code: 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()
  
  
  a     :			Union[str, Any]					    =						logging.getLogger(__name__)
  
  @dataclass
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							str        =						field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
      SCREAMING_SNAKE_CASE:							Optional[str]        =						field(
          default=lowerCAmelCase_   ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
      SCREAMING_SNAKE_CASE:							Optional[str]        =						field(
          default='NER'   ,     metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
      SCREAMING_SNAKE_CASE:							Optional[str]        =						field(
          default=lowerCAmelCase_   ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
      SCREAMING_SNAKE_CASE:							bool        =						field(default=lowerCAmelCase_   ,     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.
      SCREAMING_SNAKE_CASE:							Optional[str]        =						field(
          default=lowerCAmelCase_   ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,     )
  
  
  
  
  
  @dataclass
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							str        =						field(
          metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
      SCREAMING_SNAKE_CASE:							Optional[str]        =						field(
          metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} )
      SCREAMING_SNAKE_CASE:							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.'
              )
          }   ,     )
      SCREAMING_SNAKE_CASE:							bool        =						field(
          default=lowerCAmelCase_   ,     metadata={'help': 'Overwrite the cached training and evaluation sets'} )
  
  
  
  
  
  def 		snake_case__       (		):
     # 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.
     lowerCAmelCase_:       Union[str, Any]         =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
     lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       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."				)
  
     lowerCAmelCase_:       Optional[int]         =  import_module("tasks"				)
  
     try:
        lowerCAmelCase_:       List[Any]         =  getattr(lowercase       ,							model_args.task_type				)
        lowerCAmelCase_:       List[Any]         =  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"       ,							lowercase				)
  
     # Prepare Token Classification task
     lowerCAmelCase_:       Optional[Any]         =  token_classification_task.get_labels(data_args.labels				)
     lowerCAmelCase_:       Tuple         =  dict(enumerate(lowercase				)				)
     lowerCAmelCase_:       Optional[Any]         =  len(lowercase				)
  
     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
  
     lowerCAmelCase_:       int         =  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       ,							)
     lowerCAmelCase_:       Optional[int]         =  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():
        lowerCAmelCase_:       Optional[Any]         =  TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path       ,							from_pt=bool(".bin" in model_args.model_name_or_path				)       ,							config=lowercase       ,							cache_dir=model_args.cache_dir       ,							)
  
     # Get datasets
     lowerCAmelCase_:       List[str]         =  (
         TFTokenClassificationDataset(
             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
     )
     lowerCAmelCase_:       Optional[Any]         =  (
         TFTokenClassificationDataset(
             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       ,							lowercase				) -> Tuple[List[int], List[int]]:
        lowerCAmelCase_:       Tuple         =  np.argmax(lowercase       ,							axis=2				)
        lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  preds.shape
        lowerCAmelCase_:       int         =  [[] for _ in range(lowercase				)]
        lowerCAmelCase_:       str         =  [[] for _ in range(lowercase				)]
  
        for i in range(lowercase				):
           for j in range(lowercase				):
              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(lowercase				) -> Dict:
        lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  align_predictions(p.predictions       ,							p.label_ids				)
  
        return {
            "precision": precision_score(lowercase       ,							lowercase				),
            "recall": recall_score(lowercase       ,							lowercase				),
            "f1": fa_score(lowercase       ,							lowercase				),
        }
  
     # Initialize our Trainer
     lowerCAmelCase_:       str         =  TFTrainer(
         model=lowercase       ,							args=lowercase       ,							train_dataset=train_dataset.get_dataset() if train_dataset else None       ,							eval_dataset=eval_dataset.get_dataset() if eval_dataset else None       ,							compute_metrics=lowercase       ,							)
  
     # Training
     if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir				)
  
     # Evaluation
     lowerCAmelCase_:       Optional[int]         =  {}
     if training_args.do_eval:
        logger.info("*** Evaluate ***"				)
  
        lowerCAmelCase_:       Union[str, Any]         =  trainer.evaluate()
        lowerCAmelCase_:       List[str]         =  os.path.join(training_args.output_dir       ,							"eval_results.txt"				)
  
        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:
        lowerCAmelCase_:       Optional[Any]         =  TFTokenClassificationDataset(
            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       ,							)
  
        lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  trainer.predict(test_dataset.get_dataset()				)
        lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[int]         =  align_predictions(lowercase       ,							lowercase				)
        lowerCAmelCase_:       Optional[Any]         =  classification_report(lowercase       ,							lowercase				)
  
        logger.info("\n%s"       ,							lowercase				)
  
        lowerCAmelCase_:       Optional[int]         =  os.path.join(training_args.output_dir       ,							"test_results.txt"				)
  
        with open(lowercase       ,							"w"				) as writer:
           writer.write("%s\n" % report				)
  
        # Save predictions
        lowerCAmelCase_:       Any         =  os.path.join(training_args.output_dir       ,							"test_predictions.txt"				)
  
        with open(lowercase       ,							"w"				) as writer:
           with open(os.path.join(data_args.data_dir       ,							"test.txt"				)       ,							"r"				) as f:
              lowerCAmelCase_:       Any         =  0
  
              for line in f:
                 if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                    writer.write(lowercase				)
  
                    if not preds_list[example_id]:
                       example_id += 1
                 elif preds_list[example_id]:
                    lowerCAmelCase_:       Optional[Any]         =  line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"
  
                    writer.write(lowercase				)
                 else:
                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."       ,							line.split()[0]				)
  
     return results
  
  
  if __name__ == "__main__":
   main()
style_context: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)