




'''simple docstring'''
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


A				: List[Any]					      =			"""\
@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				: Union[str, Any]					      =			"""\
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				: Union[str, Any]					      =			"""
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 			SCREAMING_SNAKE_CASE(							datasets.Metric	):

       def        snake_case__     (       self	)       ->   Union[str, Any]:

           """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        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Optional[int]:

           """simple docstring"""



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




       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__=32   ,						lowerCamelCase__=128   ,						lowerCamelCase__=None   ,						)       ->   Dict:

           """simple docstring"""



           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:
               __lowercase     							=		"""gpu""" if torch.cuda.is_available() else """cpu"""
           __lowercase     							=		TrainingArguments(
               """trainer"""   ,						fpaa=(device == """gpu""")   ,						per_device_eval_batch_size=lowerCamelCase__   ,						report_to="""all"""   ,						no_cuda=(device == """cpu""")   ,						log_level="""warning"""   ,						)
           __lowercase     							=		{"""sentence1""": predictions, """sentence2""": references}
           __lowercase     							=		datasets.Dataset.from_dict(lowerCamelCase__	)

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

           __lowercase     							=		raw_datasets.map(lowerCamelCase__   ,						batched=lowerCamelCase__	)
           tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]	)
           __lowercase     							=		Trainer(self.model   ,						lowerCamelCase__   ,						tokenizer=self.tokenizer	)
           __lowercase     							=		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())




