+
    ˜°³j6  ã                   ó^   € ^ RI Ht ^ RIHt ^RIHt RR.t ! R R]4      t ! R R]4      tR# )	é    ©ÚTensor)Ú_size)ÚModuleÚFlattenÚ	Unflattenc                   ó~   a a€ ] tR t^t oRtRR.tR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   aS  
Flattens a contiguous range of dims into a tensor.

For use with :class:`~nn.Sequential`, see :meth:`torch.flatten` for details.

Shape:
    - Input: :math:`(*, S_{\text{start}},..., S_{i}, ..., S_{\text{end}}, *)`,'
      where :math:`S_{i}` is the size at dimension :math:`i` and :math:`*` means any
      number of dimensions including none.
    - Output: :math:`(*, \prod_{i=\text{start}}^{\text{end}} S_{i}, *)`.

Args:
    start_dim: first dim to flatten (default = 1).
    end_dim: last dim to flatten (default = -1).

Examples::
    >>> input = torch.randn(32, 1, 5, 5)
    >>> # With default parameters
    >>> m = nn.Flatten()
    >>> output = m(input)
    >>> output.size()
    torch.Size([32, 25])
    >>> # With non-default parameters
    >>> m = nn.Flatten(0, 2)
    >>> output = m(input)
    >>> output.size()
    torch.Size([160, 5])
Ú	start_dimÚend_dimc                ó*   <€ V ^8„  d   QhRS[ RS[ RR/# )é   r
   r   ÚreturnN©Úint)ÚformatÚ__classdict__s   "€ÚY/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/nn/modules/flatten.pyÚ__annotate__ÚFlatten.__annotate__.   s"   ø€ ÷ ñ ¡#ð ±Cð Àñ ó    c                ó<   <€ \         SV `  4        Wn        W n        R # ©N)ÚsuperÚ__init__r
   r   )Úselfr
   r   Ú	__class__s   &&&€r   r   ÚFlatten.__init__.   s   ø€ Ü‰ÑÔØ"ŒØŽr   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# ©r   Úinputr   r   )r   r   s   "€r   r   r   3   s   ø€ ÷ ;ñ ;™Vð ;©ñ ;r   c                óN   € VP                  V P                  V P                  4      # ©z
Runs the forward pass.
)Úflattenr
   r   ©r   r    s   &&r   ÚforwardÚFlatten.forward3   s   € ð �}‰}˜TŸ^™^¨T¯\©\Ó:Ð:r   c                ó    <€ V ^8„  d   QhRS[ /# ©r   r   ©Ústr)r   r   s   "€r   r   r   9   s   ø€ ÷ Eñ E™Cñ Er   c                ó:   € RV P                    RV P                   2# )ú1
Returns the extra representation of the module.
z
start_dim=z
, end_dim=)r
   r   ©r   s   &r   Ú
extra_reprÚFlatten.extra_repr9   s    € ð ˜DŸN™NÐ+¨:°d·l±l°^ÐDÐDr   c                ó2   <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   # )r   r
   r   r   )r   r   s   "€r   r   r      s   ø‡ ‚ ñ> �Nñ? ñ@ �LòA r   )r   r
   )é   éÿÿÿÿ)Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú__constants__r   r%   r.   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__©r   r   s   @@r   r   r      s>   ù‡ € ñð: ! )Ð,€M÷õ ÷
;ð ;÷Eð E÷[ … r   c                   óŒ   a a€ ] tR t^@t oRtRR.tV3R lV 3R lltV3R lR 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   a  
Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.

* :attr:`dim` specifies the dimension of the input tensor to be unflattened.

* :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
  a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input.

Shape:
    - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
      dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
    - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
      :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.

Args:
    dim (int): Dimension to be unflattened
    unflattened_size (Union[torch.Size, Tuple, List]): New shape of the unflattened dimension

Examples:
    >>> input = torch.randn(2, 50)
    >>> # With tuple of ints
    >>> m = nn.Sequential(
    >>>     nn.Linear(50, 50),
    >>>     nn.Unflatten(1, (2, 5, 5))
    >>> )
    >>> output = m(input)
    >>> output.size()
    torch.Size([2, 2, 5, 5])
    >>> # With torch.Size
    >>> m = nn.Sequential(
    >>>     nn.Linear(50, 50),
    >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
    >>> )
    >>> output = m(input)
    >>> output.size()
    torch.Size([2, 2, 5, 5])
ÚdimÚunflattened_sizec                ó*   <€ V ^8„  d   QhRS[ RS[RR/# )r   r?   r@   r   N©r   r   )r   r   s   "€r   r   ÚUnflatten.__annotate__k   s"   ø€ ÷ 1ñ 1™Cð 1±5ð 1¸Tñ 1r   c                ó^   <€ \         SV `  4        V P                  V4       Wn        W n        R # r   )r   r   Ú_require_tuple_intr?   r@   )r   r?   r@   r   s   &&&€r   r   ÚUnflatten.__init__k   s(   ø€ Ü‰ÑÔØ×ÑÐ 0Ô1ØŒØ 0Ör   c                ó   <€ V ^8„  d   QhRR/# )r   r   N© )r   r   s   "€r   r   rC   q   s   ø€ ÷ 
ñ 
¨4ñ 
r   c                ó*  € \        V\        \        34      '       dX   \        V4       FF  w  r#\        V\        4      '       d   K  \        R R\        V4      P                   RV 2,           4      h	  R# \        R\        V4      P                   24      h)z(unflattened_size must be tuple of ints, zbut found element of type z at pos Nz9unflattened_size must be a tuple of ints, but found type )Ú
isinstanceÚtupleÚlistÚ	enumerater   Ú	TypeErrorÚtyper3   )r   r    ÚidxÚelems   &&  r   rE   ÚUnflatten._require_tuple_intq   s‹   € Ü�eœe¤T˜]×+Ò+Ü& uÖ-‘	�Ü! $¬×,Ô,Ü#ØBØ6´t¸D³z×7JÑ7JÐ6KÈ8ÐTWÐSXÐYõZóð ñ .ñ ÜØGÌÈUË×H\ÑH\ÐG]Ð^ó
ð 	
r   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# r   r   )r   r   s   "€r   r   rC   ~   s   ø€ ÷ @ñ @™Vð @©ñ @r   c                óN   € VP                  V P                  V P                  4      # r"   )Ú	unflattenr?   r@   r$   s   &&r   r%   ÚUnflatten.forward~   s   € ð �‰˜tŸx™x¨×)>Ñ)>Ó?Ð?r   c                ó    <€ V ^8„  d   QhRS[ /# r(   r)   )r   r   s   "€r   r   rC   „   s   ø€ ÷ Kñ K™Cñ Kr   c                ó:   € RV P                    RV P                   2# )r,   zdim=z, unflattened_size=©r?   r@   r-   s   &r   r.   ÚUnflatten.extra_repr„   s#   € ð �d—h‘h�ZÐ2°4×3HÑ3HÐ2IÐJÐJr   c                ó2   <€ V ^8„  d   Qh/ S[ ;R&   S[;R&   # )r   r?   r@   rB   )r   r   s   "€r   r   rC   @   s"   ø‡ ‚ ñP 
�HñQ ñR ÑòS r   rY   )r3   r4   r5   r6   r7   r8   r   rE   r%   r.   r9   r:   r;   r<   r=   s   @@r   r   r   @   sL   ù‡ € ñ$ðL Ð.Ð/€M÷1ó 1÷
ð 
÷@ð @÷Kð K÷I … r   N)	Útorchr   Útorch.typesr   Úmoduler   Ú__all__r   r   rH   r   r   Ú<module>r`      s;   ðõ Ý å ð �kÐ
"€ô1Eˆfô 1EôhHK�ö HKr   