
    Yh                     v    d dl mZ d dlmZ d dlmZ ddlmZ ddgZ G d de          Z	 G d	 de          Z
d
S )    )Union)Tensor)_size   )ModuleFlatten	Unflattenc                   l     e Zd ZU dZddgZeed<   eed<   ddededdf fdZd	edefd
Z	de
fdZ xZS )r   a  
    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])
    	start_dimend_dimr   returnNc                 d    t                                                       || _        || _        d S N)super__init__r   r   )selfr   r   	__class__s      j/var/www/tools.fuzzalab.pt/emblema-extractor/venv/lib/python3.11/site-packages/torch/nn/modules/flatten.pyr   zFlatten.__init__/   s+    "    inputc                 B    |                     | j        | j                  S r   )flattenr   r   r   r   s     r   forwardzFlatten.forward4   s    }}T^T\:::r   c                 &    d| j          d| j         S )Nz
start_dim=z
, end_dim=)r   r   r   s    r   
extra_reprzFlatten.extra_repr7   s    DDNDDdlDDDr   )r   r   )__name__
__module____qualname____doc____constants__int__annotations__r   r   r   strr   __classcell__r   s   @r   r   r      s          : !),MNNNLLL # C       
;V ; ; ; ; ;EC E E E E E E E Er   c                        e Zd ZU dZeeeef                  ZddgZe	eef         e
d<   e	eef         e
d<   de	eef         de	eef         ddf fdZd Zd Zd	edefd
ZdefdZ xZS )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, and it can
      be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.

    * :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;  a `NamedShape`
      (tuple of `(name, size)` tuples) for `NamedTensor` 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 (Union[int, str]): Dimension to be unflattened
        unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): 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])
        >>> # With namedshape (tuple of tuples)
        >>> input = torch.randn(2, 50, names=("N", "features"))
        >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5)))
        >>> output = unflatten(input)
        >>> output.size()
        torch.Size([2, 2, 5, 5])
    dimunflattened_sizer   Nc                 .   t                                                       t          |t                    r|                     |           n:t          |t
                    r|                     |           nt          d          || _        || _	        d S )Nz'invalid argument type for dim parameter)
r   r   
isinstancer$   _require_tuple_intr&   _require_tuple_tuple	TypeErrorr*   r+   )r   r*   r+   r   s      r   r   zUnflatten.__init__p   s     	c3 	G##$45555S!! 	G%%&67777EFFF 0r   c                 &   t          |t                    rVt          |          D ]D\  }}t          |t                    s*t          ddt	          |          j         d| z             Ed S t          ddt	          |          j         z             )Nz*unflattened_size must be tuple of tuples, but found element of type  at pos z,unflattened_size must be a tuple of tuples, zbut found type )r-   tuple	enumerater0   typer   r   r   idxelems       r   r/   zUnflatten._require_tuple_tuple   s    eU## 	&u--  	T!$.. #DYtDzz7JYYTWYYZ  
 F:6U 4667
 
 	
r   c                 .   t          |t          t          f          rVt          |          D ]D\  }}t          |t                    s*t          ddt          |          j         d| z             Ed S t          dt          |          j                   )Nz(unflattened_size must be tuple of ints, r2   r3   z9unflattened_size must be a tuple of ints, but found type )r-   r4   listr5   r$   r0   r6   r   r7   s       r   r.   zUnflatten._require_tuple_int   s    eeT]++ 	&u--  	T!$,, #BYtDzz7JYYTWYYZ  
 F^UH\^^
 
 	
r   r   c                 B    |                     | j        | j                  S r   )	unflattenr*   r+   r   s     r   r   zUnflatten.forward   s    tx)>???r   c                 &    d| j          d| j         S )Nzdim=z, unflattened_size=)r*   r+   r   s    r   r   zUnflatten.extra_repr   s    JdhJJ43HJJJr   )r   r    r!   r"   r4   r&   r$   
NamedShaper#   r   r%   r   r   r/   r.   r   r   r   r'   r(   s   @r   r	   r	   ;   s'        , ,\ uS#X'J./M	sCxE:-....1c?16;E:<M6N1	1 1 1 1 1 1
 
 

 
 
@V @ @ @ @ @KC K K K K K K K Kr   N)typingr   torchr   torch.typesr   moduler   __all__r   r	    r   r   <module>rF      s                            k
"+E +E +E +E +Ef +E +E +E\cK cK cK cK cK cK cK cK cK cKr   