
"""simple docstring"""





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 A_      (datasets.Metric ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """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 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""


     if self.config_name == "default":
        UpperCAmelCase_   :   Tuple     	=       "moussaKam/frugalscore_tiny_bert-base_bert-score"
     else:
        UpperCAmelCase_   :   Union[str, Any]     	=       self.config_name
     UpperCAmelCase_   :   str     	=       AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase    )
     UpperCAmelCase_   :   Union[str, Any]     	=       AutoTokenizer.from_pretrained(__UpperCamelCase    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_=32							,  lowercase_=128							,  lowercase_=None							,  ):

     """simple docstring"""
     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:
        UpperCAmelCase_   :   List[Any]     	=       "gpu" if torch.cuda.is_available() else "cpu"
     UpperCAmelCase_   :   List[Any]     	=       TrainingArguments(
         "trainer"							,  fpaa=(device == "gpu")							,  per_device_eval_batch_size=__UpperCamelCase							,  report_to="all"							,  no_cuda=(device == "cpu")							,  log_level="warning"							,  )
     UpperCAmelCase_   :   List[str]     	=       {"sentence1": predictions, "sentence2": references}
     UpperCAmelCase_   :   List[str]     	=       datasets.Dataset.from_dict(__UpperCamelCase    )

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

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




_A							=       [chr(i) for i in range(32, 126)]
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(reversed(range(len(alphabets))))
_A							=       []
_A							=       _A							=       _A							=       0




def 					lowerCamelCase__					(    )		->						List[str]:
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       UpperCamelCase_											=							gear_one[0]
       gear_one.append(a__  )
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(a__  )  ) == 0:
              UpperCamelCase_											=							gear_two[0]
              gear_two.append(a__  )
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(a__  )  ) == 0:
                     UpperCamelCase_											=							gear_three[0]
                     gear_three.append(a__  )
                     del gear_three[0]
                     gear_three_pos += 1




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							alphabets.index(a__  )
       UpperCamelCase_											=							gear_one[target]
       UpperCamelCase_											=							gear_two[target]
       UpperCamelCase_											=							gear_three[target]
       UpperCamelCase_											=							reflector[target]
       UpperCamelCase_											=							gear_three.index(a__  )
       UpperCamelCase_											=							gear_two.index(a__  )
       UpperCamelCase_											=							gear_one.index(a__  )
       code.append(alphabets[target]  )
       rotator()


if __name__ == "__main__":
      _A							=       list(input('''Type your message:\n'''))
      while True:
            try:
                  _A							=       int(input('''Please set token:(must be only digits)\n'''))
                  break
            except Exception as error:
                  print(error)
      for _ in range(token):
            rotator()
      for j in decode:
            engine(j)
      print('''\n''' + ''''''.join(code))
      print(
          F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
          '''this message again you should input same digits as token!'''
      )



