h dZddlZddlmZddlmZmZddlZddlmZddl m Z ddl m Z dd l mZdd lmZmZdd lmZmZmZmZmZmZdd lmZd dlmZer ddlmZmZndZdZej@e!Z"eedGddeZ#eedGddeZ$eedGddeZ%GddejLZ'GddejLZ(Gd d!ejLZ)dDd"ejTd#e+d$e,d%ejTfd&Z-Gd'd(ejLZ.Gd)d*ejLZ/Gd+d,ejLZ0Gd-d.ejLZ1Gd/d0ejLZ2Gd1d2ejLZ3Gd3d4ejLZ4Gd5d6ejLZ5Gd7d8ejLZ6eGd9d:eZ7eGd;dd?e7Z9ed@GdAdBe7eZ:gdCZ;y)Ez9PyTorch Dilated Neighborhood Attention Transformer model.N) dataclass)OptionalUnion)nn)ACT2FN)BackboneOutput)PreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer) ModelOutputOptionalDependencyNotAvailableauto_docstringis_natten_availableloggingrequires_backends) BackboneMixin) DinatConfig) natten2dav natten2dqkrpbctNrargskwargss g/var/www/html/aiagenthome/venv/lib/python3.12/site-packages/transformers/models/dinat/modeling_dinat.pyrr, ,..ctrrrs rrr/rr zO Dinat encoder's outputs, with potential hidden states and attentions. ) custom_introceZdZUdZdZeejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y)DinatEncoderOutputa reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nlast_hidden_state. hidden_states attentionsreshaped_hidden_states) __name__ __module__ __qualname____doc__r%rtorch FloatTensor__annotations__r&tupler'r(r rr$r$9s}6:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+