






'''simple docstring'''
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def 						__SCREAMING_SNAKE_CASE    (     ):


   """simple docstring"""




   def filter_log(_UpperCamelCase							):
      return False if "This IS expected if you are initializing" in record.msg else True

   lowercase_		:			List[Any]							=   datasets.utils.logging.get_logger("transformers.modeling_utils"							)
   logger.addFilter(__lowerCAmelCase							)
   try:
      yield
   finally:
      logger.removeFilter(__lowerCAmelCase							)


UpperCamelCase__       =	'\\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'

UpperCamelCase__       =	'\\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'

UpperCamelCase__       =	'\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     :				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  lowerCAmelCase__ (	self     :				Tuple     ,   a     :				Tuple     ,   a     :				Optional[int]     ,   a     :				int=None     ,   a     :				Optional[Any]=None     ,   a     :				Optional[Any]=None     ,   a     :				Tuple=False     ,   a     :				int=False     ,   a     :				str=None     ,   a     :				int=6_4     ,   a     :				Optional[int]=4     ,   a     :				Any=False     ,   a     :				str=False     ,   a     :				Any=None     ,   a     :				int=False     ,   ):






       '''simple docstring'''






       lowercase_		:			Any							=   bert_score.utils.get_hash
       lowercase_		:			int							=   bert_score.BERTScorer

       if version.parse(bert_score.__version__	) >= version.parse("0.3.10"	):
          lowercase_		:			Union[str, Any]							=   functools.partial(_lowerCamelCase     ,   use_fast_tokenizer=_lowerCamelCase	)
          lowercase_		:			Tuple							=   functools.partial(_lowerCamelCase     ,   use_fast_tokenizer=_lowerCamelCase	)
       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"
          lowercase_		:			Dict							=   bert_score.utils.langamodel[lang.lower()]

       if num_layers is None:
          lowercase_		:			Optional[int]							=   bert_score.utils.modelalayers[model_type]

       lowercase_		:			Any							=   get_hash(
           model=_lowerCamelCase     ,   num_layers=_lowerCamelCase     ,   idf=_lowerCamelCase     ,   rescale_with_baseline=_lowerCamelCase     ,   use_custom_baseline=baseline_path is not None     ,   )

       with filter_logging_context():
          if not hasattr(self     ,   "cached_bertscorer"	) or self.cached_bertscorer.hash != hashcode:
             lowercase_		:			Any							=   scorer(
                 model_type=_lowerCamelCase     ,   num_layers=_lowerCamelCase     ,   batch_size=_lowerCamelCase     ,   nthreads=_lowerCamelCase     ,   all_layers=_lowerCamelCase     ,   idf=_lowerCamelCase     ,   device=_lowerCamelCase     ,   lang=_lowerCamelCase     ,   rescale_with_baseline=_lowerCamelCase     ,   baseline_path=_lowerCamelCase     ,   )

       ((lowercase_)			,						(lowercase_)			,						(lowercase_))		:			Any							=   self.cached_bertscorer.score(
           cands=_lowerCamelCase     ,   refs=_lowerCamelCase     ,   verbose=_lowerCamelCase     ,   batch_size=_lowerCamelCase     ,   )
       lowercase_		:			Union[str, Any]							=   {
           "precision": P.tolist(),
           "recall": R.tolist(),
           "f1": F.tolist(),
           "hashcode": hashcode,
       }
       return output_dict




    def  lowerCAmelCase__ (	self     :				Union[str, Any]     ,   a     :				int=None     ,   a     :				int=None     ,   **a     :				Tuple	):






       '''simple docstring'''






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





    def  lowerCAmelCase__ (	self     :				List[str]     ,   a     :				List[Any]=None     ,   a     :				str=None     ,   **a     :				List[str]	):






       '''simple docstring'''






       if isinstance(_lowerCamelCase     ,   _lowerCamelCase	):
          lowercase_		:			Any							=   [reference]
       super().add(prediction=_lowerCamelCase     ,   reference=_lowerCamelCase     ,   **_lowerCamelCase	)





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							=logging.get_logger(__name__)

a							={
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Optional[int]  							=       '''mpnet'''

				def __init__(      self	, _lowerCamelCase=30527	, _lowerCamelCase=768	, _lowerCamelCase=12	, _lowerCamelCase=12	, _lowerCamelCase=3072	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=0.0_2	, _lowerCamelCase=1E-12	, _lowerCamelCase=32	, _lowerCamelCase=1	, _lowerCamelCase=0	, _lowerCamelCase=2	, **_lowerCamelCase	, ):
										super().__init__(pad_token_id=_lowerCamelCase	, bos_token_id=_lowerCamelCase	, eos_token_id=_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=num_attention_heads
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=initializer_range
										lowerCamelCase__					=layer_norm_eps
										lowerCamelCase__					=relative_attention_num_buckets


