"""simple docstring"""







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

import datasets


lowerCamelCase      : Tuple       ="""\
@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      : int       ="""\
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      : Tuple       ="""
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 							__snake_case(					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":
              __A					:					Optional[Any]								=		"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
         else:
              __A					:					List[str]								=		self.config_name
         __A					:					List[Any]								=		AutoModelForSequenceClassification.from_pretrained(a_	)
         __A					:					Any								=		AutoTokenizer.from_pretrained(a_	)

    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase=32					,       __lowerCamelCase=128					,       __lowerCamelCase=None					,       ):




         '''simple docstring'''



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

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

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








'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

        return np.mean((labels - prediction) ** 2				)







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               print("""Error: X and y have different lengths"""				)
               return
        if y.ndim != 1:
               print("""Error: Data set labels must be one dimensional"""				)
               return

        if len(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

        _UpperCAmelCase    :		Union[str, Any]     	=					0
        _UpperCAmelCase    :		Optional[Any]     	=					self.mean_squared_error(a_    ,np.mean(a_				)				) * 2
        for i in range(len(a_				)				):
               if len(x[:i]				) < self.min_leaf_size:
                      continue
               elif len(x[i:]				) < self.min_leaf_size:
                      continue
               else:
                      _UpperCAmelCase    :		Dict     	=					self.mean_squared_error(x[:i]    ,np.mean(y[:i]				)				)
                      _UpperCAmelCase    :		Any     	=					self.mean_squared_error(x[i:]    ,np.mean(y[i:]				)				)
                      _UpperCAmelCase    :		Optional[Any]     	=					error_left + error_right
                      if error < min_error:
                             _UpperCAmelCase    :		Union[str, Any]     	=					i
                             _UpperCAmelCase    :		Optional[int]     	=					error

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        if self.prediction is not None:
               return self.prediction
        elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                      return self.right.predict(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		Optional[Any]     	=					np.mean((predictions - test_cases) ** 2						)

       print("""Test values: """ + str(lowerCAmelCase_						)						)
       print("""Predictions: """ + str(lowerCAmelCase_						)						)
       print("""Average error: """ + str(lowerCAmelCase_						)						)


if __name__ == "__main__":
   main()
   import doctest

   doctest.testmod(name="""mean_squarred_error""", verbose=True)



