


import numpy as np




class A  :






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int=5      ,       lowercase_						:					Tuple=5			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		int          =depth
             _lowerCamelCase	:		Tuple          =0
             _lowerCamelCase	:		Any          =None
             _lowerCamelCase	:		Union[str, Any]          =None
             _lowerCamelCase	:		Optional[int]          =min_leaf_size
             _lowerCamelCase	:		int          =None






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Dict      ,       lowercase_						:					str			) ->						Union[str, Any]:
             """simple docstring"""




             if labels.ndim != 1:
                    print('Error: Input labels must be one dimensional'			)

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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Any			) ->						Dict:
             """simple docstring"""




             if x.ndim != 1:
                    print('Error: Input data set must be one dimensional'			)
                    return
             if len(lowercase_			) != len(lowercase_			):
                    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(lowercase_			) < 2 * self.min_leaf_size:
                    _lowerCamelCase	:		Union[str, Any]          =np.mean(lowercase_			)
                    return

             if self.depth == 1:
                    _lowerCamelCase	:		Union[str, Any]          =np.mean(lowercase_			)
                    return

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

             if best_split != 0:
                    _lowerCamelCase	:		int          =x[:best_split]
                    _lowerCamelCase	:		Any          =y[:best_split]
                    _lowerCamelCase	:		List[Any]          =x[best_split:]
                    _lowerCamelCase	:		Optional[int]          =y[best_split:]

                    _lowerCamelCase	:		str          =x[best_split]
                    _lowerCamelCase	:		int          =DecisionTree(
                        depth=self.depth - 1      ,       min_leaf_size=self.min_leaf_size			)
                    _lowerCamelCase	:		Optional[Any]          =DecisionTree(
                        depth=self.depth - 1      ,       min_leaf_size=self.min_leaf_size			)
                    self.left.train(lowercase_      ,       lowercase_			)
                    self.right.train(lowercase_      ,       lowercase_			)
             else:
                    _lowerCamelCase	:		Optional[int]          =np.mean(lowercase_			)

             return






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					int			) ->						List[Any]:
             """simple docstring"""




             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(lowercase_			)
                    else:
                           return self.left.predict(lowercase_			)
             else:
                    print('Error: Decision tree not yet trained'			)
                    return None




class A  :






      @staticmethod
      def 	lowerCamelCase	( lowercase_						:					List[Any]      ,       lowercase_						:					Optional[int]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =float(0			)
             for label in labels:
                    squared_error_sum += (label - prediction) ** 2

             return float(squared_error_sum / labels.size			)




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =np.arange(-1.0		,      1.0		,      0.0_05					)
       _lowerCamelCase	:		Tuple          =np.sin(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		int          =DecisionTree(depth=10		,      min_leaf_size=10					)
       tree.train(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

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

       print('Test values: ' + str(SCREAMING_SNAKE_CASE__					)					)
       print('Predictions: ' + str(SCREAMING_SNAKE_CASE__					)					)
       print('Average error: ' + str(SCREAMING_SNAKE_CASE__					)					)


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

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






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

import datasets


lowerCamelCase						 =	'\\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'

lowerCamelCase						 =	'\\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'


lowerCamelCase						 =	'\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					):






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """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 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """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:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

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

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


