






"""simple docstring"""
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


A_     :					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"

A_     :					Dict							=     "\\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"


A_     :					List[Any]							=     "\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					):







      '''simple docstring'''

      def      a__				(self   ):

       '''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      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



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





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=3_2,	lowerCamelCase_=1_2_8,	lowerCamelCase_=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:
        lowerCamelCase__	:     str								=		'gpu' if torch.cuda.is_available() else 'cpu'
       lowerCamelCase__	:     Tuple								=		TrainingArguments(
           'trainer',	fpaa=(device == 'gpu'),	per_device_eval_batch_size=lowerCamelCase_,	report_to='all',	no_cuda=(device == 'cpu'),	log_level='warning',	)
       lowerCamelCase__	:     List[str]								=		{'sentence1': predictions, 'sentence2': references}
       lowerCamelCase__	:     Optional[Any]								=		datasets.Dataset.from_dict(lowerCamelCase_   )

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

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









"""simple docstring"""
class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		data
       lowerCamelCase__	:     BinaryTreeNode | None								=		None
       lowerCamelCase__	:     BinaryTreeNode | None								=		None






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if node is None:
  lowerCamelCase__	:     List[str]								=		BinaryTreeNode(_lowerCamelCase							)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase__	:     str								=		insert(node.left_child     ,  _lowerCamelCase							)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase__	:     str								=		insert(node.right_child     ,  _lowerCamelCase							)
 return node






def 			lowerCamelCase_    (		_lowerCamelCase							):  # if node is None,return
 if node:
  lowerCamelCase__	:     Any								=		inorder(node.left_child							)
  lowerCamelCase__	:     Tuple								=		[*inorder_array, node.data]
  lowerCamelCase__	:     List[Any]								=		inorder_array + inorder(node.right_child							)
 else:
  lowerCamelCase__	:     Any								=		[]
 return inorder_array






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		insert(_lowerCamelCase     ,  15							)
 insert(_lowerCamelCase     ,  10							)
 insert(_lowerCamelCase     ,  25							)
 insert(_lowerCamelCase     ,  6							)
 insert(_lowerCamelCase     ,  14							)
 insert(_lowerCamelCase     ,  20							)
 insert(_lowerCamelCase     ,  60							)
 return root






def 			lowerCamelCase_    (		):
 # main function
 lowerCamelCase__	:     List[Any]								=		make_tree()
 print('Printing values of binary search tree in Inorder Traversal.'							)
 inorder(_lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()

