



def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  int:






      '''simple docstring'''







      for index, item in enumerate(__snake_case     ):
            if item == target:
                  return index
      return -1







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case     )  ->  int:






      '''simple docstring'''







      if not (0 <= high < len(__snake_case     ) and 0 <= low < len(__snake_case     )):
            raise Exception("Invalid upper or lower bound!"     )
      if high < low:
            return -1
      if sequence[low] == target:
            return low
      if sequence[high] == target:
            return high
      return rec_linear_search(__snake_case       ,	low + 1       ,	high - 1       ,	__snake_case     )


if __name__ == "__main__":
   __lowerCamelCase								=      input('''Enter numbers separated by comma:\n''').strip()
   __lowerCamelCase								=      [int(item.strip()) for item in user_input.split(''',''')]

   __lowerCamelCase								=      int(input('''Enter a single number to be found in the list:\n''').strip())
   __lowerCamelCase								=      linear_search(sequence, target)
   if result != -1:
      print(F'''linear_search({sequence}, {target}) = {result}''')
   else:
      print(F'''{target} was not found in {sequence}''')





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

import datasets


__lowerCamelCase								=      '''\
@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}
}
'''

__lowerCamelCase								=      '''\
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.
'''


__lowerCamelCase								=      '''
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 	snake_case_			(datasets.Metric	):




       """simple docstring"""






       def 			A_ (  self):

             """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 			A_ (  self  ,lowercase):

             """simple docstring"""





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



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase=32  ,lowercase=128  ,lowercase=None  ,):

             """simple docstring"""





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

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

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