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S )    )AnyDictOptionalTuple)random_generator)IntensityAugmentationBase2D)_range_bound)pi)Tensor)
adjust_huec                       sx   e Zd ZdZ	ddeeef dededed	d
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ddZ  ZS )	RandomHueaX  Apply a random transformation to the hue of a tensor image.

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

    .. image:: _static/img/RandomHue.png

    Args:
        hue: the saturation factor to apply.
        same_on_batch: apply the same transformation across the batch.
        p: probability of applying the transformation.
        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_hue`

    Examples:
        >>> rng = torch.manual_seed(0)
        >>> inputs = torch.rand(1, 3, 3, 3)
        >>> aug = RandomHue(hue = (-0.5,0.5),p=1.)
        >>> aug(inputs)
        tensor([[[[0.3993, 0.2823, 0.6816],
                  [0.6117, 0.2090, 0.4081],
                  [0.4693, 0.5529, 0.9527]],
        <BLANKLINE>
                 [[0.1610, 0.5962, 0.4971],
                  [0.9152, 0.3971, 0.8742],
                  [0.4194, 0.6771, 0.7162]],
        <BLANKLINE>
                 [[0.6323, 0.7682, 0.0885],
                  [0.0223, 0.1689, 0.2939],
                  [0.5185, 0.8964, 0.4556]]]])

    To apply the exact augmenation again, you may take the advantage of the previous parameter state:

        >>> input = torch.rand(1, 3, 32, 32)
        >>> aug = RandomHue((-0.2,0.2), p=1.)
        >>> (aug(input) == aug(input, params=aug._params)).all()
        tensor(True)

            r   F      ?huesame_on_batchpkeepdimreturnNc                    s<   t  j|||d t|ddd| _t| jdd d f| _d S )N)r   r   r   r   )g      g      ?)bounds
hue_factor)super__init__r   r   rgPlainUniformGenerator_param_generator)selfr   r   r   r   	__class__ Y/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/_2d/intensity/hue.pyr   J   s   zRandomHue.__init__inputparamsflags	transformc                 C   s    |d  |}t||d t S )Nr      )tor   r	   )r   r!   r"   r#   r$   r   r   r   r    apply_transformQ   s   zRandomHue.apply_transform)r   Fr   F)N)__name__
__module____qualname____doc__r   floatboolr   r
   r   strr   r   r'   __classcell__r   r   r   r    r      s4    .


r   N)typingr   r   r   r   kornia.augmentationr   r   &kornia.augmentation._2d.intensity.baser   kornia.augmentation.utilsr   kornia.constantsr	   kornia.corer
   kornia.enhance.adjustr   r   r   r   r   r    <module>   s   