



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

import datasets


UpperCamelCase_									=   '''\
@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}
}
'''

UpperCamelCase_									=   '''\
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.
'''


UpperCamelCase_									=   '''
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       _snake_case				(     datasets.Metric ):






   '''simple docstring'''






   def       A__    (    self:     Any							)	->      Optional[int]:
       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:     Union[str, Any]  ,lowerCamelCase_:     str							)	->      Tuple:
       if self.config_name == "default":
           UpperCAmelCase_     :       Optional[Any]  		=					"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
       else:
           UpperCAmelCase_     :       str  		=					self.config_name
       UpperCAmelCase_     :       int  		=					AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					AutoTokenizer.from_pretrained(lowerCamelCase_							)






   def       A__    (    self:     int  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Tuple=32  ,lowerCamelCase_:     Tuple=128  ,lowerCamelCase_:     Optional[int]=None  ,)	->      List[str]:
       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:
           UpperCAmelCase_     :       Dict  		=					"""gpu""" if torch.cuda.is_available() else """cpu"""
       UpperCAmelCase_     :       Union[str, Any]  		=					TrainingArguments(
           """trainer"""  ,fpaa=(device == """gpu""")  ,per_device_eval_batch_size=lowerCamelCase_  ,report_to="""all"""  ,no_cuda=(device == """cpu""")  ,log_level="""warning"""  ,)
       UpperCAmelCase_     :       Optional[int]  		=					{"""sentence1""": predictions, """sentence2""": references}
       UpperCAmelCase_     :       Optional[int]  		=					datasets.Dataset.from_dict(lowerCamelCase_							)

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

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




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizerFast"]


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez import BarthezTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez_fast import BarthezTokenizerFast

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)