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

import datasets


_A							=       '''\
@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							=       '''\
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							=       '''
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					):

 def lowerCamelCase_					(   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 lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase=3_2  ,						__UpperCamelCase=1_2_8  ,						__UpperCamelCase=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:
               UpperCamelCase_											=							"""gpu""" if torch.cuda.is_available() else """cpu"""
        UpperCamelCase_											=							TrainingArguments(
            """trainer"""  ,						fpaa=(device == """gpu""")  ,						per_device_eval_batch_size=__UpperCamelCase  ,						report_to="""all"""  ,						no_cuda=(device == """cpu""")  ,						log_level="""warning"""  ,						)
        UpperCamelCase_											=							{"""sentence1""": predictions, """sentence2""": references}
        UpperCamelCase_											=							datasets.Dataset.from_dict(__UpperCamelCase  )

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=       {'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _A							=       [
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      _A							=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



