

import sys

from transformers import AutoTokenizer


lowerCamelCase     =      sys.argv[1]
lowerCamelCase     =      sys.argv[2]
lowerCamelCase     =      int(sys.argv[3])

lowerCamelCase     =      0

lowerCamelCase     =      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
       for line in f_p:
              lowerCamelCase     =      line.rstrip()

              if not line:
                     print(line)
                     lowerCamelCase     =      0
                     continue

              lowerCamelCase     =      line.split()[0]

              lowerCamelCase     =      len(tokenizer.tokenize(token))

              # Token contains strange control characters like \x96 or \x95
              # Just filter out the complete line
              if current_subwords_len == 0:
                     continue

              if (subword_len_counter + current_subwords_len) > max_len:
                     print('''''')
                     print(line)
                     lowerCamelCase     =      current_subwords_len
                     continue

              subword_len_counter += current_subwords_len

              print(line)



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

import datasets


lowerCamelCase     =      '''\
@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}
}
'''

lowerCamelCase     =      '''\
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.
'''


lowerCamelCase     =      '''
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 		__magic_name__    (							datasets.Metric							):

   '''simple docstring'''




   def 				_UpperCAmelCase				(       self )							->					Optional[int]:






     """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_ )							->					Dict:






     """simple docstring"""






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



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_=32,					lowercase_=128,					lowercase_=None,					)							->					Union[str, Any]:






     """simple docstring"""






     assert len(lowercase_ ) == len(
         lowercase_ ), "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=lowercase_,					report_to='''all''',					no_cuda=(device == '''cpu'''),					log_level='''warning''',					)
     a__						={'''sentence1''': predictions, '''sentence2''': references}
     a__						=datasets.Dataset.from_dict(lowercase_ )

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

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