code: 
  
  
  """simple docstring"""
  
  
  
  
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  
  
  
  @contextmanager
  def 			a_   (  ):
  			def filter_log(lowercase__   :Union[str, Any]    ):
  						return False if "This IS expected if you are initializing" in record.msg else True
  
  			__lowerCamelCase        =			datasets.utils.logging.get_logger("""transformers.modeling_utils"""    )
  			logger.addFilter(lowercase__    )
  			try:
  						yield
  			finally:
  						logger.removeFilter(lowercase__    )
  
  
  __magic_name__	:   Optional[int]          =							'\\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'
  
  __magic_name__	:   Optional[Any]          =							'\\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'
  
  __magic_name__	:   str          =							'\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      __a      (    self:						Dict      ):
  										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      __a      (    self:						Dict    ,       A_:						List[Any]    ,       A_:						int    ,       A_:						str=None    ,       A_:						List[Any]=None    ,       A_:						Any=None    ,       A_:						Tuple=False    ,       A_:						Dict=False    ,       A_:						Union[str, Any]=None    ,       A_:						Any=64    ,       A_:						int=4    ,       A_:						Optional[Any]=False    ,       A_:						Optional[Any]=False    ,       A_:						Union[str, Any]=None    ,       A_:						Tuple=False    ,       ):
  										__lowerCamelCase        =			bert_score.utils.get_hash
  										__lowerCamelCase        =			bert_score.BERTScorer
  
  										if version.parse(bert_score.__version__      ) >= version.parse("""0.3.10"""      ):
  													__lowerCamelCase        =			functools.partial(A_    ,       use_fast_tokenizer=A_      )
  													__lowerCamelCase        =			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"
  													__lowerCamelCase        =			bert_score.utils.langamodel[lang.lower()]
  
  										if num_layers is None:
  													__lowerCamelCase        =			bert_score.utils.modelalayers[model_type]
  
  										__lowerCamelCase        =			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:
  																__lowerCamelCase        =			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_    ,       )
  
  										((__lowerCamelCase)    ,(__lowerCamelCase)    ,(__lowerCamelCase))        =			self.cached_bertscorer.score(
  										    cands=A_    ,       refs=A_    ,       verbose=A_    ,       batch_size=A_    ,       )
  										__lowerCamelCase        =			{
  										    """precision""": P.tolist(),
  										    """recall""": R.tolist(),
  										    """f1""": F.tolist(),
  										    """hashcode""": hashcode,
  										}
  										return output_dict
  
  
  
  
  							def      __a      (    self:						List[Any]    ,       A_:						Tuple=None    ,       A_:						Dict=None    ,       **A_:						int      ):
  										# References can be strings or lists of strings
  										# Let's change strings to lists of strings with one element
  										if references is not None:
  													__lowerCamelCase        =			[[ref] if isinstance(A_    ,       A_      ) else ref for ref in references]
  										super().add_batch(predictions=A_    ,       references=A_    ,       **A_      )
  
  
  
  
  							def      __a      (    self:						List[str]    ,       A_:						List[Any]=None    ,       A_:						Any=None    ,       **A_:						Dict      ):
  										# References can be strings or lists of strings
  										# Let's change strings to lists of strings with one element
  										if isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[reference]
  										super().add(prediction=A_    ,       reference=A_    ,       **A_      )
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __magic_name__	:   Optional[int]          =							logging.getLogger()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser()
  			parser.add_argument("""-f"""    )
  			__lowerCamelCase        =			parser.parse_args()
  			return args.f
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
  										logger.addHandler(A_      )
  
  										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  										with patch.object(A_    ,       """argv"""    ,       A_      ):
  													__lowerCamelCase        =			run_glue_with_pabee.main()
  													for value in result.values():
  																self.assertGreaterEqual(A_    ,       0.75      )
  
  
  
  
  