fTh: xSrSSKrSSKrSSKrSSKJr SSKJrJ r J r SSK r SSK r SSK J r Jr SSKJr SSKJr SS KJr SS KJrJrJr SS KJrJrJrJr SS KJr S SK J!r! \RD"\#5r$\"SS\55r%\"SS\55r&\"SS\55r'\"SS\55r(Sr)Sr*SES\ RS\+S\,S\ R4Sjjr-"SS\R\5r/"S S!\R\5r0"S"S#\R\5r1"S$S%\R\5r2"S&S'\R\5r3"S(S)\R\5r4"S*S+\R\5r5"S,S-\R\5r6"S.S/\R\5r7"S0S1\R\5r8"S2S3\R\5r9"S4S5\R\5r:\"S6S7\55r;\"S8S9\;55r<\"S:S;9"S<S=\;55r=\"S>S;9"S?S@\;55r>\"SAS;9"SBSC\;\55r?/SDQr@g)Fz!PyTorch Swinv2 Transformer model.N) dataclass)OptionalTupleUnion)Tensornn)ACT2FN)BackboneOutput)PreTrainedModel) find_pruneable_heads_and_indicesmeshgridprune_linear_layer) ModelOutputauto_docstringlogging torch_int) BackboneMixin) Swinv2Configc\rSrSr%SrSr\\R\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'S rg) Swinv2EncoderOutput*a6 Swinv2 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____firstlineno____doc__rrtorch FloatTensor__annotations__rrrr__static_attributes__rb/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/swinv2/modeling_swinv2.pyrr*s}26:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+