o
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                     @   sL   d dl mZmZmZ d dlmZ d dlmZ d dlm	Z	 G dd deZ
dS )    )AnyDictOptional)Tensor)IntensityAugmentationBase2Dequalizec                       sj   e Zd ZdZddedededdf fd	d
Z	ddedee	ef dee	e
f dee def
ddZ  ZS )RandomEqualizeaF  Equalize given tensor image or a batch of tensor images randomly.

    .. image:: _static/img/RandomEqualize.png

    Args:
        p: Probability to equalize an image.
        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.equalize`.

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> input = torch.rand(1, 1, 5, 5)
        >>> equalize = RandomEqualize(p=1.)
        >>> equalize(input)
        tensor([[[[0.4963, 0.7682, 0.0885, 0.1320, 0.3074],
                  [0.6341, 0.4901, 0.8964, 0.4556, 0.6323],
                  [0.3489, 0.4017, 0.0223, 0.1689, 0.2939],
                  [0.5185, 0.6977, 0.8000, 0.1610, 0.2823],
                  [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]])

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

    F      ?same_on_batchpkeepdimreturnNc                    s   t  j|||d d S )N)r   r   r   )super__init__)selfr   r   r   	__class__ ^/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_2d/intensity/equalize.pyr   ?   s   zRandomEqualize.__init__inputparamsflags	transformc                 C   s   t |S Nr   )r   r   r   r   r   r   r   r   apply_transformB   s   zRandomEqualize.apply_transform)Fr
   Fr   )__name__
__module____qualname____doc__boolfloatr   r   r   strr   r   r   __classcell__r   r   r   r   r	      s     $

r	   N)typingr   r   r   torchr   &kornia.augmentation._2d.intensity.baser   kornia.enhancer   r	   r   r   r   r   <module>   s
   