
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__      =   {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__      =   ['DetrFeatureExtractor']
   UpperCAmelCase__      =   ['DetrImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__      =   [
       'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DetrForObjectDetection',
       'DetrForSegmentation',
       'DetrModel',
       'DetrPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_detr import DetrFeatureExtractor
      from .image_processing_detr import DetrImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_detr import (
          DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
          DetrForObjectDetection,
          DetrForSegmentation,
          DetrModel,
          DetrPreTrainedModel,
      )

else:
   import sys

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





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

import datasets


UpperCAmelCase__	:  Optional[Any]      =   '\\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'

UpperCAmelCase__	:  List[Any]      =   '\\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'


UpperCAmelCase__	:  List[str]      =   '\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 UpperCAmelCase     (		datasets.Metric     ):





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Dict  ):

      """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 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      if self.config_name == "default":
         _A:						Dict	    = '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
      else:
         _A:						str	    = self.config_name
      _A:						Any	    = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_  )
      _A:						Tuple	    = AutoTokenizer.from_pretrained(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Dict    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Any=3_2    ,    lowerCAmelCase_  :							str=1_2_8    ,    lowerCAmelCase_  :							str=None    ,    ):

      """simple docstring"""
      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:
         _A:						Union[str, Any]	    = '''gpu''' if torch.cuda.is_available() else '''cpu'''
      _A:						Optional[int]	    = TrainingArguments(
          '''trainer'''    ,    fpaa=(device == '''gpu''')    ,    per_device_eval_batch_size=lowerCAmelCase_    ,    report_to='''all'''    ,    no_cuda=(device == '''cpu''')    ,    log_level='''warning'''    ,    )
      _A:						Optional[int]	    = {'''sentence1''': predictions, '''sentence2''': references}
      _A:						Dict	    = datasets.Dataset.from_dict(lowerCAmelCase_  )

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

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



