
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def      _a     (   ):


   def filter_log(__UpperCamelCase       :  Optional[int]    ):
      return False if "This IS expected if you are initializing" in record.msg else True

   lowerCAmelCase__ :    int				      =			datasets.utils.logging.get_logger('''transformers.modeling_utils'''    )
   logger.addFilter(SCREAMING_SNAKE_CASE__    )
   try:
      yield
   finally:
      logger.removeFilter(SCREAMING_SNAKE_CASE__    )


A__       :   Optional[Any]   						=					"""\\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__       :   List[str]   						=					"""\\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__       :   List[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 						lowercase       (  datasets.Metric					):
     def lowercase_			(		self    ):
        """simple docstring"""







        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 lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=64 ,	SCREAMING_SNAKE_CASE__=4 ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=False ,	):
        """simple docstring"""







        lowerCAmelCase__ :    Union[str, Any]				      =			bert_score.utils.get_hash
        lowerCAmelCase__ :    List[Any]				      =			bert_score.BERTScorer

        if version.parse(bert_score.__version__    ) >= version.parse('''0.3.10'''    ):
           lowerCAmelCase__ :    str				      =			functools.partial(UpperCamelCase__ ,	use_fast_tokenizer=UpperCamelCase__    )
           lowerCAmelCase__ :    str				      =			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"
           lowerCAmelCase__ :    Optional[Any]				      =			bert_score.utils.langamodel[lang.lower()]

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

        lowerCAmelCase__ :    str				      =			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:
              lowerCAmelCase__ :    Optional[int]				      =			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__ ,	)

        (lowerCAmelCase__) :    Any				      =			self.cached_bertscorer.score(
            cands=UpperCamelCase__ ,	refs=UpperCamelCase__ ,	verbose=UpperCamelCase__ ,	batch_size=UpperCamelCase__ ,	)
        lowerCAmelCase__ :    List[str]				      =			{
            '''precision''': P.tolist(),
            '''recall''': R.tolist(),
            '''f1''': F.tolist(),
            '''hashcode''': hashcode,
        }
        return output_dict
     def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	**SCREAMING_SNAKE_CASE__    ):
        """simple docstring"""







        if references is not None:
           lowerCAmelCase__ :    Optional[Any]				      =			[[ref] if isinstance(UpperCamelCase__ ,	UpperCamelCase__    ) else ref for ref in references]
        super().add_batch(predictions=UpperCamelCase__ ,	references=UpperCamelCase__ ,	**UpperCamelCase__    )
     def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	**SCREAMING_SNAKE_CASE__    ):
        """simple docstring"""







        if isinstance(UpperCamelCase__ ,	UpperCamelCase__    ):
           lowerCAmelCase__ :    Optional[int]				      =			[reference]
        super().add(prediction=UpperCamelCase__ ,	reference=UpperCamelCase__ ,	**UpperCamelCase__    )





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A__       :   Tuple   						=					TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=__UpperCamelCase					)
class 						lowercase       (  abc.ABC					):
					# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
					__a					=    42
					__a					=    42
					__a					=    42
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return copy.deepcopy(self    )
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return Features(**self.input_schema ,	**self.label_schema    )
					@property
					@abc.abstractmethod
					def lowercase_			(		self    ):
								"""simple docstring"""







								raise NotImplementedError
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{f.name for f in dataclasses.fields(cls    )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}    )



