




from sklearn.metrics import matthews_corrcoef

import datasets


lowerCamelCase__ :   List[Any]					=							'\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'

lowerCamelCase__ :   Union[str, Any]					=							'\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n'

lowerCamelCase__ :   str					=							'\\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					):


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		str						):
       return datasets.MetricInfo(
           description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(
               {
                   'predictions': datasets.Value('int32'						),
                   'references': datasets.Value('int32'						),
               }						)				,				reference_urls=[
               'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
           ]				,				)




 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		List[str]=None						):
       return {
           "matthews_correlation": float(matthews_corrcoef(snake_case__				,				snake_case__				,				sample_weight=snake_case__						)						),
       }

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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "megatron-bert"



   def __init__(self		:					Optional[int]    ,						snake_case__		:					List[str]=2_90_56    ,						snake_case__		:					List[Any]=10_24    ,						snake_case__		:					str=24    ,						snake_case__		:					Tuple=16    ,						snake_case__		:					Union[str, Any]=40_96    ,						snake_case__		:					str="gelu"    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Optional[int]=0.1    ,						snake_case__		:					Tuple=5_12    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					Dict=0.02    ,						snake_case__		:					List[Any]=1e-12    ,						snake_case__		:					int=0    ,						snake_case__		:					Tuple="absolute"    ,						snake_case__		:					Any=True    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Optional[Any]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						**snake_case__ )

          snake_case       :   Tuple							=      vocab_size
          snake_case       :   str							=      hidden_size
          snake_case       :   str							=      num_hidden_layers
          snake_case       :   str							=      num_attention_heads
          snake_case       :   Optional[int]							=      hidden_act
          snake_case       :   int							=      intermediate_size
          snake_case       :   List[str]							=      hidden_dropout_prob
          snake_case       :   Union[str, Any]							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   List[str]							=      type_vocab_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   Tuple							=      layer_norm_eps
          snake_case       :   int							=      position_embedding_type
          snake_case       :   str							=      use_cache





