
'''simple docstring'''





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


a__		: List[Any]  							=							'\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

a__		: Dict  							=							'\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


a__		: Optional[int]  							=							'\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,  _KWARGS_DESCRIPTION)
class 			UpperCamelCase__	(     datasets.Metric):






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       return datasets.MetricInfo(
           description=_DESCRIPTION						,					citation=_CITATION						,					inputs_description=_KWARGS_DESCRIPTION						,					features=datasets.Features(
               {
                   'predictions': datasets.Value('string'      ),
                   'references': datasets.Value('string'      ),
               }      )						,					homepage='https://github.com/moussaKam/FrugalScore'						,					)






 def 						lowercase_    (      self	:str						,					_A	:str      )					->      int:







       '''simple docstring'''



       if self.config_name == "default":
             __A													=      'moussaKam/frugalscore_tiny_bert-base_bert-score'
       else:
             __A													=      self.config_name
       __A													=      AutoModelForSequenceClassification.from_pretrained(_A      )
       __A													=      AutoTokenizer.from_pretrained(_A      )






 def 						lowercase_    (      self	:str						,					_A	:str						,					_A	:Union[str, Any]						,					_A	:Any=32						,					_A	:Optional[Any]=128						,					_A	:Tuple=None						,					)					->      Dict:







       '''simple docstring'''



       assert len(_A      ) == len(
           _A      ), "predictions and references should have the same number of sentences."
       if device is not None:
             assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
       else:
             __A													=      'gpu' if torch.cuda.is_available() else 'cpu'
       __A													=      TrainingArguments(
           'trainer'						,					fpaa=(device == 'gpu')						,					per_device_eval_batch_size=_A						,					report_to='all'						,					no_cuda=(device == 'cpu')						,					log_level='warning'						,					)
       __A													=      {'sentence1': predictions, 'sentence2': references}
       __A													=      datasets.Dataset.from_dict(_A      )

       def tokenize_function(_A	:str      ):
             return self.tokenizer(
                 data['sentence1']						,					data['sentence2']						,					max_length=_A						,					truncation=_A						,					padding=_A      )

       __A													=      raw_datasets.map(_A						,					batched=_A      )
       tokenized_datasets.remove_columns(['sentence1', 'sentence2']      )
       __A													=      Trainer(self.model						,					_A						,					tokenizer=self.tokenizer      )
       __A													=      trainer.predict(_A      )
       return {"scores": list(predictions.predictions.squeeze(-1      )      )}









"""simple docstring"""



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class        a    :



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	vocab
         A__				      =	normalizers.BertNormalizer(
             clean_text=UpperCamelCase     ,    handle_chinese_chars=UpperCamelCase     ,    strip_accents=UpperCamelCase     ,    lowercase=UpperCamelCase     ,    )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  """You need to install rjieba to use RoFormerTokenizer. """
                  """See https://pypi.org/project/rjieba/ for installation."""							)
         A__				      =	rjieba

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			int     ,    UpperCamelCase:			NormalizedString							):






         """simple docstring"""


         A__				      =	[]

         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
         for token, start, end in self.jieba.tokenize(str(UpperCamelCase							)     ,    hmm=UpperCamelCase							):
              if token in self.vocab:
                   splits.append(normalized_string[start:end]							)
              else:
                   A__				      =	self.normalizers.normalize_str(UpperCamelCase							).split()
                   for token in token_list:
                        if token:
                             A__				      =	start + len(UpperCamelCase							)
                             splits.append(normalized_string[start:end]							)
                             A__				      =	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

         return splits





    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			PreTokenizedString							):






         """simple docstring"""


         pretok.split(self.jieba_split							)






