"""simple docstring"""




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      UpperCamelCase__(      datasets.Metric			):





						def 					snake_case__						(							self  )       ->					Union[str, 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		,__UpperCAmelCase  )       ->					Optional[int]:



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





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=32		,__UpperCAmelCase=1_28		,__UpperCAmelCase=None		,)       ->					Dict:
													assert len(lowerCamelCase__  ) == len(
													    lowerCamelCase__  ), "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=lowerCamelCase__		,report_to='all'		,no_cuda=(device == 'cpu')		,log_level='warning'		,)
													A__						  =				{'sentence1': predictions, 'sentence2': references}
													A__						  =				datasets.Dataset.from_dict(lowerCamelCase__  )

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

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











'''simple docstring'''
import os
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		os.path.dirname(os.path.realpath(a__     )     )
    __lowercase     							=		os.path.join(a__       ,"""triangle.txt"""     )

    with open(a__     ) as f:
        __lowercase     							=		f.readlines()

    __lowercase     							=		[[int(a__     ) for y in x.rstrip("""\r\n"""     ).split(""" """     )] for x in triangle]

    for i in range(1       ,len(a__     )     ):
        for j in range(len(a[i]     )     ):
            __lowercase     							=		a[i - 1][j] if j != len(a[i - 1]     ) else 0
            __lowercase     							=		a[i - 1][j - 1] if j > 0 else 0
            a[i][j] += max(a__       ,a__     )
    return max(a[-1]     )


if __name__ == "__main__":
  print(solution())




