_predictions(p.predictions,					p.label_ids )

    return {
        "precision": precision_score(A_,					A_ ),
        "recall": recall_score(A_,					A_ ),
        "f1": fa_score(A_,					A_ ),
    }

  # Initialize our Trainer
  __magic_name__    					=	TFTrainer(
      model=A_,					args=A_,					train_dataset=train_dataset.get_dataset() if train_dataset else None,					eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,					compute_metrics=A_,					)

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir )

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

    __magic_name__    					=	trainer.evaluate()
    __magic_name__    					=	os.path.join(training_args.output_dir,					"""eval_results.txt""" )

    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:
    __magic_name__    					=	TFTokenClassificationDataset(
        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,					)

    __magic_name__					,   __magic_name__					,   __magic_name__    					=	trainer.predict(test_dataset.get_dataset() )
    __magic_name__					,   __magic_name__    					=	align_predictions(A_,					A_ )
    __magic_name__    					=	classification_report(A_,					A_ )

    logger.info("""\n%s""",					A_ )

    __magic_name__    					=	os.path.join(training_args.output_dir,					"""test_results.txt""" )

    with open(A_,					"""w""" ) as writer:
      writer.write("""%s\n""" % report )

    # Save predictions
    __magic_name__    					=	os.path.join(training_args.output_dir,					"""test_predictions.txt""" )

    with open(A_,					"""w""" ) as writer:
      with open(os.path.join(data_args.data_dir,					"""test.txt""" ),					"""r""" ) as f:
        __magic_name__    					=	0

        for line in f:
          if line.startswith("""-DOCSTART-""" ) or line == "" or line == "\n":
            writer.write(A_ )

            if not preds_list[example_id]:
              example_id += 1
          elif preds_list[example_id]:
            __magic_name__    					=	line.split()[0] + """ """ + preds_list[example_id].pop(0 ) + """\n"""

            writer.write(A_ )
          else:
            logger.warning("""Maximum sequence length exceeded: No prediction for '%s'.""",					line.split()[0] )

  return results


if __name__ == "__main__":
    main()



def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	str(A_ ).strip()
  if not bin_string:
    raise ValueError("""Empty string was passed to the function""" )
  __magic_name__    					=	bin_string[0] == """-"""
  if is_negative:
    __magic_name__    					=	bin_string[1:]
  if not all(char in """01""" for char in bin_string ):
    raise ValueError("""Non-binary value was passed to the function""" )
  __magic_name__    					=	0
  for char in bin_string:
    __magic_name__    					=	2 * decimal_number + int(A_ )
  return -decimal_number if is_negative else decimal_number


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

    testmod()

