

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class 		__magic_name__    (							lowerCamelCase__	, lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Union[str, Any]												=     'convnext'



   def __init__(       self,					lowercase_=3,					lowercase_=4,					lowercase_=4,					lowercase_=None,					lowercase_=None,					lowercase_="gelu",					lowercase_=0.02,					lowercase_=1E-12,					lowercase_=1E-6,					lowercase_=0.0,					lowercase_=224,					lowercase_=None,					lowercase_=None,					**lowercase_,					)							->					List[str]:






     """simple docstring"""






     super().__init__(**lowercase_ )

     a__						=num_channels
     a__						=patch_size
     a__						=num_stages
     a__						=[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     a__						=[3, 3, 9, 3] if depths is None else depths
     a__						=hidden_act
     a__						=initializer_range
     a__						=layer_norm_eps
     a__						=layer_scale_init_value
     a__						=drop_path_rate
     a__						=image_size
     a__						=['''stem'''] + [F"""stage{idx}""" for idx in range(1,					len(self.depths ) + 1 )]
     a__,   a__						=get_aligned_output_features_output_indices(
         out_features=lowercase_,					out_indices=lowercase_,					stage_names=self.stage_names )







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Tuple												=     version.parse('1.11'							)



   @property
   def 				_UpperCAmelCase				(       self )							->					Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ] )



   @property
   def 				_UpperCAmelCase				(       self )							->					float:






     """simple docstring"""






     return 1E-5



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 ) )}
