




'''simple docstring'''





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

import datasets


a		:  Optional[Any]   			=			'''\
@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}
}
'''

a		:  List[str]   			=			'''\
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.
'''


a		:  Dict   			=			'''
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     UpperCamelCase					(  datasets.Metric					):



  def 						a_       ( 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 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      if self.config_name == "default":
          _a							:       str       					=			"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
      else:
          _a							:       Tuple       					=			self.config_name
      _a							:       Tuple       					=			AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase   )
      _a							:       str       					=			AutoTokenizer.from_pretrained(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=32		,    __lowerCAmelCase=128		,    __lowerCAmelCase=None		,    ):







      '''simple docstring'''


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

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

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








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a		:  Dict   			=			{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MobileViTV2ForImageClassification''',
      '''MobileViTV2ForSemanticSegmentation''',
      '''MobileViTV2Model''',
      '''MobileViTV2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  a		:  Dict   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


