






"""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()


_SCREAMING_SNAKE_CASE :    Optional[int]    =						logging.getLogger(__name__)






@dataclass
class 					a						:
    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=__snake_case  ,				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=__snake_case  ,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
    SCREAMING_SNAKE_CASE						:					bool						=			field(default=__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.
    SCREAMING_SNAKE_CASE						:					Optional[str]						=			field(
        default=__snake_case  ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,				)






@dataclass
class 					a						:
    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=1_2_8  ,				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=__snake_case  ,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)







def 					lowerCamelCase__	(							)				->    Optional[int]:
  # 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_   					=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
  lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						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_   					=						import_module('tasks'			)

  try:
    lowerCamelCase_   					=						getattr(_lowerCamelCase		,    model_args.task_type			)
    lowerCamelCase_   					=						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_   					=						token_classification_task.get_labels(data_args.labels			)
  lowerCamelCase_   					=						dict(enumerate(_lowerCamelCase			)			)
  lowerCamelCase_   					=						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_   					=						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_   					=						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_   					=						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_   					=						(
      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_   					=						(
      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_   					=						np.argmax(_lowerCamelCase		,    axis=2			)
    lowerCamelCase_					,     lowerCamelCase_   					=						preds.shape
    lowerCamelCase_   					=						[[] for _ in range(_lowerCamelCase			)]
    lowerCamelCase_   					=						[[] 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_   					=						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_   					=						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_   					=						{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'			)

    lowerCamelCase_   					=						trainer.evaluate()
    lowerCamelCase_   					=						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_   					=						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_   					=						trainer.predict(test_dataset.get_dataset()			)
    lowerCamelCase_					,     lowerCamelCase_   					=						align_predictions(_lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						classification_report(_lowerCamelCase		,    _lowerCamelCase			)

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

    lowerCamelCase_   					=						os.path.join(training_args.output_dir		,    'test_results.txt'			)

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

    # Save predictions
    lowerCamelCase_   					=						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_   					=						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_   					=						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 numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




