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

import datasets


_lowerCAmelCase        =       '\\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'

_lowerCAmelCase        =       '\\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'


_lowerCAmelCase        =       '\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				):






 """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 ,       __magic_name__      ):

        """simple docstring"""

        if self.config_name == "default":
               A_							:       Optional[int]	      =				'''moussaKam/frugalscore_tiny_bert-base_bert-score'''
        else:
               A_							:       Union[str, Any]	      =				self.config_name
        A_							:       List[Any]	      =				AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE      )
        A_							:       Optional[Any]	      =				AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__=32 ,       __magic_name__=128 ,       __magic_name__=None ,       ):

        """simple docstring"""

        assert len(_SCREAMING_SNAKE_CASE      ) == len(
            _SCREAMING_SNAKE_CASE      ), "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_							:       List[str]	      =				'''gpu''' if torch.cuda.is_available() else '''cpu'''
        A_							:       Dict	      =				TrainingArguments(
            '''trainer''' ,       fpaa=(device == '''gpu''') ,       per_device_eval_batch_size=_SCREAMING_SNAKE_CASE ,       report_to='''all''' ,       no_cuda=(device == '''cpu''') ,       log_level='''warning''' ,       )
        A_							:       Any	      =				{'''sentence1''': predictions, '''sentence2''': references}
        A_							:       Tuple	      =				datasets.Dataset.from_dict(_SCREAMING_SNAKE_CASE      )

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

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









def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	word.upper()
       UpperCamelCase_ 							=	0
       UpperCamelCase_ 							=	{}
       UpperCamelCase_ 							=	[]

       for letter in word:
              if letter not in letter_nums:
                     UpperCamelCase_ 							=	str(UpperCamelCase_	)
                     next_num += 1
              word_pattern.append(letter_nums[letter]	)
       return ".".join(UpperCamelCase_	)


if __name__ == "__main__":
     import pprint
     import time

     _UpperCAmelCase										=					time.time()
     with open('dictionary.txt') as in_file:
          _UpperCAmelCase										=					in_file.read().splitlines()

     _UpperCAmelCase										=					{}
     for word in word_list:
          _UpperCAmelCase										=					get_word_pattern(word)
          if pattern in all_patterns:
               all_patterns[pattern].append(word)
          else:
               _UpperCAmelCase										=					[word]

     with open('word_patterns.txt', 'w') as out_file:
          out_file.write(pprint.pformat(all_patterns))

     _UpperCAmelCase										=					round(time.time() - start_time, 2)
     print(f'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
     # Done!  9,581 word patterns found in 0.58 seconds.

