





'''simple docstring'''



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

import datasets


_SCREAMING_SNAKE_CASE    =			"""\
@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}
}
"""

_SCREAMING_SNAKE_CASE    =			"""\
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.
"""


_SCREAMING_SNAKE_CASE    =			"""
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 							__lowercase				(					datasets.Metric  ):







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							str:







				'''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   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=32   ,_lowerCamelCase=128   ,_lowerCamelCase=None   ,)							->							Any:







				'''simple docstring'''





				assert len(snake_case_       ) == len(
				    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:
							__lowercase   =							"""gpu""" if torch.cuda.is_available() else """cpu"""
				__lowercase   =							TrainingArguments(
				    '''trainer'''   ,fpaa=(device == '''gpu''')   ,per_device_eval_batch_size=snake_case_   ,report_to='''all'''   ,no_cuda=(device == '''cpu''')   ,log_level='''warning'''   ,)
				__lowercase   =							{"""sentence1""": predictions, """sentence2""": references}
				__lowercase   =							datasets.Dataset.from_dict(snake_case_       )

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

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







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowerCamelCase_  :       List[Any]					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				Dict  ,		*snake_case_       :				Optional[int]  ,		**snake_case_       :				List[Any]					):
							warnings.warn(
							    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV2ImageProcessor instead."""  ,		snake_case_  ,		)
							super().__init__(*snake_case_  ,		**snake_case_					)






