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mZ d dlmZ G dd de	ZdS )	    )AnyDictOptionalTuple)random_generator)IntensityAugmentationBase2D)Tensor)adjust_gammac                       s   e Zd ZdZ					ddeeef deeef deded	ed
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ddZ  ZS )RandomGammaa  Apply a random transformation to the gamma of a tensor image.

    This implementation aligns PIL. Hence, the output is close to TorchVision.

    .. image:: _static/img/RandomGamma.png

    Args:
        p: probability of applying the transformation.
        gamma: the gamma factor to apply.
        gain: the gain factor to apply.
        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.adjust_gamma`

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> inputs = torch.rand(1, 3, 3, 3)
        >>> aug = RandomGamma((0.5,2.),(1.5,1.5),p=1.)
        >>> aug(inputs)
        tensor([[[[1.0000, 1.0000, 0.3912],
                  [0.4883, 0.7801, 1.0000],
                  [1.0000, 1.0000, 0.9702]],
        <BLANKLINE>
                 [[1.0000, 0.8368, 0.9048],
                  [0.1824, 0.5597, 0.7609],
                  [1.0000, 1.0000, 1.0000]],
        <BLANKLINE>
                 [[0.5452, 0.7441, 1.0000],
                  [1.0000, 0.8990, 1.0000],
                  [0.9267, 1.0000, 1.0000]]]])

    To apply the exact augmenation again, you may take the advantage of the previous parameter state:
        >>> input = torch.rand(1, 3, 32, 32)
        >>> aug = RandomGamma((0.8,1.2), p=1.)
        >>> (aug(input) == aug(input, params=aug._params)).all()
        tensor(True)

          ?r   Fr   gammagain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   gamma_factorgain_factor)super__init__rgPlainUniformGenerator_param_generator)selfr   r   r   r   r   	__class__ [/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_2d/intensity/gamma.pyr   H   s   
zRandomGamma.__init__inputparamsflags	transformc                 C   s(   |d  |}|d  |}t|||S )Nr   r   )tor	   )r   r   r    r!   r"   r   r   r   r   r   apply_transformU   s   zRandomGamma.apply_transform)r   r   Fr   F)N)__name__
__module____qualname____doc__r   floatboolr   r   r   strr   r   r$   __classcell__r   r   r   r   r
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r
   N)typingr   r   r   r   kornia.augmentationr   r   &kornia.augmentation._2d.intensity.baser   kornia.corer   kornia.enhance.adjustr	   r
   r   r   r   r   <module>   s   