

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def  __a    (				) ->       int:






    '''simple docstring'''
    def filter_log(SCREAMING_SNAKE_CASE ):
        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_						:     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'

A_						:     List[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'

A_						:     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    A_				( datasets.Metric  ):


    '''simple docstring'''






    def        lowerCAmelCase_  (self					)       -> str:
        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__=64       , lowercase__=4       , lowercase__=False       , lowercase__=False       , lowercase__=None       , lowercase__=False       , )       -> List[Any]:
        __UpperCAmelCase			   =     bert_score.utils.get_hash
        __UpperCAmelCase			   =     bert_score.BERTScorer

        if version.parse(bert_score.__version__					) >= version.parse('''0.3.10'''					):
            __UpperCAmelCase			   =     functools.partial(lowercase__       , use_fast_tokenizer=lowercase__					)
            __UpperCAmelCase			   =     functools.partial(lowercase__       , use_fast_tokenizer=lowercase__					)
        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=lowercase__       , num_layers=lowercase__       , idf=lowercase__       , rescale_with_baseline=lowercase__       , 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=lowercase__       , num_layers=lowercase__       , batch_size=lowercase__       , nthreads=lowercase__       , all_layers=lowercase__       , idf=lowercase__       , device=lowercase__       , lang=lowercase__       , rescale_with_baseline=lowercase__       , baseline_path=lowercase__       , )

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


    def        lowerCAmelCase_  (self       , lowercase__=None       , lowercase__=None       , **lowercase__					)       -> 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:
            __UpperCAmelCase			   =     [[ref] if isinstance(lowercase__       , lowercase__					) else ref for ref in references]
        super().add_batch(predictions=lowercase__       , references=lowercase__       , **lowercase__					)






    def        lowerCAmelCase_  (self       , lowercase__=None       , lowercase__=None       , **lowercase__					)       -> Tuple:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if isinstance(lowercase__       , lowercase__					):
            __UpperCAmelCase			   =     [reference]
        super().add(prediction=lowercase__       , reference=lowercase__       , **lowercase__					)







import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


A_						:     int			  =				logging.get_logger(__name__)
class    A_				( _a  ):


    '''simple docstring'''






    def __init__(self       , *lowercase__       , **lowercase__					)       -> None:
        warnings.warn(
            '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use ConvNextImageProcessor instead.'''       , lowercase__       , )
        super().__init__(*lowercase__       , **lowercase__					)




