


from sklearn.metrics import accuracy_score

import datasets


a					:				List[str]       =      '\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'


a					:				Union[str, Any]       =      '\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'


a					:				Dict       =      '\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   lowerCamelCase						(	datasets.Metric  ):

       def 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=True						,    a__=None       ):






        '''simple docstring'''






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










import copy

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


a					:				Optional[Any]       =      logging.get_logger(__name__)

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hybrid-clip"""
       _UpperCamelCase     		=   True

       def __init__(      self						,    a__=5_12						,    **a__       ):






        '''simple docstring'''






        super().__init__(**a__       )

        if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''       )

        if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''       )

        snake_case :			int							=   kwargs.pop('''text_config'''       )
        snake_case :			Optional[int]							=   kwargs.pop('''vision_config'''       )

        snake_case :			Optional[Any]							=   text_config.pop('''model_type'''       )
        snake_case :			str							=   vision_config.pop('''model_type'''       )

        from transformers import AutoConfig

        snake_case :			List[Any]							=   AutoConfig.for_model(a__						,    **a__       )

        if vision_model_type == "clip":
         snake_case :			Dict							=   AutoConfig.for_model(a__						,    **a__       ).vision_config
        elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         snake_case :			str							=   CLIPVisionConfig(**a__       )
        else:
         snake_case :			Any							=   AutoConfig.for_model(a__						,    **a__       )

        snake_case :			Optional[Any]							=   projection_dim
        snake_case :			int							=   1.0

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    **a__       ):






        '''simple docstring'''






        return cls(text_config=text_config.to_dict()						,    vision_config=vision_config.to_dict()						,    **a__       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   copy.deepcopy(self.__dict__       )
        snake_case :			Dict							=   self.text_config.to_dict()
        snake_case :			int							=   self.vision_config.to_dict()
        snake_case :			List[Any]							=   self.__class__.model_type
        return output






