Uh| \dZddlZddlmZddlmZmZmZddlZddl Zddlm Z ddl m Z m Z mZddlmZdd lmZdd lmZdd lmZmZdd lmZmZmZmZmZmZdd lm Z ddl!m"Z"er ddl#m$Z$m%Z%ndZ%dZ$ejLe'Z(eGddeZ)eGddeZ*eGddeZ+Gdde jXZ-Gdde jXZ.Gdde jXZ/dBdej`d e1d!e2d"ej`fd#Z3Gd$d%e jXZ4Gd&d'e jXZ5Gd(d)e jXZ6Gd*d+e jXZ7Gd,d-e jXZ8Gd.d/e jXZ9Gd0d1e jXZ:Gd2d3e jXZ;Gd4d5e jXZed:;Gd<d=e=Z?ed>;Gd?d@e=e Z@gdAZAy)Cz9PyTorch Dilated Neighborhood Attention Transformer model.N) dataclass)OptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BackboneOutput)PreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer) ModelOutputOptionalDependencyNotAvailableauto_docstringis_natten_availableloggingrequires_backends) BackboneMixin) DinatConfig) natten2dav natten2dqkrpbctNrargskwargss z/var/www/catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/dinat/modeling_dinat.pyrr. ,..ctrrrs r"rr1r#r$ceZdZUdZdZeejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y)DinatEncoderOutputa Dinat encoder's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nlast_hidden_state. hidden_states attentionsreshaped_hidden_states) __name__ __module__ __qualname____doc__r(rtorch FloatTensor__annotations__r)rr*r+r$r"r'r';s}26:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+