

"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if isinstance(lowercase__     ,				lowercase__ ):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(lowercase__     ,				lowercase__ ):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										A     =   hash_data.replace("\\"     ,				"/" )
					elif isinstance(lowercase__     ,				lowercase__ ):
										A     =   str(lowercase__ )
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(lowercase__ )

					return hash_data.encode("utf-8" )





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						int):
							A     =   "\nFAILURE TO GENERATE DATASET: Invalid key type detected"
							A     =   F"""\nFound Key {hash_data} of type {type(__SCREAMING_SNAKE_CASE)}"""
							A     =   "\nKeys should be either str, int or bytes type"
							super().__init__(F"""{self.prefix}{self.err_msg}{self.suffix}""")





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						int=""):
							A     =   key
							A     =   duplicate_key_indices
							A     =   fix_msg
							A     =   "Found multiple examples generated with the same key"
							if len(__SCREAMING_SNAKE_CASE) <= 2_0:
												A     =   F"""\nThe examples at index {", ".join(__SCREAMING_SNAKE_CASE)} have the key {key}"""
							else:
												A     =   F"""\nThe examples at index {", ".join(duplicate_key_indices[:2_0])}... ({len(__SCREAMING_SNAKE_CASE) - 2_0} more) have the key {key}"""
							A     =   "\n" + fix_msg if fix_msg else ""
							super().__init__(F"""{self.prefix}{self.err_msg}{self.suffix}""")





class      __UpperCamelCase					:


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						str):
							A     =   hashlib.mda(_as_bytes(__SCREAMING_SNAKE_CASE))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Union[str, int, bytes]):
							A     =   self._split_mda.copy()
							A     =   _as_bytes(__SCREAMING_SNAKE_CASE)
							mda.update(__SCREAMING_SNAKE_CASE)
							# Convert to integer with hexadecimal conversion
							return int(mda.hexdigest()	,	1_6)









from sklearn.metrics import mean_absolute_error

import datasets


a					:				Optional[Any]       =      '\\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'

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


a					:				Any       =      '\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   lowerCamelCase						(	datasets.Metric  ):

       def 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=None						,    a__="uniform_average"       ):






        '''simple docstring'''






        snake_case :			str							=   mean_absolute_error(a__						,    a__						,    sample_weight=a__						,    multioutput=a__       )

        return {"mae": mae_score}






