


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}






'''simple docstring'''







import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


A__:   List[Any]    =	logging.get_logger(__name__)

A__:   List[Any]    =	{
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Dict     	=							"vit-hybrid"






      def __init__(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Any=7_6_8					,						SCREAMING_SNAKE_CASE      :Optional[Any]=1_2					,						SCREAMING_SNAKE_CASE      :int=1_2					,						SCREAMING_SNAKE_CASE      :Any=3_0_7_2					,						SCREAMING_SNAKE_CASE      :List[str]="gelu"					,						SCREAMING_SNAKE_CASE      :str=0.0					,						SCREAMING_SNAKE_CASE      :Dict=0.0					,						SCREAMING_SNAKE_CASE      :Dict=0.02					,						SCREAMING_SNAKE_CASE      :Tuple=1e-12					,						SCREAMING_SNAKE_CASE      :List[Any]=2_2_4					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=1					,						SCREAMING_SNAKE_CASE      :List[Any]=3					,						SCREAMING_SNAKE_CASE      :Optional[Any]=[1, 1_0_2_4, 2_4, 2_4]					,						SCREAMING_SNAKE_CASE      :Optional[Any]=True					,						**SCREAMING_SNAKE_CASE      :Dict					,						)	-> List[Any]:




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE     )

             if backbone_config is None:
                    logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""     )
                    _a   :			Optional[int]					 ={
                        """global_padding""": """same""",
                        """layer_type""": """bottleneck""",
                        """depths""": [3, 4, 9],
                        """out_features""": ["""stage3"""],
                        """embedding_dynamic_padding""": True,
                    }

             if isinstance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
                    if "model_type" in backbone_config:
                           _a   :			Dict					 =CONFIG_MAPPING[backbone_config["""model_type"""]]
                    else:
                           logger.info(
                               """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""     )
                           _a   :			List[str]					 =BitConfig
                    _a   :			List[Any]					 =backbone_config_class(**SCREAMING_SNAKE_CASE     )

             _a   :			str					 =backbone_featmap_shape
             _a   :			Dict					 =backbone_config
             _a   :			Tuple					 =hidden_size
             _a   :			Union[str, Any]					 =num_hidden_layers
             _a   :			Tuple					 =num_attention_heads
             _a   :			int					 =intermediate_size
             _a   :			Any					 =hidden_act
             _a   :			Union[str, Any]					 =hidden_dropout_prob
             _a   :			Any					 =attention_probs_dropout_prob
             _a   :			Tuple					 =initializer_range
             _a   :			int					 =layer_norm_eps
             _a   :			Union[str, Any]					 =image_size
             _a   :			str					 =patch_size
             _a   :			Optional[int]					 =num_channels
             _a   :			Union[str, Any]					 =qkv_bias






      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Dict[str, any]:




             '''simple docstring'''
             _a   :			Dict					 =copy.deepcopy(self.__dict__     )
             _a   :			Tuple					 =self.backbone_config.to_dict()
             _a   :			str					 =self.__class__.model_type
             return output


