




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class     a__ (      UpperCamelCase__   ):
     a	:    List[Any]	      =			c_float







     @property
     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          return sizeof(self.value				)







def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase = 0)    ->  int:
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
          a											=   fixed_dimension

     dimension -= num_token_to_add
     return dimension

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  int:
     return num_parameters * dtype.size





def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

     try:
          return AutoProcessor.from_pretrained(__UpperCamelCase)
     except (ValueError, OSError, KeyError):
          a						,   a											=   None, None
          try:
               a											=   AutoTokenizer.from_pretrained(__UpperCamelCase)
          except (OSError, KeyError):
               pass
          try:
               a											=   AutoFeatureExtractor.from_pretrained(__UpperCamelCase)
          except (OSError, KeyError):
               pass

          if tokenizer is not None and feature_extractor is not None:
               raise ValueError(
                   f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.''')
          elif tokenizer is None and feature_extractor is None:
               return None
          elif tokenizer is not None:
               return tokenizer
          else:
               return feature_extractor







from sklearn.metrics import mean_absolute_error

import datasets


lowercase__   :    int 					=      "\\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"

lowercase__   :    str 					=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


lowercase__   :    Tuple 					=      "\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     a__ (      datasets.Metric   ):







     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          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     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          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     lowerCAmelCase_       (			self						,	A						,	A						,	A=None						,	A="uniform_average"				)						->					List[Any]:



          '''simple docstring'''



          a											=   mean_absolute_error(A						,	A						,	sample_weight=A						,	multioutput=A				)

          return {"mae": mae_score}

