



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 			__snake_case   ( datasets.Metric):







							'''simple docstring'''






							def _a   (				self		):
											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	,	a_		):
											if self.config_name == "default":
															a__					   =		"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
											else:
															a__					   =		self.config_name
											a__					   =		AutoModelForSequenceClassification.from_pretrained(a_		)
											a__					   =		AutoTokenizer.from_pretrained(a_		)







							def _a   (				self	,	a_	,	a_	,	a_=32	,	a_=128	,	a_=None	,	):
											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:
															a__					   =		"""gpu""" if torch.cuda.is_available() else """cpu"""
											a__					   =		TrainingArguments(
											    """trainer"""	,	fpaa=(device == """gpu""")	,	per_device_eval_batch_size=a_	,	report_to="""all"""	,	no_cuda=(device == """cpu""")	,	log_level="""warning"""	,	)
											a__					   =		{"""sentence1""": predictions, """sentence2""": references}
											a__					   =		datasets.Dataset.from_dict(a_		)

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

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





_A		:					Optional[int]          =							{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]			)							->				str:
   return c.isdigit()



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]			)							->				str:
   __lowerCamelCase:					List[str]	 =						[]

   # iterate over the string in reverse order
   for c in expression.split()[::-1]:
      # push operand to stack
      if is_operand(snake_case			):
         stack.append(int(snake_case			)			)

      else:
         # pop values from stack can calculate the result
         # push the result onto the stack again
         __lowerCamelCase:					Any	 =						stack.pop()
         __lowerCamelCase:					int	 =						stack.pop()
         stack.append(calc[c](snake_case    ,   snake_case			)			)

   return stack.pop()


# Driver code
if __name__ == "__main__":
   _A		:					int          =							'''+ 9 * 2 6'''
   print(evaluate(test_expression))

   _A		:					str          =							'''/ * 10 2 + 4 1 '''
   print(evaluate(test_expression))


