






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase_   =   """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


lowerCAmelCase_   =   """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


lowerCAmelCase_   =   """
@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}
}
"""

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



      def    __a (					self					)      ->   List[str]:



             '''simple docstring'''

             return datasets.MetricInfo(
                 description=_DESCRIPTION     ,  citation=_CITATION     ,  inputs_description=_KWARGS_DESCRIPTION     ,  features=datasets.Features(
                     {
                         '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
                         '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
                     }
                     if self.config_name == '''multilabel'''
                     else {
                         '''predictions''': datasets.Value('''int32'''					),
                         '''references''': datasets.Value('''int32'''					),
                     }					)     ,  reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']     ,  )



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_=True     ,  lowercase_=None					)      ->   Union[str, Any]:



             '''simple docstring'''

             return {
                 "accuracy": float(
                     accuracy_score(lowerCamelCase__     ,  lowerCamelCase__     ,  normalize=lowerCamelCase__     ,  sample_weight=lowerCamelCase__					)					)
             }













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       ,__UpperCAmelCase				):



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						load_tool('''translation'''					)
             self.tool.setup()
             _snake_case      :							Any     				=						load_tool('''translation'''     ,  remote=lowercase_					)



      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
             self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)



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



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.remote_tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
             self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Dict     				=						self.tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
             self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

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



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
             _snake_case      :							Dict     				=						self.tool(*lowercase_					)

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

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



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
             _snake_case      :							Dict     				=						self.tool(*lowercase_					)

             if not isinstance(lowercase_     ,  lowercase_					):
                    _snake_case      :							Optional[Any]     				=						[outputs]

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

             for output, output_type in zip(lowercase_     ,  self.tool.outputs					):
                    _snake_case      :							List[str]     				=						AGENT_TYPE_MAPPING[output_type]
                    self.assertTrue(isinstance(lowercase_     ,  lowercase_					)					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Dict     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']

             _snake_case      :							Any     				=						[]

             for _input, input_type in zip(lowercase_     ,  self.tool.inputs					):
                    if isinstance(lowercase_     ,  lowercase_					):
                           _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
             _snake_case      :							List[Any]     				=						self.tool(*lowercase_					)

             if not isinstance(lowercase_     ,  lowercase_					):
                    _snake_case      :							Any     				=						[outputs]

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






