

import unittest

from diffusers import __version__
from diffusers.utils import deprecate



class  __snake_case					(      unittest.TestCase    ):
  __lowerCAmelCase       :		Tuple  	=					'.'.join([str(int(__version__.split('.'    )[0]    ) + 1    )] + __version__.split('.'    )[1:]    )
  __lowerCAmelCase       :		Optional[int]  	=					'0.0.1'

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  {'deprecated_arg': 4}

      with self.assertWarns(_A) as warning:
          SCREAMING_SNAKE_CASE_													=  deprecate('deprecated_arg'  ,						self.higher_version  ,						'message'  ,						take_from=_A)

      assert output == 4
      assert (
          str(warning.warning)
          == f"""The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."""
          " message"
      )

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  {'deprecated_arg': 4}

      with self.assertWarns(_A) as warning:
          SCREAMING_SNAKE_CASE_													=  deprecate(('deprecated_arg', self.higher_version, 'message')  ,						take_from=_A)

      assert output == 4
      assert (
          str(warning.warning)
          == f"""The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."""
          " message"
      )

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  {'deprecated_arg_1': 4, 'deprecated_arg_2': 8}
      with self.assertWarns(_A) as warning:
          SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  deprecate(
              ('deprecated_arg_1', self.higher_version, 'Hey')  ,						('deprecated_arg_2', self.higher_version, 'Hey')  ,						take_from=_A  ,						)
      assert output_a == 4
      assert output_a == 8
      assert (
          str(warning.warnings[0].message)
          == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
          f""" {self.higher_version}. Hey"""
      )
      assert (
          str(warning.warnings[1].message)
          == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
          f""" {self.higher_version}. Hey"""
      )

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  {'deprecated_arg': 4}

      with self.assertRaises(_A) as error:
          deprecate(('wrong_arg', self.higher_version, 'message')  ,						take_from=_A)

      assert "test_deprecate_function_incorrect_arg in" in str(error.exception)
      assert "line" in str(error.exception)
      assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception)

  def   lowerCAmelCase__       (   self):
      with self.assertWarns(_A) as warning:
          deprecate(('deprecated_arg', self.higher_version, 'message'))

      assert (
          str(warning.warning)
          == f"""`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message"""
      )

  def   lowerCAmelCase__       (   self):
      with self.assertWarns(_A) as warning:
          deprecate(
              ('deprecated_arg_1', self.higher_version, 'Hey')  ,						('deprecated_arg_2', self.higher_version, 'Hey')  ,						)
      assert (
          str(warning.warnings[0].message)
          == f"""`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey"""
      )
      assert (
          str(warning.warnings[1].message)
          == f"""`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey"""
      )

  def   lowerCAmelCase__       (   self):



      class  __snake_case					:
        __lowerCAmelCase       :		Tuple  	=					5

      with self.assertWarns(_A) as warning:
          SCREAMING_SNAKE_CASE_													=  deprecate(('arg', self.higher_version, 'message')  ,						take_from=Args())

      assert arg == 5
      assert (
          str(warning.warning)
          == f"""The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"""
      )

  def   lowerCAmelCase__       (   self):



      class  __snake_case					:
        __lowerCAmelCase       :		Tuple  	=					5
        __lowerCAmelCase       :		Union[str, Any]  	=					7

      with self.assertWarns(_A) as warning:
          SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  deprecate(
              ('arg', self.higher_version, 'message')  ,						('foo', self.higher_version, 'message')  ,						('does not exist', self.higher_version, 'message')  ,						take_from=Args()  ,						)

      assert arg_a == 5
      assert arg_a == 7
      assert (
          str(warning.warning)
          == f"""The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"""
      )
      assert (
          str(warning.warnings[0].message)
          == f"""The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"""
      )
      assert (
          str(warning.warnings[1].message)
          == f"""The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message"""
      )

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  {'deprecated_arg': 4}

      with self.assertRaises(_A) as error:
          deprecate(('wrong_arg', self.lower_version, 'message')  ,						take_from=_A)

      assert (
          str(error.exception)
          == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
          f""" {__version__} is >= {self.lower_version}"""
      )

  def   lowerCAmelCase__       (   self):
      with self.assertWarns(_A) as warning:
          deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')  ,						standard_warn=_A)

      assert str(warning.warning) == "This message is better!!!"


  def   lowerCAmelCase__       (   self):
      with self.assertWarns(_A) as warning:
          deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')  ,						standard_warn=_A)
      assert str(warning.warning) == "This message is better!!!"
      assert "diffusers/tests/others/test_utils.py" in warning.filename





from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase__					: Union[str, 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"

UpperCamelCase__					: Optional[Any]								=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


UpperCamelCase__					: 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  __snake_case					(      datasets.Metric    ):

  def   lowerCAmelCase__       (   self):
      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):
      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"):
      SCREAMING_SNAKE_CASE_													=  mean_absolute_error(_A  ,						_A  ,						sample_weight=_A  ,						multioutput=_A)

      return {"mae": mae_score}


