





from sklearn.metrics import accuracy_score

import datasets


_lowerCamelCase							=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


_lowerCamelCase							=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> 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])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


_lowerCamelCase							=     '\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'




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


							def        _snake_case					(							self      :str						)				->				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        _snake_case					(							self      :Optional[int]							,			__A      :Tuple							,			__A      :int							,			__A      :str=True							,			__A      :int=None						)				->				Optional[int]:



									"""simple docstring"""



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






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


_lowerCamelCase							=     logging.get_logger(__name__)

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




class 					UpperCamelCase_    (       UpperCamelCase__      ):
							lowerCamelCase_          =   "videomae"


							def __init__(							self      :Dict							,			__A      :List[str]=224							,			__A      :List[Any]=16							,			__A      :int=3							,			__A      :Optional[Any]=16							,			__A      :Optional[int]=2							,			__A      :Optional[Any]=768							,			__A      :List[str]=12							,			__A      :Optional[Any]=12							,			__A      :List[str]=3072							,			__A      :Tuple="gelu"							,			__A      :Optional[int]=0.0							,			__A      :Union[str, Any]=0.0							,			__A      :int=0.0_2							,			__A      :Union[str, Any]=1E-12							,			__A      :Optional[Any]=True							,			__A      :Union[str, Any]=True							,			__A      :Optional[int]=6							,			__A      :Tuple=384							,			__A      :str=4							,			__A      :List[str]=1536							,			__A      :Dict=True							,			**__A      :int							,			)				->				Optional[int]:



									"""simple docstring"""



									super().__init__(**__A						)

									SCREAMING_SNAKE_CASE__								= image_size
									SCREAMING_SNAKE_CASE__								= patch_size
									SCREAMING_SNAKE_CASE__								= num_channels
									SCREAMING_SNAKE_CASE__								= num_frames
									SCREAMING_SNAKE_CASE__								= tubelet_size

									SCREAMING_SNAKE_CASE__								= hidden_size
									SCREAMING_SNAKE_CASE__								= num_hidden_layers
									SCREAMING_SNAKE_CASE__								= num_attention_heads
									SCREAMING_SNAKE_CASE__								= intermediate_size
									SCREAMING_SNAKE_CASE__								= hidden_act
									SCREAMING_SNAKE_CASE__								= hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								= initializer_range
									SCREAMING_SNAKE_CASE__								= layer_norm_eps
									SCREAMING_SNAKE_CASE__								= qkv_bias
									SCREAMING_SNAKE_CASE__								= use_mean_pooling

									SCREAMING_SNAKE_CASE__								= decoder_num_attention_heads
									SCREAMING_SNAKE_CASE__								= decoder_hidden_size
									SCREAMING_SNAKE_CASE__								= decoder_num_hidden_layers
									SCREAMING_SNAKE_CASE__								= decoder_intermediate_size
									SCREAMING_SNAKE_CASE__								= norm_pix_loss