


"""simple docstring"""





from sklearn.metrics import mean_absolute_error

import datasets


__UpperCAmelCase    				=      "\\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    				=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


__UpperCAmelCase    				=      "\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  lowercase_  ( datasets.Metric						):







      def 				_lowerCAmelCase       (      self :							str    ):
        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 :							int    ):
        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 :							Any		,  _lowercase :							List[str]		,  _lowercase :							Any		,  _lowercase :							Union[str, Any]=None		,  _lowercase :							Any="uniform_average"    ):
        lowerCAmelCase__    :    Union[str, Any]								=					mean_absolute_error(_lowercase		,  _lowercase		,  sample_weight=_lowercase		,  multioutput=_lowercase    )

        return {"mae": mae_score}




"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase    				=      logging.get_logger(__name__)

__UpperCAmelCase    				=      {
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}

class  lowercase_  ( a_						):
      __magic_name__     :							Union[str, Any]          =				"""videomae"""







      def __init__(      self :							Dict		,  _lowercase :							int=2_2_4		,  _lowercase :							Optional[Any]=1_6		,  _lowercase :							List[Any]=3		,  _lowercase :							int=1_6		,  _lowercase :							Optional[Any]=2		,  _lowercase :							Union[str, Any]=7_6_8		,  _lowercase :							int=1_2		,  _lowercase :							Dict=1_2		,  _lowercase :							Optional[Any]=3_0_7_2		,  _lowercase :							List[str]="gelu"		,  _lowercase :							int=0.0		,  _lowercase :							int=0.0		,  _lowercase :							Optional[int]=0.02		,  _lowercase :							List[str]=1e-1_2		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Any=6		,  _lowercase :							Any=3_8_4		,  _lowercase :							int=4		,  _lowercase :							Optional[int]=1_5_3_6		,  _lowercase :							Tuple=True		,  **_lowercase :							List[str]		,  ):
        super().__init__(**_lowercase    )

        lowerCAmelCase__    :    List[Any]								=					image_size
        lowerCAmelCase__    :    Tuple								=					patch_size
        lowerCAmelCase__    :    Union[str, Any]								=					num_channels
        lowerCAmelCase__    :    Optional[Any]								=					num_frames
        lowerCAmelCase__    :    Union[str, Any]								=					tubelet_size

        lowerCAmelCase__    :    Union[str, Any]								=					hidden_size
        lowerCAmelCase__    :    Dict								=					num_hidden_layers
        lowerCAmelCase__    :    Any								=					num_attention_heads
        lowerCAmelCase__    :    List[Any]								=					intermediate_size
        lowerCAmelCase__    :    Optional[Any]								=					hidden_act
        lowerCAmelCase__    :    int								=					hidden_dropout_prob
        lowerCAmelCase__    :    Optional[Any]								=					attention_probs_dropout_prob
        lowerCAmelCase__    :    Any								=					initializer_range
        lowerCAmelCase__    :    Any								=					layer_norm_eps
        lowerCAmelCase__    :    Union[str, Any]								=					qkv_bias
        lowerCAmelCase__    :    int								=					use_mean_pooling

        lowerCAmelCase__    :    List[Any]								=					decoder_num_attention_heads
        lowerCAmelCase__    :    List[str]								=					decoder_hidden_size
        lowerCAmelCase__    :    int								=					decoder_num_hidden_layers
        lowerCAmelCase__    :    Optional[int]								=					decoder_intermediate_size
        lowerCAmelCase__    :    Optional[Any]								=					norm_pix_loss
