`i dZddlZddlmZddlmZmZddlZddlmZddl m Z ddl m Z dd l mZdd lmZmZdd lmZmZmZmZmZmZdd lmZd dlmZer ddlmZmZndZdZej e!Z"eedGddeZ#eedGddeZ$eedGddeZ%Gddej&Z'Gddej&Z(Gd d!ej&Z)dFd$ej*d%e+d&e,d'ej*fd(Z-Gd)d*ej&Z.Gd+d,ej&Z/Gd-d.ej&Z0Gd/d0ej&Z1Gd1d2ej&Z2Gd3d4ej&Z3Gd5d6ej&Z4Gd7d8ej&Z5Gd9d:ej&Z6eGd;de7Z8ed?Gd@dAe7Z9edBGdCdDe7eZ:gdEZ;dS)Gz9PyTorch 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 |/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/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<dS)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$9s6:x 12999=AM8E%"3S"89:AAA:>Ju0#567>>>FJHU5+