

"""simple docstring"""



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

import datasets


SCREAMING_SNAKE_CASE_       :						List[Any]					=    '\\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'

SCREAMING_SNAKE_CASE_       :						Dict					=    '\\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'


SCREAMING_SNAKE_CASE_       :						Optional[int]					=    '\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:			Union[str, Any]							):






         """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:			str     ,    UpperCamelCase:			str							):






         """simple docstring"""


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





    def 					UpperCamelCase						(  self:			str     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Union[str, Any]     ,    UpperCamelCase:			Any=32     ,    UpperCamelCase:			Optional[Any]=1_28     ,    UpperCamelCase:			Tuple=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:
              A__				      =	"""gpu""" if torch.cuda.is_available() else """cpu"""
         A__				      =	TrainingArguments(
             """trainer"""     ,    fpaa=(device == """gpu""")     ,    per_device_eval_batch_size=UpperCamelCase     ,    report_to="""all"""     ,    no_cuda=(device == """cpu""")     ,    log_level="""warning"""     ,    )
         A__				      =	{"""sentence1""": predictions, """sentence2""": references}
         A__				      =	datasets.Dataset.from_dict(UpperCamelCase							)

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

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









"""simple docstring"""



from __future__ import annotations





def 			_snake_case  (     UpperCAmelCase_	:       list[list[int]]       , UpperCAmelCase_	:       list[list[int]]      ):
     assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )

     A__				      =	len(UpperCAmelCase_      )
     A__				      =	list(range(UpperCAmelCase_      )      )
     A__				      =	[-1] * n  # who the donor has donated to
     A__				      =	[-1] * n  # who the recipient has received from
     A__				      =	[0] * n

     while unmatched_donors:
          A__				      =	unmatched_donors[0]
          A__				      =	donor_pref[donor]
          A__				      =	donor_preference[num_donations[donor]]
          num_donations[donor] += 1
          A__				      =	recipient_pref[recipient]
          A__				      =	rec_record[recipient]

          if prev_donor != -1:
               if rec_preference.index(UpperCAmelCase_      ) > rec_preference.index(UpperCAmelCase_      ):
                    A__				      =	donor
                    A__				      =	recipient
                    unmatched_donors.append(UpperCAmelCase_      )
                    unmatched_donors.remove(UpperCAmelCase_      )
          else:
               A__				      =	donor
               A__				      =	recipient
               unmatched_donors.remove(UpperCAmelCase_      )
     return donor_record






