a h@s\ddlmZddlmZddlmZddlmZddgZGdddeZ Gd ddeZ d S) )Union)Tensor)_size)ModuleFlatten UnflattencsdeZdZUdZddgZeed<eed<deeddfdd Zeed d d Z e d ddZ Z S)ra 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_dimend_dimrN)r r returncst||_||_dSN)super__init__r r )selfr r  __class__F/var/www/auris/lib/python3.9/site-packages/torch/nn/modules/flatten.pyr/s zFlatten.__init__inputr cCs||j|jSr )flattenr r rrrrrforward4szFlatten.forwardr cCsd|jd|jS)Nz start_dim=z , end_dim=)r r rrrr extra_repr7szFlatten.extra_repr)rr ) __name__ __module__ __qualname____doc__ __constants__int__annotations__rrrstrr __classcell__rrrrr s cseZdZUdZeeeefZddgZe eefe d<e e efe d<e eefe e efddfdd Z dd Z d d Zeed d dZedddZZS)ra 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]) dimunflattened_sizeN)r&r'r csNtt|tr ||nt|tr6||ntd||_||_ dS)Nz'invalid argument type for dim parameter) rr isinstancer"_require_tuple_intr$_require_tuple_tuple TypeErrorr&r')rr&r'rrrrps     zUnflatten.__init__cCsdt|trHt|D]0\}}t|tstddt|jd|qdStddt|jdS)Nz*unflattened_size must be tuple of tuples, but found element of type  at pos z,unflattened_size must be a tuple of tuples, zbut found type )r(tuple enumerater+typerrridxelemrrrr*s  zUnflatten._require_tuple_tuplecCsdt|ttfrLt|D]0\}}t|tstddt|jd|qdStdt|jdS)Nz(unflattened_size must be tuple of ints, r,r-z9unflattened_size must be a tuple of ints, but found type )r(r.listr/r"r+r0rr1rrrr)s zUnflatten._require_tuple_intrcCs||j|jSr )Z unflattenr&r'rrrrrszUnflatten.forwardrcCsd|jd|jS)Nzdim=z, unflattened_size=)r&r'rrrrrszUnflatten.extra_repr)rrrr r.r$r"Z NamedShaper!rr#rrr*r)rrrr%rrrrr;s . N) typingrZtorchrZ torch.typesrmoduler__all__rrrrrrs    .