


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase						 =	{
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase						 =	[
     'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'Speech2Text2ForCausalLM',
     'Speech2Text2PreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

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






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

import datasets


lowerCamelCase						 =	'\\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'

lowerCamelCase						 =	'\\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'


lowerCamelCase						 =	'\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 A  (       datasets.Metric					):






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """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						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




             if self.config_name == "default":
                    _lowerCamelCase	:		Optional[int]          ='moussaKam/frugalscore_tiny_bert-base_bert-score'
             else:
                    _lowerCamelCase	:		List[Any]          =self.config_name
             _lowerCamelCase	:		str          =AutoModelForSequenceClassification.from_pretrained(lowercase_			)
             _lowerCamelCase	:		str          =AutoTokenizer.from_pretrained(lowercase_			)






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """simple docstring"""




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

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

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


