


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

import datasets


__UpperCAmelCase      			=  """\
@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}
}
"""

__UpperCAmelCase      			=  """\
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.
"""


__UpperCAmelCase      			=  """
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    UpperCAmelCase							(		self)					->						Dict:







         """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    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         if self.config_name == "default":
            UpperCamelCase__ :			Optional[Any] 						="moussaKam/frugalscore_tiny_bert-base_bert-score"
         else:
            UpperCamelCase__ :			str 						=self.config_name
         UpperCamelCase__ :			Dict 						=AutoModelForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			str 						=AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=32   ,			__SCREAMING_SNAKE_CASE=1_28   ,			__SCREAMING_SNAKE_CASE=None   ,			)					->						Optional[int]:







         """simple docstring"""







         assert len(__SCREAMING_SNAKE_CASE) == len(
             __SCREAMING_SNAKE_CASE), "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__ :			List[Any] 						="gpu" if torch.cuda.is_available() else "cpu"
         UpperCamelCase__ :			Any 						=TrainingArguments(
             "trainer"   ,			fpaa=(device == "gpu")   ,			per_device_eval_batch_size=__SCREAMING_SNAKE_CASE   ,			report_to="all"   ,			no_cuda=(device == "cpu")   ,			log_level="warning"   ,			)
         UpperCamelCase__ :			Optional[int] 						={"sentence1": predictions, "sentence2": references}
         UpperCamelCase__ :			Dict 						=datasets.Dataset.from_dict(__SCREAMING_SNAKE_CASE)

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

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





def 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

