





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

import datasets


UpperCAmelCase_											=  "\\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"

UpperCAmelCase_											=  "\\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"


UpperCAmelCase_											=  "\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 		UpperCamelCase(    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 		UpperCamelCase(    self							,       _UpperCamelCase				):
        if self.config_name == "default":
           _UpperCAmelCase											=      '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
        else:
           _UpperCAmelCase											=      self.config_name
        _UpperCAmelCase											=      AutoModelForSequenceClassification.from_pretrained(_UpperCamelCase				)
        _UpperCAmelCase											=      AutoTokenizer.from_pretrained(_UpperCamelCase				)





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

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

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






def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     str:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise TypeError('''plaintext must be a string'''	)
   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise TypeError('''key must be a string'''	)

   if not plaintext:
      raise ValueError('''plaintext is empty'''	)
   if not key:
      raise ValueError('''key is empty'''	)

   key += plaintext
   _UpperCAmelCase											=      plaintext.lower()
   _UpperCAmelCase											=      key.lower()
   _UpperCAmelCase											=      0
   _UpperCAmelCase											=      0
   _UpperCAmelCase											=      ''''''
   while plaintext_iterator < len(SCREAMING_SNAKE_CASE_	):
      if (
          ord(plaintext[plaintext_iterator]	) < 97
          or ord(plaintext[plaintext_iterator]	) > 1_22
      ):
         ciphertext += plaintext[plaintext_iterator]
         plaintext_iterator += 1
      elif ord(key[key_iterator]	) < 97 or ord(key[key_iterator]	) > 1_22:
         key_iterator += 1
      else:
         ciphertext += chr(
             (
                 (ord(plaintext[plaintext_iterator]	) - 97 + ord(key[key_iterator]	))
                 - 97
             )
             % 26
             + 97	)
         key_iterator += 1
         plaintext_iterator += 1
   return ciphertext
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     str:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise TypeError('''ciphertext must be a string'''	)
   if not isinstance(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	):
      raise TypeError('''key must be a string'''	)

   if not ciphertext:
      raise ValueError('''ciphertext is empty'''	)
   if not key:
      raise ValueError('''key is empty'''	)

   _UpperCAmelCase											=      key.lower()
   _UpperCAmelCase											=      0
   _UpperCAmelCase											=      0
   _UpperCAmelCase											=      ''''''
   while ciphertext_iterator < len(SCREAMING_SNAKE_CASE_	):
      if (
          ord(ciphertext[ciphertext_iterator]	) < 97
          or ord(ciphertext[ciphertext_iterator]	) > 1_22
      ):
         plaintext += ciphertext[ciphertext_iterator]
      else:
         plaintext += chr(
             (ord(ciphertext[ciphertext_iterator]	) - ord(key[key_iterator]	)) % 26
             + 97	)
         key += chr(
             (ord(ciphertext[ciphertext_iterator]	) - ord(key[key_iterator]	)) % 26
             + 97	)
         key_iterator += 1
      ciphertext_iterator += 1
   return plaintext


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   UpperCAmelCase_											=  int(input("Type 1 to encrypt or 2 to decrypt:"))
   if operation == 1:
      UpperCAmelCase_											=  input("Typeplaintext to be encrypted:\n")
      UpperCAmelCase_											=  input("Type the key:\n")
      print(encrypt(plaintext, key))
   elif operation == 2:
      UpperCAmelCase_											=  input("Type the ciphertext to be decrypted:\n")
      UpperCAmelCase_											=  input("Type the key:\n")
      print(decrypt(ciphertext, key))
   decrypt("jsqqs avvwo", "coffee")