+
     °³j”  ã                   ód   € ^ RI t ^ RIHtHt ^ RIt^ RIt^ RIt^ RI	H
t ^ RIHt  ! R R]4      tR# )é    N)ÚAnyÚUnion)Ú
functional)Ú	Transformc                   ó˜   a a€ ] tR t^t oRt]P                  P                  ]P                  3t	V3R lV 3R llt
V3R lR ltRtVtV ;t# )ÚToTensoraB  [DEPRECATED] Use ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`` instead.

Convert a PIL Image or ndarray to tensor and scale the values accordingly.

.. warning::
    :class:`v2.ToTensor` is deprecated and will be removed in a future release.
    Please use instead ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])``.
    Output is equivalent up to float precision.

This transform does not support torchscript.


Converts a PIL Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
or if the numpy.ndarray has dtype = np.uint8

In the other cases, tensors are returned without scaling.

.. note::
    Because the input image is scaled to [0.0, 1.0], this transformation should not be used when
    transforming target image masks. See the `references`_ for implementing the transforms for image masks.

.. _references: https://github.com/pytorch/vision/tree/main/references/segmentation
c                ó   <€ V ^8„  d   QhRR/# )é   ÚreturnN© )ÚformatÚ__classdict__s   "€Úf/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/transforms/v2/_deprecated.pyÚ__annotate__ÚToTensor.__annotate__)   s   ø€ ÷ ñ ˜$ñ ó    c                óP   <€ \         P                  ! R 4       \        SV `  4        R# )zÕThe transform `ToTensor()` is deprecated and will be removed in a future release. Instead, please use `v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`.Output is equivalent up to float precision.N)ÚwarningsÚwarnÚsuperÚ__init__)ÚselfÚ	__class__s   &€r   r   ÚToTensor.__init__)   s!   ø€ Ü�Šð:ô	
ô
 	‰ÑÖr   c                ó¨   <€ V ^8„  d   QhRS[ S[P                  P                  S[P                  3,          RS[S[S[3,          RS[P                  /# )r
   ÚinptÚparamsr   )
r   ÚPILÚImageÚnpÚndarrayÚdictÚstrr   ÚtorchÚTensor)r   r   s   "€r   r   r   1   sH   ø€ ÷ "ñ "™e¡C§I¡I§O¡O±R·Z±ZÐ$?Õ@ð "É$ÉsÑTWÈxÍ.ð "Ñ]b×]iÑ]iñ "r   c                ó.   € \         P                  ! V4      # )N)Ú_FÚ	to_tensor)r   r   r   s   &&&r   Ú	transformÚToTensor.transform1   s   € Ü�|Š|˜DÓ!Ð!r   r   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r   r   r    r!   Ú_transformed_typesr   r)   Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r   s   @@r   r   r      s<   ù‡ € ñð4 Ÿ)™)Ÿ/™/¨2¯:©:Ð6Ð÷ó ÷"÷ "ð "r   r   )r   Útypingr   r   Únumpyr    Ú	PIL.Imager   r$   Útorchvision.transformsr   r'   Útorchvision.transforms.v2r   r   r   r   r   Ú<module>r9      s&   ðÛ ß ã Û Û Ý 3å /ô&"ˆyö &"r   