



'''simple docstring'''






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

import datasets


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

A_				:					int							=      """\
FrugalScore 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.
"""


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



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


 """simple docstring"""


 def _snake_case					(				self				)						->  Any:
        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 _snake_case					(				self    ,a_				)						->  int:
        if self.config_name == "default":
               _UpperCAmelCase    :		Optional[Any]     	=					"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
        else:
               _UpperCAmelCase    :		List[str]     	=					self.config_name
        _UpperCAmelCase    :		List[Any]     	=					AutoModelForSequenceClassification.from_pretrained(a_				)
        _UpperCAmelCase    :		Any     	=					AutoTokenizer.from_pretrained(a_				)







 def _snake_case					(				self    ,a_    ,a_    ,a_=32    ,a_=128    ,a_=None    ,)						->  Optional[Any]:
        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:
               _UpperCAmelCase    :		Tuple     	=					"""gpu""" if torch.cuda.is_available() else """cpu"""
        _UpperCAmelCase    :		Optional[int]     	=					TrainingArguments(
            """trainer"""    ,fpaa=(device == """gpu""")    ,per_device_eval_batch_size=a_    ,report_to="""all"""    ,no_cuda=(device == """cpu""")    ,log_level="""warning"""    ,)
        _UpperCAmelCase    :		Optional[Any]     	=					{"""sentence1""": predictions, """sentence2""": references}
        _UpperCAmelCase    :		Any     	=					datasets.Dataset.from_dict(a_				)

        def tokenize_function(a_				):
               return self.tokenizer(
                   data["""sentence1"""]    ,data["""sentence2"""]    ,max_length=a_    ,truncation=a_    ,padding=a_				)

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








'''simple docstring'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


       if n < 0:
              return 0
       # table[i] represents the number of ways to get to amount i
       _UpperCAmelCase    :		Union[str, Any]     	=					[0] * (n + 1)

       # There is exactly 1 way to get to zero(You pick no coins).
       _UpperCAmelCase    :		Optional[int]     	=					1

       # Pick all coins one by one and update table[] values
       # after the index greater than or equal to the value of the
       # picked coin
       for coin_val in s:
              for j in range(lowerCAmelCase_			,  n + 1						):
                     table[j] += table[j - coin_val]

       return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



