tj6jddlmZddlmZddlmZddgZGddeZGddeZd S) )Tensor)_size)ModuleFlatten UnflattencleZdZUdZddgZeed<eed<d dededdffd Zd edefd Z de fd Z xZ 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_dimrreturnNcdt||_||_dSN)super__init__r r )selfr r __class__s V/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/nn/modules/flatten.pyrzFlatten.__init__.s+ " inputcB||j|jSz( Runs the forward pass. )flattenr r rrs rforwardzFlatten.forward3s}}T^T\:::rc&d|jd|jS)A Returns the extra representation of the module. z start_dim=z , end_dim=)r r rs r extra_reprzFlatten.extra_repr9sEDNDDdlDDDr)rr ) __name__ __module__ __qualname____doc__ __constants__int__annotations__rrrstrr __classcell__rs@rrr s:!),MNNN LLL#C ;V;;;;; ECEEEEEEEErcreZdZUdZddgZeed<eed<dededdffd Zd dZ de de fd Z de fd Z xZS) 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. * :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]) dimunflattened_sizer Nct||||_||_dSr)rr_require_tuple_intr+r,)rr+r,rs rrzUnflatten.__init__ksC   0111 0rc.t|ttfrVt|D]D\}}t|ts*t ddt |jd|zEdSt dt |j)Nz(unflattened_size must be tuple of ints, zbut found element of type z at pos z9unflattened_size must be a tuple of ints, but found type ) isinstancetuplelist enumerater% TypeErrortyper )rridxelems rr.zUnflatten._require_tuple_intqs eeT] + + &u--   T!$,,#BYtDzz7JYYTWYYZ F ^U H\ ^ ^   rrcB||j|jSr) unflattenr+r,rs rrzUnflatten.forward~stx)>???rc&d|jd|jS)rzdim=z, unflattened_size=)r+r,rs rrzUnflatten.extra_reprs KdhJJ43HJJJr)r N)r r!r"r#r$r%r&rrr.rrr'rr(r)s@rrr@s$$L./M HHH1C151T111111     @V@@@@@ KCKKKKKKKKrN) torchr torch.typesrmoduler__all__rrrrr@s k "1E1E1E1E1Ef1E1E1EhHKHKHKHKHKHKHKHKHKHKr