




'''simple docstring'''







import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 		snake_case_				():






    def filter_log(_a      :				List[str]	):
        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(_a	)
    try:
        yield
    finally:
        logger.removeFilter(_a	)


A					       ='\\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					       ='\\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					       ='\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					):
       def        A (      self		:      Union[str, Any]							):



           '''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        A (      self		:      Tuple  ,							lowercase		:      str  ,							lowercase		:      Optional[int]  ,							lowercase		:      Any=None  ,							lowercase		:      Dict=None  ,							lowercase		:      List[Any]=None  ,							lowercase		:      str=False  ,							lowercase		:      Dict=False  ,							lowercase		:      Union[str, Any]=None  ,							lowercase		:      Optional[Any]=64  ,							lowercase		:      Union[str, Any]=4  ,							lowercase		:      Union[str, Any]=False  ,							lowercase		:      Optional[int]=False  ,							lowercase		:      List[str]=None  ,							lowercase		:      Union[str, Any]=False  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						bert_score.utils.get_hash
           UpperCAmelCase     			=						bert_score.BERTScorer

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

           (UpperCAmelCase)     			=						self.cached_bertscorer.score(
               cands=_SCREAMING_SNAKE_CASE  ,							refs=_SCREAMING_SNAKE_CASE  ,							verbose=_SCREAMING_SNAKE_CASE  ,							batch_size=_SCREAMING_SNAKE_CASE  ,							)
           UpperCAmelCase     			=						{
               '''precision''': P.tolist(),
               '''recall''': R.tolist(),
               '''f1''': F.tolist(),
               '''hashcode''': hashcode,
           }
           return output_dict
       def        A (      self		:      Dict  ,							lowercase		:      str=None  ,							lowercase		:      Dict=None  ,							**lowercase		:      Union[str, Any]							):



           '''simple docstring'''

           if references is not None:
               UpperCAmelCase     			=						[[ref] if isinstance(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							) else ref for ref in references]
           super().add_batch(predictions=_SCREAMING_SNAKE_CASE  ,							references=_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_SNAKE_CASE							)







       def        A (      self		:      Tuple  ,							lowercase		:      str=None  ,							lowercase		:      int=None  ,							**lowercase		:      Optional[Any]							):



           '''simple docstring'''

           if isinstance(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							):
               UpperCAmelCase     			=						[reference]
           super().add(prediction=_SCREAMING_SNAKE_CASE  ,							reference=_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_SNAKE_CASE							)








import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


lowerCamelCase		=						logging.get_logger(__name__)





class    _a				(							_lowercase):




	def __init__(   self		:						Any						,   *_SCREAMING_SNAKE_CASE		:						int						,   **_SCREAMING_SNAKE_CASE		:						Dict						)->			None:
			warnings.warn(
			    '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
			    ''' Please use MaskFormerImageProcessor instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			super().__init__(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




