+
    ˜°³j�  ã                   óž   € ^ RI Ht ^ RIHt ^ RIHt ^ RIHt ^ RIH	t	 RR.t
]! RR	R
7      tR t]! R4       ! R R]],          4      4       tR# )é    )ÚCallable)ÚTypeVar)Úfunctional_datapipe)ÚMapDataPipe)Ú_check_unpickable_fnÚMapperMapDataPipeÚ
default_fnÚ_T_coT)Ú	covariantc                 ó   € V # ©N© )Údatas   &Úh/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/utils/data/datapipes/map/callable.pyr	   r	      s   € Ø€Kó    Úmapc                   óx   a a€ ] tR t^t oRt]3V3R lV 3R llltV3R lR ltV3R lR ltV3R lt	R	t
VtV ;t# )
r   aÄ  
Apply the input function over each item from the source DataPipe (functional name: ``map``).

The function can be any regular Python function or partial object. Lambda
function is not recommended as it is not supported by pickle.

Args:
    datapipe: Source MapDataPipe
    fn: Function being applied to each item

Example:
    >>> # xdoctest: +SKIP
    >>> from torchdata.datapipes.map import SequenceWrapper, Mapper
    >>> def add_one(x):
    ...     return x + 1
    >>> dp = SequenceWrapper(range(10))
    >>> map_dp_1 = dp.map(add_one)
    >>> list(map_dp_1)
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    >>> map_dp_2 = Mapper(dp, lambda x: x + 1)
    >>> list(map_dp_2)
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
c                ó*   <€ V ^8„  d   QhRS[ RS[RR/# )é   ÚdatapipeÚfnÚreturnN©r   r   )ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚMapperMapDataPipe.__annotate__4   s)   ø€ ÷ ñ áðñ ðð 
ñ	r   c                óR   <€ \         SV `  4        Wn        \        V4       W n        R # r   )ÚsuperÚ__init__r   r   r   )Úselfr   r   Ú	__class__s   &&&€r   r    ÚMapperMapDataPipe.__init__4   s"   ø€ ô
 	‰ÑÔØ ŒÜ˜RÔ ØŽr   c                ó    <€ V ^8„  d   QhRS[ /# ©r   r   )Úint)r   r   s   "€r   r   r   >   s   ø€ ÷ "ñ "™ñ "r   c                ó,   € \        V P                  4      # r   )Úlenr   )r!   s   &r   Ú__len__ÚMapperMapDataPipe.__len__>   s   € ä�4—=‘=Ó!Ð!r   c                ó    <€ V ^8„  d   QhRS[ /# r%   )r
   )r   r   s   "€r   r   r   B   s   ø€ ÷ -ñ -¡Eñ -r   c                óF   € V P                  V P                  V,          4      # r   )r   r   )r!   Úindexs   &&r   Ú__getitem__ÚMapperMapDataPipe.__getitem__B   s   € Ø�w‰w�t—}‘} UÕ+Ó,Ð,r   c                ó2   <€ V ^8„  d   Qh/ S[ ;R&   S[;R&   # )r   r   r   r   )r   r   s   "€r   r   r      s   ø‡ ‚ ñ4 Ññ5 ñ6 	�Lò7 r   )r   r   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r	   r    r)   r.   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r"   r   s   @@r   r   r      s5   ù‡ € ñð< "÷õ ÷"ð "÷-ð -÷W … r   N)Úcollections.abcr   Útypingr   Ú%torch.utils.data.datapipes._decoratorr   Ú#torch.utils.data.datapipes.datapiper   Ú'torch.utils.data.datapipes.utils.commonr   Ú__all__r
   r	   r   r   r   r   Ú<module>r@      sX   ðå $Ý å EÝ ;Ý Hð  Ð
-€ñ 	� 4Ô(€òñ �UÓô+-˜ EÕ*ó +-ó ò+-r   