


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        UpperCAmelCase__			(   datasets.Metric						):



    """simple docstring"""






    def _a   (     self       )    ->			Union[str, Any]:
         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 _a   (     self   ,  A_       )    ->			Optional[int]:
         if self.config_name == "default":
              __UpperCamelCase       				='moussaKam/frugalscore_tiny_bert-base_bert-score'
         else:
              __UpperCamelCase       				=self.config_name
         __UpperCamelCase       				=AutoModelForSequenceClassification.from_pretrained(A_       )
         __UpperCamelCase       				=AutoTokenizer.from_pretrained(A_       )






    def _a   (     self   ,  A_   ,  A_   ,  A_=32   ,  A_=128   ,  A_=None   ,  )    ->			List[Any]:
         assert len(A_       ) == len(
             A_       ), "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=A_   ,  report_to='all'   ,  no_cuda=(device == 'cpu')   ,  log_level='warning'   ,  )
         __UpperCamelCase       				={'sentence1': predictions, 'sentence2': references}
         __UpperCamelCase       				=datasets.Dataset.from_dict(A_       )

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

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









_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





