

"""simple docstring"""




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def    _UpperCAmelCase    (					):





    """simple docstring"""


    def filter_log(_SCREAMING_SNAKE_CASE   :						Dict    ):
        return False if "This IS expected if you are initializing" in record.msg else True

    SCREAMING_SNAKE_CASE_													=  datasets.utils.logging.get_logger('transformers.modeling_utils'    )
    logger.addFilter(_SCREAMING_SNAKE_CASE    )
    try:
        yield
    finally:
        logger.removeFilter(_SCREAMING_SNAKE_CASE    )


UpperCamelCase__					: List[str]								=      "\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

UpperCamelCase__					: Dict								=      "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

UpperCamelCase__					: Dict								=      "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"





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

  def   lowerCAmelCase__       (   self):
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,						citation=_CITATION  ,						homepage='https://github.com/Tiiiger/bert_score'  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
              {
                  'predictions': datasets.Value('string'  ,						id='sequence'),
                  'references': datasets.Sequence(datasets.Value('string'  ,						id='sequence')  ,						id='references'),
              })  ,						codebase_urls=['https://github.com/Tiiiger/bert_score']  ,						reference_urls=[
              'https://github.com/Tiiiger/bert_score',
              'https://arxiv.org/abs/1904.09675',
          ]  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=None  ,						_A=None  ,						_A=None  ,						_A=False  ,						_A=False  ,						_A=None  ,						_A=64  ,						_A=4  ,						_A=False  ,						_A=False  ,						_A=None  ,						_A=False  ,						):
      SCREAMING_SNAKE_CASE_													=  bert_score.utils.get_hash
      SCREAMING_SNAKE_CASE_													=  bert_score.BERTScorer

      if version.parse(bert_score.__version__) >= version.parse('0.3.10'):
          SCREAMING_SNAKE_CASE_													=  functools.partial(_A  ,						use_fast_tokenizer=_A)
          SCREAMING_SNAKE_CASE_													=  functools.partial(_A  ,						use_fast_tokenizer=_A)
      elif use_fast_tokenizer:
          raise ImportWarning(
              'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
              'You can install it with `pip install "bert-score>=0.3.10"`.')

      if model_type is None:
          assert lang is not None, "either lang or model_type should be specified"
          SCREAMING_SNAKE_CASE_													=  bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
          SCREAMING_SNAKE_CASE_													=  bert_score.utils.modelalayers[model_type]

      SCREAMING_SNAKE_CASE_													=  get_hash(
          model=_A  ,						num_layers=_A  ,						idf=_A  ,						rescale_with_baseline=_A  ,						use_custom_baseline=baseline_path is not None  ,						)

      with filter_logging_context():
          if not hasattr(self  ,						'cached_bertscorer') or self.cached_bertscorer.hash != hashcode:
              SCREAMING_SNAKE_CASE_													=  scorer(
                  model_type=_A  ,						num_layers=_A  ,						batch_size=_A  ,						nthreads=_A  ,						all_layers=_A  ,						idf=_A  ,						device=_A  ,						lang=_A  ,						rescale_with_baseline=_A  ,						baseline_path=_A  ,						)

      ((SCREAMING_SNAKE_CASE_)							,     (SCREAMING_SNAKE_CASE_)							,     (SCREAMING_SNAKE_CASE_))													=  self.cached_bertscorer.score(
          cands=_A  ,						refs=_A  ,						verbose=_A  ,						batch_size=_A  ,						)
      SCREAMING_SNAKE_CASE_													=  {
          'precision': P.tolist(),
          'recall': R.tolist(),
          'f1': F.tolist(),
          'hashcode': hashcode,
      }
      return output_dict

  def   lowerCAmelCase__       (   self  ,						_A=None  ,						_A=None  ,						**_A):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if references is not None:
          SCREAMING_SNAKE_CASE_													=  [[ref] if isinstance(_A  ,						_A) else ref for ref in references]
      super().add_batch(predictions=_A  ,						references=_A  ,						**_A)


  def   lowerCAmelCase__       (   self  ,						_A=None  ,						_A=None  ,						**_A):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if isinstance(_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  [reference]
      super().add(prediction=_A  ,						reference=_A  ,						**_A)


