

"""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()


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







@dataclass
class     a__    :







  """simple docstring"""




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







@dataclass
class     a__    :







  """simple docstring"""




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






def    __lowercase (       )       ->  Dict:
      SCREAMING_SNAKE_CASE	: int					       =				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
      SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Union[str, 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."""     )

      SCREAMING_SNAKE_CASE	: Optional[int]					       =				import_module("""tasks"""     )

      try:
            SCREAMING_SNAKE_CASE	: Dict					       =				getattr(A_  ,      model_args.task_type     )
            SCREAMING_SNAKE_CASE	: Optional[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"""  ,      A_     )

      # Prepare Token Classification task
      SCREAMING_SNAKE_CASE	: List[Any]					       =				token_classification_task.get_labels(data_args.labels     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				dict(enumerate(A_     )     )
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				len(A_     )

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

      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				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  ,      )
      SCREAMING_SNAKE_CASE	: List[str]					       =				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():
            SCREAMING_SNAKE_CASE	: int					       =				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
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				(
          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
      )
      SCREAMING_SNAKE_CASE	: int					       =				(
          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]]:
            SCREAMING_SNAKE_CASE	: Tuple					       =				np.argmax(A_  ,      axis=2     )
            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				preds.shape
            SCREAMING_SNAKE_CASE	: str					       =				[[] for _ in range(A_     )]
            SCREAMING_SNAKE_CASE	: List[str]					       =				[[] 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:
            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				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
      SCREAMING_SNAKE_CASE	: int					       =				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
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				{}
      if training_args.do_eval:
            logger.info("""*** Evaluate ***"""     )

            SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				trainer.evaluate()
            SCREAMING_SNAKE_CASE	: Dict					       =				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:
            SCREAMING_SNAKE_CASE	: List[Any]					       =				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  ,      )

            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				trainer.predict(test_dataset.get_dataset()     )
            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				align_predictions(A_  ,      A_     )
            SCREAMING_SNAKE_CASE	: Dict					       =				classification_report(A_  ,      A_     )

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

            SCREAMING_SNAKE_CASE	: List[Any]					       =				os.path.join(training_args.output_dir  ,      """test_results.txt"""     )

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

            # Save predictions
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				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:
                        SCREAMING_SNAKE_CASE	: str					       =				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]:
                                    SCREAMING_SNAKE_CASE	: Dict					       =				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()







import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:       Tuple			      =      """\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
"""

UpperCAmelCase__	:       Union[str, Any]			      =      """\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
"""


UpperCAmelCase__	:       str			      =      """
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION    )
class     a__    (				datasets.Metric    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Any						)   ->Optional[int]:
        """simple docstring"""







        return datasets.MetricInfo(
            description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""						),
                    """references""": datasets.Value("""string"""						),
                }						)			,			homepage="""https://github.com/moussaKam/FrugalScore"""			,			)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->Any:
        """simple docstring"""







        if self.config_name == "default":
              SCREAMING_SNAKE_CASE	: int					       =				"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
        else:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.config_name
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoTokenizer.from_pretrained(UpperCAmelCase__						)




  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	str=3_2			,			UpperCAmelCase__      :	Optional[int]=1_2_8			,			UpperCAmelCase__      :	Optional[int]=None			,			)   ->Any:
        """simple docstring"""







        assert len(UpperCAmelCase__						) == len(
            UpperCAmelCase__						), "predictions and references should have the same number of sentences."
        if device is not None:
              assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
        else:
              SCREAMING_SNAKE_CASE	: List[str]					       =				"""gpu""" if torch.cuda.is_available() else """cpu"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				TrainingArguments(
            """trainer"""			,			fpaa=(device == """gpu""")			,			per_device_eval_batch_size=UpperCAmelCase__			,			report_to="""all"""			,			no_cuda=(device == """cpu""")			,			log_level="""warning"""			,			)
        SCREAMING_SNAKE_CASE	: int					       =				{"""sentence1""": predictions, """sentence2""": references}
        SCREAMING_SNAKE_CASE	: List[str]					       =				datasets.Dataset.from_dict(UpperCAmelCase__						)

        def tokenize_function(UpperCAmelCase__      :	Any						):
              return self.tokenizer(
                  data["""sentence1"""]			,			data["""sentence2"""]			,			max_length=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			padding=UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Dict					       =				raw_datasets.map(UpperCAmelCase__			,			batched=UpperCAmelCase__						)
        tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				Trainer(self.model			,			UpperCAmelCase__			,			tokenizer=self.tokenizer						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				trainer.predict(UpperCAmelCase__						)
        return {"scores": list(predictions.predictions.squeeze(-1						)						)}




