

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

import datasets


__A     						=      '\\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     						=      '\\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     						=      '\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     lowercase_       (    datasets.Metric	):



      def 		UpperCamelCase_						(     self :							Tuple						)       ->	Optional[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 		UpperCamelCase_						(     self :							Dict     ,   A__ :							List[str]						)       ->	Optional[int]:
        if self.config_name == "default":
          _snake_case		=				"moussaKam/frugalscore_tiny_bert-base_bert-score"
        else:
          _snake_case		=				self.config_name
        _snake_case		=				AutoModelForSequenceClassification.from_pretrained(_A						)
        _snake_case		=				AutoTokenizer.from_pretrained(_A						)







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

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

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






def 						snake_case__							(       __lowercase = 1_0_0_0							)		->		int:







						"""simple docstring"""

						A__						:				int         =			0
						A__						:				Optional[int]         =			0
						while 1:
												num += 3
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
						return total


if __name__ == "__main__":
		print(f"""{solution() = }""")