


'''simple docstring'''


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


_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'cpmant'


     def __init__(      self ,    lowerCamelCase = 3_0720 ,    lowerCamelCase = 4096 ,    lowerCamelCase = 32 ,    lowerCamelCase = 128 ,    lowerCamelCase = 1_0240 ,    lowerCamelCase = 48 ,    lowerCamelCase = 0.0 ,    lowerCamelCase = 512 ,    lowerCamelCase = 2048 ,    lowerCamelCase = 1E-6 ,    lowerCamelCase = 1.0 ,    lowerCamelCase = 32 ,    lowerCamelCase = 32 ,    lowerCamelCase = 32 ,    lowerCamelCase = True ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A  :					Dict						     =       prompt_types
          __A  :					Optional[Any]						     =       prompt_length
          __A  :					Union[str, Any]						     =       segment_types
          __A  :					Any						     =       hidden_size
          __A  :					Dict						     =       num_attention_heads
          __A  :					Optional[int]						     =       dim_head
          __A  :					Dict						     =       dim_ff
          __A  :					Tuple						     =       num_hidden_layers
          __A  :					Any						     =       position_bias_num_buckets
          __A  :					Optional[Any]						     =       position_bias_max_distance
          __A  :					Tuple						     =       dropout_p
          __A  :					Dict						     =       eps
          __A  :					Optional[Any]						     =       use_cache
          __A  :					List[Any]						     =       vocab_size
          __A  :					Optional[Any]						     =       init_std





'''simple docstring'''


from sklearn.metrics import accuracy_score

import datasets


_UpperCamelCase          =     """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


_UpperCamelCase          =     """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> 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])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


_UpperCamelCase          =     """
@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}
}
"""







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

     """simple docstring"""






     def lowerCAmelCase__     (      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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=True ,    lowerCamelCase=None		):


          '''simple docstring'''





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

