




"""simple docstring"""




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						):
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					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 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Any:
					if self.config_name == "default":
								__lowercase	:       List[Any]				=   '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
					else:
								__lowercase	:       str				=   self.config_name
					__lowercase	:       List[Any]				=   AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_   )
					__lowercase	:       Dict				=   AutoTokenizer.from_pretrained(UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=32							,		UpperCamelCase_=1_28							,		UpperCamelCase_=None							,		)		->   Optional[int]:
					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:
								__lowercase	:       Union[str, Any]				=   '''gpu''' if torch.cuda.is_available() else '''cpu'''
					__lowercase	:       List[str]				=   TrainingArguments(
					    '''trainer'''							,		fpaa=(device == '''gpu''')							,		per_device_eval_batch_size=UpperCamelCase_							,		report_to='''all'''							,		no_cuda=(device == '''cpu''')							,		log_level='''warning'''							,		)
					__lowercase	:       int				=   {'''sentence1''': predictions, '''sentence2''': references}
					__lowercase	:       List[str]				=   datasets.Dataset.from_dict(UpperCamelCase_   )

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

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









"""simple docstring"""




def       __UpperCAmelCase      (				__UpperCamelCase					):
			if len(__UpperCamelCase					) <= 1:
						return data
			else:
						return [
						    *quick_sort([e for e in data[1:] if e <= data[0]]					),
						    data[0],
						    *quick_sort([e for e in data[1:] if e > data[0]]					),
						]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



