code: 
  
  
  
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  
  
  
  
  
  @contextmanager
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   def filter_log(__A       ):
    return False if "This IS expected if you are initializing" in record.msg else True
  
   __UpperCamelCase				      =  datasets.utils.logging.get_logger("""transformers.modeling_utils"""       )
   logger.addFilter(SCREAMING_SNAKE_CASE__       )
   try:
    yield
   finally:
    logger.removeFilter(SCREAMING_SNAKE_CASE__       )
  
  
  a__						:			Tuple										=			'\\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'
  
  a__						:			Tuple										=			'\\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'
  
  a__						:			Tuple										=			'\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        UpperCAmelCase__  (    datasets.Metric):
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    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		,	lowercase		,	lowercase		,	lowercase=None		,	lowercase=None		,	lowercase=None		,	lowercase=False		,	lowercase=False		,	lowercase=None		,	lowercase=6_4		,	lowercase=4		,	lowercase=False		,	lowercase=False		,	lowercase=None		,	lowercase=False		,	)  ->							Optional[int]:
    __UpperCamelCase				      =  bert_score.utils.get_hash
    __UpperCamelCase				      =  bert_score.BERTScorer
  
    if version.parse(bert_score.__version__			) >= version.parse("""0.3.10"""			):
     __UpperCamelCase				      =  functools.partial(UpperCamelCase__		,	use_fast_tokenizer=UpperCamelCase__			)
     __UpperCamelCase				      =  functools.partial(UpperCamelCase__		,	use_fast_tokenizer=UpperCamelCase__			)
    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"
     __UpperCamelCase				      =  bert_score.utils.langamodel[lang.lower()]
  
    if num_layers is None:
     __UpperCamelCase				      =  bert_score.utils.modelalayers[model_type]
  
    __UpperCamelCase				      =  get_hash(
        model=UpperCamelCase__		,	num_layers=UpperCamelCase__		,	idf=UpperCamelCase__		,	rescale_with_baseline=UpperCamelCase__		,	use_custom_baseline=baseline_path is not None		,	)
  
    with filter_logging_context():
     if not hasattr(self		,	"""cached_bertscorer"""			) or self.cached_bertscorer.hash != hashcode:
      __UpperCamelCase				      =  scorer(
          model_type=UpperCamelCase__		,	num_layers=UpperCamelCase__		,	batch_size=UpperCamelCase__		,	nthreads=UpperCamelCase__		,	all_layers=UpperCamelCase__		,	idf=UpperCamelCase__		,	device=UpperCamelCase__		,	lang=UpperCamelCase__		,	rescale_with_baseline=UpperCamelCase__		,	baseline_path=UpperCamelCase__		,	)
  
    (__UpperCamelCase)				      =  self.cached_bertscorer.score(
        cands=UpperCamelCase__		,	refs=UpperCamelCase__		,	verbose=UpperCamelCase__		,	batch_size=UpperCamelCase__		,	)
    __UpperCamelCase				      =  {
        '''precision''': P.tolist(),
        '''recall''': R.tolist(),
        '''f1''': F.tolist(),
        '''hashcode''': hashcode,
    }
    return output_dict
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase=None		,	lowercase=None		,	**lowercase			)  ->							List[str]:
  
  
  
  
  
  
  
    if references is not None:
     __UpperCamelCase				      =  [[ref] if isinstance(UpperCamelCase__		,	UpperCamelCase__			) else ref for ref in references]
    super().add_batch(predictions=UpperCamelCase__		,	references=UpperCamelCase__		,	**UpperCamelCase__			)
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase=None		,	lowercase=None		,	**lowercase			)  ->							Union[str, Any]:
  
  
  
  
  
  
  
    if isinstance(UpperCamelCase__		,	UpperCamelCase__			):
     __UpperCamelCase				      =  [reference]
    super().add(prediction=UpperCamelCase__		,	reference=UpperCamelCase__		,	**UpperCamelCase__			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text.txt"""
   __UpperCamelCase				      =  textwrap.dedent(
       """\
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
  
          Second paragraph:
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
          """       )
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
   __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  
  
  @require_pil
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines()[0]
   __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
   __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
   assert pa_table.schema.field("""image"""       ).type == Image()()
   __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read()
   if sample_by == "line":
    __UpperCamelCase				      =  expected_content.splitlines()
   elif sample_by == "paragraph":
    __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
   elif sample_by == "document":
    __UpperCamelCase				      =  [expected_content]
   __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  