


from sklearn.metrics import matthews_corrcoef

import datasets


__lowerCAmelCase			=							'''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

__lowerCAmelCase			=							'''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

__lowerCAmelCase			=							'''\
@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   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None			) ->    Optional[int]:




        '''simple docstring'''
        return {
            "matthews_correlation": float(matthews_corrcoef(lowerCAmelCase__    ,			lowerCAmelCase__    ,			sample_weight=lowerCAmelCase__			)			),
        }





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


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Any       					=			'pegasus'
 __lowercase			:							Union[str, Any]       					=			['past_key_values']
 __lowercase			:							Any       					=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(       self    ,			lowerCAmelCase__=50_265    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			lowerCAmelCase__=1    ,			**lowerCAmelCase__    ,			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       vocab_size
        lowercase__:       Optional[int]        =       max_position_embeddings
        lowercase__:       List[str]        =       d_model
        lowercase__:       Optional[Any]        =       encoder_ffn_dim
        lowercase__:       Optional[Any]        =       encoder_layers
        lowercase__:       Union[str, Any]        =       encoder_attention_heads
        lowercase__:       Optional[int]        =       decoder_ffn_dim
        lowercase__:       Tuple        =       decoder_layers
        lowercase__:       Union[str, Any]        =       decoder_attention_heads
        lowercase__:       Dict        =       dropout
        lowercase__:       List[str]        =       attention_dropout
        lowercase__:       List[str]        =       activation_dropout
        lowercase__:       Optional[int]        =       activation_function
        lowercase__:       Dict        =       init_std
        lowercase__:       Optional[Any]        =       encoder_layerdrop
        lowercase__:       List[str]        =       decoder_layerdrop
        lowercase__:       Union[str, Any]        =       use_cache
        lowercase__:       Any        =       encoder_layers
        lowercase__:       List[str]        =       scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,			eos_token_id=lowerCAmelCase__    ,			is_encoder_decoder=lowerCAmelCase__    ,			decoder_start_token_id=lowerCAmelCase__    ,			forced_eos_token_id=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.encoder_attention_heads
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.d_model


