






'''simple docstring'''



from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase   :							Union[str, Any]         =   """
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]
"""

__UpperCamelCase   :							str         =   """
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
"""

__UpperCamelCase   :							int         =   """\
@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 				__SCREAMING_SNAKE_CASE							(				datasets.Metric  ):




      def 			__UpperCamelCase				(	self   )				->str:



         '''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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase=None   )				->str:



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







'''simple docstring'''



import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


__UpperCamelCase   :							int         =   logging.get_logger(__name__)

__UpperCamelCase   :							int         =   {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_text_model"




      def __init__(	self			, lowerCamelCase=3_0522			, lowerCamelCase=768			, lowerCamelCase=12			, lowerCamelCase=12			, lowerCamelCase=3072			, lowerCamelCase="gelu"			, lowerCamelCase=0.1			, lowerCamelCase=0.1			, lowerCamelCase=512			, lowerCamelCase=2			, lowerCamelCase=0.02			, lowerCamelCase=1e-12			, lowerCamelCase=0			, lowerCamelCase="absolute"			, lowerCamelCase=True			, **lowerCamelCase			, )				->List[Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     vocab_size
         __a					  =     hidden_size
         __a					  =     num_hidden_layers
         __a					  =     num_attention_heads
         __a					  =     hidden_act
         __a					  =     intermediate_size
         __a					  =     hidden_dropout_prob
         __a					  =     attention_probs_dropout_prob
         __a					  =     max_position_embeddings
         __a					  =     type_vocab_size
         __a					  =     initializer_range
         __a					  =     layer_norm_eps
         __a					  =     position_embedding_type
         __a					  =     use_cache
         __a					  =     pad_token_id






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the text config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['text_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_vision_model"




      def __init__(	self			, lowerCamelCase = 3			, lowerCamelCase = 600			, lowerCamelCase = 2.0			, lowerCamelCase = 3.1			, lowerCamelCase = 8			, lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]			, lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]			, lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]			, lowerCamelCase = []			, lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]			, lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]			, lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]			, lowerCamelCase = 0.25			, lowerCamelCase = "swish"			, lowerCamelCase = 2560			, lowerCamelCase = "mean"			, lowerCamelCase = 0.02			, lowerCamelCase = 0.001			, lowerCamelCase = 0.99			, lowerCamelCase = 0.2			, **lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     num_channels
         __a					  =     image_size
         __a					  =     width_coefficient
         __a					  =     depth_coefficient
         __a					  =     depth_divisor
         __a					  =     kernel_sizes
         __a					  =     in_channels
         __a					  =     out_channels
         __a					  =     depthwise_padding
         __a					  =     strides
         __a					  =     num_block_repeats
         __a					  =     expand_ratios
         __a					  =     squeeze_expansion_ratio
         __a					  =     hidden_act
         __a					  =     hidden_dim
         __a					  =     pooling_type
         __a					  =     initializer_range
         __a					  =     batch_norm_eps
         __a					  =     batch_norm_momentum
         __a					  =     drop_connect_rate
         __a					  =     sum(lowerCamelCase   ) * 4






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the vision config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align"
      __a	       =True




      def __init__(	self			, lowerCamelCase=None			, lowerCamelCase=None			, lowerCamelCase=640			, lowerCamelCase=1.0			, lowerCamelCase=0.02			, **lowerCamelCase			, )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         if text_config is None:
            __a					  =     {}
            logger.info('text_config is None. Initializing the AlignTextConfig with default values.'   )

         if vision_config is None:
            __a					  =     {}
            logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'   )

         __a					  =     AlignTextConfig(**lowerCamelCase   )
         __a					  =     AlignVisionConfig(**lowerCamelCase   )

         __a					  =     projection_dim
         __a					  =     temperature_init_value
         __a					  =     initializer_range




      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, lowerCamelCase			, **lowerCamelCase   )				->int:



         '''simple docstring'''
         return cls(text_config=text_config.to_dict()			, vision_config=vision_config.to_dict()			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     copy.deepcopy(self.__dict__   )
         __a					  =     self.text_config.to_dict()
         __a					  =     self.vision_config.to_dict()
         __a					  =     self.__class__.model_type
         return output