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  __lowerCAmelCase	(							datasets.Metric    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Any	)			->				Union[str, Any]:
        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							:		int							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		Any=None	)			->				Union[str, Any]:
        return {
            "accuracy": float(
                accuracy_score(lowerCamelCase							,      lowerCamelCase							,      normalize=lowerCamelCase							,      sample_weight=lowerCamelCase	)	)
        }











"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__       :			Union[str, Any]=						logging.get_logger(__name__)

A__       :			Dict=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""rembert"""

   def __init__(       self			,					snake_case_=25_0300			,					snake_case_=1152			,					snake_case_=32			,					snake_case_=18			,					snake_case_=256			,					snake_case_=1664			,					snake_case_=4608			,					snake_case_="gelu"			,					snake_case_=0.0			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=True			,					snake_case_=0			,					snake_case_=312			,					snake_case_=313			,					**snake_case_			,					)		->							str:
       super().__init__(pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     input_embedding_size
       UpperCamelCase__								=     output_embedding_size
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     classifier_dropout_prob
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     type_vocab_size
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     use_cache
       UpperCamelCase__								=     False







class  __lowerCamelCase     (	_a	):

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase__								=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           UpperCamelCase__								=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]   )







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							float:
       return 1E-4




