



"""simple docstring"""







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()


lowercase__						:							Any        =      logging.getLogger(__name__)







@dataclass
class  UpperCamelCase__  :




      """simple docstring"""




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







@dataclass
class  UpperCamelCase__  :




      """simple docstring"""




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




def 							UpperCamelCase_       (       )		->  Tuple:

  """simple docstring"""




  lowerCAmelCase_  :							Union[str, Any]								=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
  lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_  :							List[str]								=					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_  :							List[str]								=					import_module('tasks'		)

  try:
    lowerCAmelCase_  :							Optional[int]								=					getattr(lowerCAmelCase__       ,  model_args.task_type		)
    lowerCAmelCase_  :							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       ,  )
  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'       ,  lowerCAmelCase__		)

  # Prepare Token Classification task
  lowerCAmelCase_  :							Tuple								=					token_classification_task.get_labels(data_args.labels		)
  lowerCAmelCase_  :							Dict[int, str]								=					dict(enumerate(lowerCAmelCase__		)		)
  lowerCAmelCase_  :							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.

  lowerCAmelCase_  :							Dict								=					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       ,  )
  lowerCAmelCase_  :							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_  :							Union[str, Any]								=					TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path       ,  from_pt=bool('.bin' in model_args.model_name_or_path		)       ,  config=lowerCAmelCase__       ,  cache_dir=model_args.cache_dir       ,  )

  # Get datasets
  lowerCAmelCase_  :							Optional[int]								=					(
      TFTokenClassificationDataset(
          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
  )
  lowerCAmelCase_  :							List[str]								=					(
      TFTokenClassificationDataset(
          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]]:
    lowerCAmelCase_  :							str								=					np.argmax(lowerCAmelCase__       ,  axis=2		)
    lowerCAmelCase_		,lowerCAmelCase_  :							List[Any]								=					preds.shape
    lowerCAmelCase_  :							Tuple								=					[[] for _ in range(lowerCAmelCase__		)]
    lowerCAmelCase_  :							Union[str, Any]								=					[[] for _ in range(lowerCAmelCase__		)]

    for i in range(lowerCAmelCase__		):
      for j in range(lowerCAmelCase__		):
        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(lowerCAmelCase__ :							EvalPrediction		) -> Dict:
    lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					align_predictions(p.predictions       ,  p.label_ids		)

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

  # Initialize our Trainer
  lowerCAmelCase_  :							List[str]								=					TFTrainer(
      model=lowerCAmelCase__       ,  args=lowerCAmelCase__       ,  train_dataset=train_dataset.get_dataset() if train_dataset else None       ,  eval_dataset=eval_dataset.get_dataset() if eval_dataset else None       ,  compute_metrics=lowerCAmelCase__       ,  )

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

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

    lowerCAmelCase_  :							Union[str, Any]								=					trainer.evaluate()
    lowerCAmelCase_  :							Optional[int]								=					os.path.join(training_args.output_dir       ,  'eval_results.txt'		)

    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:
    lowerCAmelCase_  :							List[Any]								=					TFTokenClassificationDataset(
        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       ,  )

    lowerCAmelCase_		,lowerCAmelCase_		,lowerCAmelCase_  :							Optional[Any]								=					trainer.predict(test_dataset.get_dataset()		)
    lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					align_predictions(lowerCAmelCase__       ,  lowerCAmelCase__		)
    lowerCAmelCase_  :							Any								=					classification_report(lowerCAmelCase__       ,  lowerCAmelCase__		)

    logger.info('\n%s'       ,  lowerCAmelCase__		)

    lowerCAmelCase_  :							Union[str, Any]								=					os.path.join(training_args.output_dir       ,  'test_results.txt'		)

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

    # Save predictions
    lowerCAmelCase_  :							Optional[Any]								=					os.path.join(training_args.output_dir       ,  'test_predictions.txt'		)

    with open(lowerCAmelCase__       ,  'w'		) as writer:
      with open(os.path.join(data_args.data_dir       ,  'test.txt'		)       ,  'r'		) as f:
        lowerCAmelCase_  :							Optional[Any]								=					0

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

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

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

  return results


if __name__ == "__main__":
     main()










"""simple docstring"""







import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    'other, expected_equality'       ,  [
        (Version('1.0.0'		), True),
        ('1.0.0', True),
        (Version('2.0.0'		), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]       ,  )
def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							List[Any]		)		->  Tuple:

  """simple docstring"""




  lowerCAmelCase_  :							List[str]								=					Version('1.0.0'		)
  assert (version == other) is expected_equality
  assert (version != other) is not expected_equality
  assert (hash(lowerCAmelCase__		) == hash(lowerCAmelCase__		)) is expected_equality





