






"""simple docstring"""




from sklearn.metrics import mean_absolute_error

import datasets


_snake_case					   =     '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

_snake_case					   =     '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


_snake_case					   =     '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION   )
class  UpperCamelCase    (			datasets.Metric   ):


 def _lowercase			(  self	:		int							)      -> List[Any]:
   return datasets.MetricInfo(
       description=_DESCRIPTION							,		citation=_CITATION							,		inputs_description=_KWARGS_DESCRIPTION							,		features=datasets.Features(self._get_feature_types()							)							,		reference_urls=[
           """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
       ]							,		)


 def _lowercase			(  self	:		List[Any]							)      -> int:
   if self.config_name == "multilist":
     return {
         "predictions": datasets.Sequence(datasets.Value("""float"""							)							),
         "references": datasets.Sequence(datasets.Value("""float"""							)							),
     }
   else:
     return {
         "predictions": datasets.Value("""float"""							),
         "references": datasets.Value("""float"""							),
     }







 def _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		Union[str, Any]							,		UpperCAmelCase__	:		Optional[Any]=None							,		UpperCAmelCase__	:		Union[str, Any]="uniform_average"							)      -> Tuple:
   _a						: Any    =			mean_absolute_error(UpperCAmelCase__							,		UpperCAmelCase__							,		sample_weight=UpperCAmelCase__							,		multioutput=UpperCAmelCase__							)

   return {"mae": mae_score}









"""simple docstring"""




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

