

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase     =      logging.get_logger(__name__)







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Any												=     ['input_values', 'attention_mask']



   def __init__(       self,					lowercase_=1,					lowercase_=16000,					lowercase_=0.0,					lowercase_=False,					lowercase_=True,					**lowercase_,					)							->					Tuple:






     """simple docstring"""






     super().__init__(feature_size=lowercase_,					sampling_rate=lowercase_,					padding_value=lowercase_,					**lowercase_ )
     a__						=return_attention_mask
     a__						=do_normalize



   @staticmethod
   def 				_UpperCAmelCase				(       lowercase_,					lowercase_,					lowercase_ = 0.0 )							->					List[np.ndarray]:






     """simple docstring"""






     if attention_mask is not None:
       a__						=np.array(lowercase_,					np.intaa )
       a__						=[]

       for vector, length in zip(lowercase_,					attention_mask.sum(-1 ) ):
         a__						=(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
         if length < normed_slice.shape[0]:
           a__						=padding_value

         normed_input_values.append(lowercase_ )
     else:
       a__						=[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

     return normed_input_values



   def __call__(       self,					lowercase_,					lowercase_ = False,					lowercase_ = None,					lowercase_ = False,					lowercase_ = None,					lowercase_ = None,					lowercase_ = None,					lowercase_ = None,					**lowercase_,					)							->					BatchFeature:






     """simple docstring"""






     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             F""" {self.sampling_rate} and not {sampling_rate}.""" )
     else:
       logger.warning(
           '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
           '''Failing to do so can result in silent errors that might be hard to debug.''' )

     a__						=isinstance(lowercase_,					np.ndarray ) and len(raw_speech.shape ) > 1
     if is_batched_numpy and len(raw_speech.shape ) > 2:
       raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
     a__						=is_batched_numpy or (
         isinstance(lowercase_,					(list, tuple) ) and (isinstance(raw_speech[0],					(np.ndarray, tuple, list) ))
     )

     # always return batch
     if not is_batched:
       a__						=[raw_speech]

     # convert into correct format for padding
     a__						=BatchFeature({'''input_values''': raw_speech} )

     a__						=self.pad(
         lowercase_,					padding=lowercase_,					max_length=lowercase_,					truncation=lowercase_,					pad_to_multiple_of=lowercase_,					return_attention_mask=lowercase_,					)

     # convert input values to correct format
     a__						=padded_inputs['''input_values''']
     if not isinstance(input_values[0],					np.ndarray ):
       a__						=[np.asarray(lowercase_,					dtype=np.floataa ) for array in input_values]
     elif (
         not isinstance(lowercase_,					np.ndarray )
         and isinstance(input_values[0],					np.ndarray )
         and input_values[0].dtype is np.dtype(np.floataa )
     ):
       a__						=[array.astype(np.floataa ) for array in input_values]
     elif isinstance(lowercase_,					np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
       a__						=input_values.astype(np.floataa )

     # convert attention_mask to correct format
     a__						=padded_inputs.get('''attention_mask''' )
     if attention_mask is not None:
       a__						=[np.asarray(lowercase_,					dtype=np.intaa ) for array in attention_mask]

     # zero-mean and unit-variance normalization
     if self.do_normalize:
       a__						=(
           attention_mask
           if self._get_padding_strategies(lowercase_,					max_length=lowercase_ ) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       a__						=self.zero_mean_unit_var_norm(
           padded_inputs['''input_values'''],					attention_mask=lowercase_,					padding_value=self.padding_value )

     if return_tensors is not None:
       a__						=padded_inputs.convert_to_tensors(lowercase_ )

     return padded_inputs



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

import datasets


lowerCamelCase     =      '''\
@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}
}
'''

lowerCamelCase     =      '''\
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.
'''


lowerCamelCase     =      '''
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 		__magic_name__    (							datasets.Metric							):

   '''simple docstring'''




   def 				_UpperCAmelCase				(       self )							->					Optional[int]:






     """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,					lowercase_ )							->					Dict:






     """simple docstring"""






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



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_=32,					lowercase_=128,					lowercase_=None,					)							->					Union[str, Any]:






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

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

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