code: 
  
  
  '''simple docstring'''
  
  import copy
  import inspect
  
  from transformers.testing_utils import require_torch, torch_device
  from transformers.utils.backbone_utils import BackboneType
  @require_torch
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Tuple   					=       ()
  		A_			:    Union[str, Any]   					=       True
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any    )						->				int:
  			__magic_name__ :					str  			=  self.config_class
  
  			# test default config
  			__magic_name__ :					Optional[Any]  			=  config_class()
  			self.assertIsNotNone(_A    )
  			__magic_name__ :					Union[str, Any]  			=  ['stem'] + [F'stage{idx}' for idx in range(1						,  len(config.depths    ) + 1    )]
  			self.assertEqual(config.stage_names						,  _A    )
  			self.assertTrue(set(config.out_features    ).issubset(set(config.stage_names    )    )    )
  
  			# Test out_features and out_indices are correctly set
  			# out_features and out_indices both None
  			__magic_name__ :					Optional[int]  			=  config_class(out_features=_A						,  out_indices=_A    )
  			self.assertEqual(config.out_features						,  [config.stage_names[-1]]    )
  			self.assertEqual(config.out_indices						,  [len(config.stage_names    ) - 1]    )
  
  			# out_features and out_indices both set
  			__magic_name__ :					List[Any]  			=  config_class(out_features=['stem', 'stage1']						,  out_indices=[0, 1]    )
  			self.assertEqual(config.out_features						,  ['stem', 'stage1']    )
  			self.assertEqual(config.out_indices						,  [0, 1]    )
  
  			# Only out_features set
  			__magic_name__ :					str  			=  config_class(out_features=['stage1', 'stage3']    )
  			self.assertEqual(config.out_features						,  ['stage1', 'stage3']    )
  			self.assertEqual(config.out_indices						,  [1, 3]    )
  
  			# Only out_indices set
  			__magic_name__ :					Dict  			=  config_class(out_indices=[0, 2]    )
  			self.assertEqual(config.out_features						,  [config.stage_names[0], config.stage_names[2]]    )
  			self.assertEqual(config.out_indices						,  [0, 2]    )
  
  			# Error raised when out_indices do not correspond to out_features
  			with self.assertRaises(_A    ):
  				__magic_name__ :					Dict  			=  config_class(out_features=['stage1', 'stage2']						,  out_indices=[0, 2]    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				int:
  			__magic_name__			,       __magic_name__ :					str  			=  self.model_tester.prepare_config_and_inputs_for_common()
  
  			for model_class in self.all_model_classes:
  				__magic_name__ :					Optional[Any]  			=  model_class(_A    )
  				__magic_name__ :					List[Any]  			=  inspect.signature(model.forward    )
  				# signature.parameters is an OrderedDict => so arg_names order is deterministic
  				__magic_name__ :					Optional[Any]  			=  [*signature.parameters.keys()]
  				__magic_name__ :					List[Any]  			=  ['pixel_values']
  				self.assertListEqual(arg_names[:1]						,  _A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int    )						->				str:
  			__magic_name__			,       __magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs_for_common()
  
  			for model_class in self.all_model_classes:
  				__magic_name__ :					Dict  			=  model_class(_A    )
  				self.assertEqual(len(model.channels    )						,  len(config.out_features    )    )
  				__magic_name__ :					Dict  			=  model.num_features
  				__magic_name__ :					List[str]  			=  [config.stage_names.index(_A    ) for feat in config.out_features]
  				__magic_name__ :					Tuple  			=  [num_features[idx] for idx in out_indices]
  				self.assertListEqual(model.channels						,  _A    )
  
  				__magic_name__ :					str  			=  copy.deepcopy(_A    )
  				__magic_name__ :					List[Any]  			=  None
  				__magic_name__ :					Optional[Any]  			=  model_class(_A    )
  				self.assertEqual(len(model.channels    )						,  1    )
  				self.assertListEqual(model.channels						,  [num_features[-1]]    )
  
  				__magic_name__ :					List[Any]  			=  copy.deepcopy(_A    )
  				__magic_name__ :					Union[str, Any]  			=  None
  				__magic_name__ :					Optional[int]  			=  model_class(_A    )
  				self.assertEqual(len(model.channels    )						,  1    )
  				self.assertListEqual(model.channels						,  [num_features[-1]]    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Dict:
  			__magic_name__			,       __magic_name__ :					Union[str, Any]  			=  self.model_tester.prepare_config_and_inputs_for_common()
  
  			for model_class in self.all_model_classes:
  				__magic_name__ :					Tuple  			=  model_class(_A    )
  				model.to(_A    )
  				model.eval()
  				__magic_name__ :					Dict  			=  model(**_A    )
  
  				self.assertEqual(len(result.feature_maps    )						,  len(config.out_features    )    )
  				self.assertEqual(len(model.channels    )						,  len(config.out_features    )    )
  				self.assertEqual(len(result.feature_maps    )						,  len(config.out_indices    )    )
  				self.assertEqual(len(model.channels    )						,  len(config.out_indices    )    )
  
  				# Check output of last stage is taken if out_features=None, out_indices=None
  				__magic_name__ :					Tuple  			=  copy.deepcopy(_A    )
  				__magic_name__ :					Any  			=  None
  				__magic_name__ :					Any  			=  model_class(_A    )
  				model.to(_A    )
  				model.eval()
  				__magic_name__ :					Optional[Any]  			=  model(**_A    )
  
  				self.assertEqual(len(result.feature_maps    )						,  1    )
  				self.assertEqual(len(model.channels    )						,  1    )
  
  				__magic_name__ :					Optional[Any]  			=  copy.deepcopy(_A    )
  				__magic_name__ :					Dict  			=  None
  				__magic_name__ :					Tuple  			=  model_class(_A    )
  				model.to(_A    )
  				model.eval()
  				__magic_name__ :					Union[str, Any]  			=  model(**_A    )
  
  				self.assertEqual(len(result.feature_maps    )						,  1    )
  				self.assertEqual(len(model.channels    )						,  1    )
  
  				# Check backbone can be initialized with fresh weights
  				__magic_name__ :					Any  			=  copy.deepcopy(_A    )
  				__magic_name__ :					Optional[Any]  			=  False
  				__magic_name__ :					Optional[Any]  			=  model_class(_A    )
  				model.to(_A    )
  				model.eval()
  				__magic_name__ :					Tuple  			=  model(**_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int    )						->				List[str]:
  			__magic_name__			,       __magic_name__ :					str  			=  self.model_tester.prepare_config_and_inputs_for_common()
  
  			for backbone_class in self.all_model_classes:
  				__magic_name__ :					Tuple  			=  backbone_class(_A    )
  
  				self.assertTrue(hasattr(_A						,  'backbone_type'    )    )
  				self.assertTrue(hasattr(_A						,  'stage_names'    )    )
  				self.assertTrue(hasattr(_A						,  'num_features'    )    )
  				self.assertTrue(hasattr(_A						,  'out_indices'    )    )
  				self.assertTrue(hasattr(_A						,  'out_features'    )    )
  				self.assertTrue(hasattr(_A						,  'out_feature_channels'    )    )
  				self.assertTrue(hasattr(_A						,  'channels'    )    )
  
  				self.assertIsInstance(backbone.backbone_type						,  _A    )
  				# Verify num_features has been initialized in the backbone init
  				self.assertIsNotNone(backbone.num_features    )
  				self.assertTrue(len(backbone.channels    ) == len(backbone.out_indices    )    )
  				self.assertTrue(len(backbone.stage_names    ) == len(backbone.num_features    )    )
  				self.assertTrue(len(backbone.channels    ) <= len(backbone.num_features    )    )
  				self.assertTrue(len(backbone.out_feature_channels    ) == len(backbone.stage_names    )    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int    )						->				Tuple:
  			__magic_name__			,       __magic_name__ :					int  			=  self.model_tester.prepare_config_and_inputs_for_common()
  			__magic_name__ :					Optional[Any]  			=  inputs_dict['pixel_values'].shape[0]
  
  			for backbone_class in self.all_model_classes:
  				__magic_name__ :					Optional[int]  			=  backbone_class(_A    )
  				backbone.to(_A    )
  				backbone.eval()
  
  				__magic_name__ :					int  			=  backbone(**_A    )
  
  				# Test default outputs and verify feature maps
  				self.assertIsInstance(outputs.feature_maps						,  _A    )
  				self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
  				for feature_map, n_channels in zip(outputs.feature_maps						,  backbone.channels    ):
  					self.assertTrue(feature_map.shape[:2]						,  (batch_size, n_channels)    )
  				self.assertIsNone(outputs.hidden_states    )
  				self.assertIsNone(outputs.attentions    )
  
  				# Test output_hidden_states=True
  				__magic_name__ :					Any  			=  backbone(**_A						,  output_hidden_states=_A    )
  				self.assertIsNotNone(outputs.hidden_states    )
  				self.assertTrue(len(outputs.hidden_states    )						,  len(backbone.stage_names    )    )
  				for hidden_state, n_channels in zip(outputs.hidden_states						,  backbone.channels    ):
  					self.assertTrue(hidden_state.shape[:2]						,  (batch_size, n_channels)    )
  
  				# Test output_attentions=True
  				if self.has_attentions:
  					__magic_name__ :					Any  			=  backbone(**_A						,  output_attentions=_A    )
  					self.assertIsNotNone(outputs.attentions    )
style_context: 
  
  
  '''simple docstring'''
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  lowerCAmelCase   :Union[str, Any]								=    '''\
  @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}
  }
  '''
  
  lowerCAmelCase   :int								=    '''\
  Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
  values.
  '''
  
  
  lowerCAmelCase   :Optional[int]								=    '''
  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 _lowerCamelCase (		datasets.Metric							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				int:
  			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    )						->				List[str]:
  			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	:		Dict						,  _A	:		Optional[int]						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]=None						,  _A	:		Tuple="uniform_average"    )						->				Any:
  			__magic_name__ :					Optional[Any]  			=  mean_absolute_error(_A						,  _A						,  sample_weight=_A						,  multioutput=_A    )
  
  			return {"mae": mae_score}