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mZ d dlmZ d dlmZ G dd deZdS )	    )AnyDictListOptionalTupleUnion)random_generator)IntensityAugmentationBase2D)Tensorsolarizec                       s   e Zd ZdZ					ddeeeeeef ee f deeeeeef ee f de	ded	e	d
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ef
ddZ  ZS )RandomSolarizea  Solarize given tensor image or a batch of tensor images randomly.

    .. image:: _static/img/RandomSolarize.png

    Args:
        p: probability of applying the transformation.
        thresholds:
            If float x, threshold will be generated from (0.5 - x, 0.5 + x).
            If tuple (x, y), threshold will be generated from (x, y).
        additions:
            If float x, addition will be generated from (-x, x).
            If tuple (x, y), addition will be generated from (x, y).
        same_on_batch: apply the same transformation across the batch.
        keepdim: whether to keep the output shape the same as input (True) or broadcast it
                 to the batch form (False).

    Shape:
        - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
        - Output: :math:`(B, C, H, W)`

    .. note::
        This function internally uses :func:`kornia.enhance.solarize`.

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> input = torch.rand(1, 1, 5, 5)
        >>> solarize = RandomSolarize(0.1, 0.1, p=1.)
        >>> solarize(input)
        tensor([[[[0.4132, 0.1412, 0.1790, 0.2226, 0.3980],
                  [0.2754, 0.4194, 0.0130, 0.4538, 0.2771],
                  [0.4394, 0.4923, 0.1129, 0.2594, 0.3844],
                  [0.3909, 0.2118, 0.1094, 0.2516, 0.3728],
                  [0.2278, 0.0000, 0.4876, 0.0353, 0.5100]]]])

    To apply the exact augmenation again, you may take the advantage of the previous parameter state:
        >>> input = torch.randn(1, 3, 32, 32)
        >>> aug = RandomSolarize(0.1, 0.1, p=1.)
        >>> (aug(input) == aug(input, params=aug._params)).all()
        tensor(True)

    皙?F      ?
thresholds	additionssame_on_batchpkeepdimreturnNc                    s4   t  j|||d t|dddf|dddf| _d S )N)r   r   r   r   r   )        g      ?r   r   )g      r   )super__init__rgPlainUniformGenerator_param_generator)selfr   r   r   r   r   	__class__ ^/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_2d/intensity/solarize.pyr   E   s   
zRandomSolarize.__init__inputparamsflags	transformc                 C   s*   |d }d|v r|d }nd }t |||S )Nr   r   r   )r   r!   r"   r#   r$   r   r   r   r   r    apply_transformR   s
   
zRandomSolarize.apply_transform)r   r   Fr   F)N)__name__
__module____qualname____doc__r   r
   floatr   r   boolr   r   strr   r   r%   __classcell__r   r   r   r    r      s@    ,

r   N)typingr   r   r   r   r   r   kornia.augmentationr   r   &kornia.augmentation._2d.intensity.baser	   kornia.corer
   kornia.enhancer   r   r   r   r   r    <module>   s    