






"""simple docstring"""




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

import datasets


_snake_case					   =     '\\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'

_snake_case					   =     '\\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'


_snake_case					   =     '\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  UpperCamelCase    (			datasets.Metric   ):


 def _lowercase			(  self	:		str							)      -> str:
   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 _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		List[str]							)      -> Optional[Any]:
   if self.config_name == "default":
     _a						: str    =			"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
   else:
     _a						: Optional[Any]    =			self.config_name
   _a						: Optional[Any]    =			AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__							)
   _a						: int    =			AutoTokenizer.from_pretrained(UpperCAmelCase__							)







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

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

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









"""simple docstring"""




from __future__ import annotations

import sys

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n < 0:
    raise ValueError("""Negative arguments are not supported"""       )
  return _fib(UpperCamelCase__       )[0]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n == 0:  # (F(0), F(1))
    return (0, 1)

  # F(2n) = F(n)[2F(n+1) − F(n)]
  # F(2n+1) = F(n+1)^2+F(n)^2
  _a   ,						_a						: Dict    =			_fib(n // 2       )
  _a						: Dict    =			a * (b * 2 - a)
  _a						: Optional[Any]    =			a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 _snake_case					   =     int(sys.argv[1])
 print(F'''fibonacci({n}) is {fibonacci(n)}''')

