



"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class       snake_case_    (    __lowercase					):
							__lowerCAmelCase            =       ['''image_processor''', '''tokenizer''']
							__lowerCAmelCase            =       '''CLIPImageProcessor'''
							__lowerCAmelCase            =       ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






							def __init__( self      ,  a_=None      ,  a_=None      ,  **a_   ):
									a_  :   Optional[Any]			    =  None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."      ,  a_      ,  )
											a_  :   List[Any]			    =  kwargs.pop("feature_extractor"   )

									a_  :   List[str]			    =  image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."   )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."   )

									super().__init__(a_      ,  a_   )






							def __call__( self      ,  a_=None      ,  a_=None      ,  a_=None      ,  **a_   ):

									if text is None and images is None:
											raise ValueError("You have to specify either text or images. Both cannot be none."   )

									if text is not None:
											a_  :   int			    =  self.tokenizer(a_      ,  return_tensors=a_      ,  **a_   )

									if images is not None:
											a_  :   str			    =  self.image_processor(a_      ,  return_tensors=a_      ,  **a_   )

									if text is not None and images is not None:
											a_  :   Union[str, Any]			    =  image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**a_   )      ,  tensor_type=a_   )






							def snake_case_	( self      ,  *a_      ,  **a_   ):
									return self.tokenizer.batch_decode(*a_      ,  **a_   )






							def snake_case_	( self      ,  *a_      ,  **a_   ):
									return self.tokenizer.decode(*a_      ,  **a_   )






							@property
							def snake_case_	( self   ):
									a_  :   Dict			    =  self.tokenizer.model_input_names
									a_  :   str			    =  self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






							@property
							def snake_case_	( self   ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,  a_      ,  )
									return self.image_processor_class






							@property
							def snake_case_	( self   ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,  a_      ,  )
									return self.image_processor






'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase					    =	"\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"

__UpperCamelCase					    =	"\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"

__UpperCamelCase					    =	"\\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  _A (					datasets.Metric		):

		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""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 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]=None			)					->    Optional[int]:


					"""simple docstring"""

					return {
					    "matthews_correlation": float(matthews_corrcoef(__magic_name__  ,      __magic_name__  ,      sample_weight=__magic_name__			)			),
					}


