


'''simple docstring'''







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types







class A__	(						unittest.TestCase			,					lowerCAmelCase__				):






      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Any:




             '''simple docstring'''
             _a   :			str					 =load_tool("""translation"""     )
             self.tool.setup()
             _a   :			List[Any]					 =load_tool("""translation"""					,						remote=_A     )






      def __UpperCAmelCase	(			self      :List[Any]     )	-> Dict:




             '''simple docstring'''
             _a   :			Any					 =self.tool("""Hey, what\'s up?"""					,						src_lang="""English"""					,						tgt_lang="""French"""     )
             self.assertEqual(_A					,						"""- Hé, comment ça va?"""     )






      def __UpperCAmelCase	(			self      :List[str]     )	-> Optional[int]:




             '''simple docstring'''
             _a   :			List[str]					 =self.remote_tool("""Hey, what\'s up?"""					,						src_lang="""English"""					,						tgt_lang="""French"""     )
             self.assertEqual(_A					,						"""- Hé, comment ça va?"""     )






      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> int:




             '''simple docstring'''
             _a   :			int					 =self.tool(text="""Hey, what\'s up?"""					,						src_lang="""English"""					,						tgt_lang="""French"""     )
             self.assertEqual(_A					,						"""- Hé, comment ça va?"""     )






      def __UpperCAmelCase	(			self      :int     )	-> List[str]:




             '''simple docstring'''
             _a   :			List[Any]					 =self.remote_tool(text="""Hey, what\'s up?"""					,						src_lang="""English"""					,						tgt_lang="""French"""     )
             self.assertEqual(_A					,						"""- Hé, comment ça va?"""     )






      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             _a   :			Optional[int]					 =['''Hey, what\'s up?''', '''English''', '''Spanish''']
             _a   :			List[Any]					 =self.tool(*_A     )

             # There is a single output
             if len(self.tool.outputs     ) == 1:
                    _a   :			Any					 =[outputs]

             self.assertListEqual(output_types(_A     )					,						self.tool.outputs     )






      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             _a   :			int					 =['''Hey, what\'s up?''', '''English''', '''Spanish''']
             _a   :			List[str]					 =self.tool(*_A     )

             if not isinstance(_A					,						_A     ):
                    _a   :			Dict					 =[outputs]

             self.assertEqual(len(_A     )					,						len(self.tool.outputs     )     )

             for output, output_type in zip(_A					,						self.tool.outputs     ):
                    _a   :			List[Any]					 =AGENT_TYPE_MAPPING[output_type]
                    self.assertTrue(isinstance(_A					,						_A     )     )






      def __UpperCAmelCase	(			self      :List[str]     )	-> Tuple:




             '''simple docstring'''
             _a   :			Dict					 =['''Hey, what\'s up?''', '''English''', '''Spanish''']

             _a   :			Optional[Any]					 =[]

             for _input, input_type in zip(_A					,						self.tool.inputs     ):
                    if isinstance(_A					,						_A     ):
                           _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input     ) for _input_type in input_type]     )
                    else:
                           _inputs.append(AGENT_TYPE_MAPPING[input_type](_input     )     )

        # Should not raise an error
             _a   :			Union[str, Any]					 =self.tool(*_A     )

             if not isinstance(_A					,						_A     ):
                    _a   :			Dict					 =[outputs]

             self.assertEqual(len(_A     )					,						len(self.tool.outputs     )     )









'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


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

A__:   Union[str, Any]    =	'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


A__:   str    =	'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        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.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''







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






      def __UpperCAmelCase	(			self      :List[Any]     )	-> 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 __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''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      :Any					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=None					,						SCREAMING_SNAKE_CASE      :Any="uniform_average"     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =mean_absolute_error(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						sample_weight=SCREAMING_SNAKE_CASE					,						multioutput=SCREAMING_SNAKE_CASE     )

             return {"mae": mae_score}


