





from sklearn.metrics import matthews_corrcoef

import datasets


A   : Optional[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]
"""

A   : int			=				"""
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
"""

A   : Any			=				"""\
@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   ):
   '''simple docstring'''




   def        a_	(      self       :   Union[str, Any]       )     ->     Optional[int]:







     """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        a_	(      self       :   Dict							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   Dict=None       )     ->     Dict:







     """simple docstring"""






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










from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


A   : int			=				logging.get_logger(__name__)


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Optional[Any]          =   ['''pixel_values''']






			def __init__(      self       :   Dict							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = 0.9							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   Union[int, float] = 1 / 2_55							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     **__lowerCAmelCase       :   Optional[int]							,     )     ->     None:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase       )
					A__  				=       size if size is not None else {"""shortest_edge""": 2_24}
					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					A__  				=       crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
					A__  				=       get_size_dict(__lowerCAmelCase							,     param_name="""crop_size"""       )

					A__  				=       do_resize
					A__  				=       size
					A__  				=       crop_pct
					A__  				=       resample
					A__  				=       do_center_crop
					A__  				=       crop_size
					A__  				=       do_rescale
					A__  				=       rescale_factor
					A__  				=       do_normalize
					A__  				=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					A__  				=       image_std if image_std is not None else IMAGENET_DEFAULT_STD






			def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[float] = None							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







					"""simple docstring"""






					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					if "shortest_edge" not in size and ("height" not in size or "width" not in size):
							raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
					if crop_pct is not None:
							if "shortest_edge" in size:
									A__  				=       int(size["""shortest_edge"""] / crop_pct       )
							elif "height" in size and "width" in size:
									if size["height"] == size["width"]:
											A__  				=       int(size["""height"""] / crop_pct       )
									else:
											A__  				=       (int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
							else:
									raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

							A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					else:
							if "shortest_edge" in size:
									A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=size["""shortest_edge"""]							,     default_to_square=__lowerCAmelCase       )
							elif "height" in size and "width" in size:
									A__  				=       (size["""height"""], size["""width"""])
							else:
									raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

					return resize(__lowerCAmelCase							,     size=__lowerCAmelCase							,     resample=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Optional[Any]							,     )     ->     np.ndarray:







					"""simple docstring"""






					A__  				=       get_size_dict(__lowerCAmelCase       )
					if "height" not in size or "width" not in size:
							raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
					return center_crop(__lowerCAmelCase							,     size=(size["""height"""], size["""width"""])							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[int, float]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Dict							,     )     ->     List[str]:







					"""simple docstring"""






					return rescale(__lowerCAmelCase							,     scale=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







					"""simple docstring"""






					return normalize(__lowerCAmelCase							,     mean=__lowerCAmelCase							,     std=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )







			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   ImageInput							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = None							,     __lowerCAmelCase       :   PILImageResampling = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   float = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[str, TensorType]] = None							,     __lowerCAmelCase       :   ChannelDimension = ChannelDimension.FIRST							,     **__lowerCAmelCase       :   Tuple							,     )     ->     PIL.Image.Image:







					"""simple docstring"""






					A__  				=       do_resize if do_resize is not None else self.do_resize
					A__  				=       crop_pct if crop_pct is not None else self.crop_pct
					A__  				=       resample if resample is not None else self.resample
					A__  				=       do_center_crop if do_center_crop is not None else self.do_center_crop
					A__  				=       do_rescale if do_rescale is not None else self.do_rescale
					A__  				=       rescale_factor if rescale_factor is not None else self.rescale_factor
					A__  				=       do_normalize if do_normalize is not None else self.do_normalize
					A__  				=       image_mean if image_mean is not None else self.image_mean
					A__  				=       image_std if image_std is not None else self.image_std

					A__  				=       size if size is not None else self.size
					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					A__  				=       crop_size if crop_size is not None else self.crop_size
					A__  				=       get_size_dict(__lowerCAmelCase							,     param_name="""crop_size"""       )

					A__  				=       make_list_of_images(__lowerCAmelCase       )

					if not valid_images(__lowerCAmelCase       ):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

					if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True."""       )

					if do_center_crop and crop_pct is None:
							raise ValueError("""Crop_pct must be specified if do_center_crop is True."""       )

					if do_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

					# All transformations expect numpy arrays.
					A__  				=       [to_numpy_array(__lowerCAmelCase       ) for image in images]

					if do_resize:
							A__  				=       [self.resize(image=__lowerCAmelCase							,     size=__lowerCAmelCase							,     crop_pct=__lowerCAmelCase							,     resample=__lowerCAmelCase       ) for image in images]

					if do_center_crop:
							A__  				=       [self.center_crop(image=__lowerCAmelCase							,     size=__lowerCAmelCase       ) for image in images]

					if do_rescale:
							A__  				=       [self.rescale(image=__lowerCAmelCase							,     scale=__lowerCAmelCase       ) for image in images]

					if do_normalize:
							A__  				=       [self.normalize(image=__lowerCAmelCase							,     mean=__lowerCAmelCase							,     std=__lowerCAmelCase       ) for image in images]

					A__  				=       [to_channel_dimension_format(__lowerCAmelCase							,     __lowerCAmelCase       ) for image in images]

					A__  				=       {"""pixel_values""": images}
					return BatchFeature(data=__lowerCAmelCase							,     tensor_type=__lowerCAmelCase       )



