cCix SrSSKrSSKrSSKJr SSKJrJr SSK r SSK J r SSK J r SSK Jr SS KJr SS KJrJrJr SS KJrJrJrJr S S KJr \R8"\5r\\"SS9"SS\555r\\"SS9"SS\555r \\"SS9"SS\555r!Sr"Sr#"SS\ RH5r%"SS\ RH5r&"SS\ RH5r'S?S \ RPS!\)S"\*S#\ RP4S$jjr+"S%S&\ RH5r,"S'S(\ RH5r-"S)S*\ RH5r."S+S,\ RH5r/"S-S.\ RH5r0"S/S0\ RH5r1"S1S2\ RH5r2"S3S4\5r3"S5S6\ RH5r4\"S7S8\55r5\"S9S:\555r6\"S;S9"S<S=\555r7/S>Qr8g)@zPyTorch Donut Swin Transformer model. This implementation is identical to a regular Swin Transformer, without final layer norm on top of the final hidden states.N) dataclass)OptionalUnion)nn)ACT2FN)GradientCheckpointingLayer)PreTrainedModel) find_pruneable_heads_and_indicesmeshgridprune_linear_layer) ModelOutputauto_docstringlogging torch_int)DonutSwinConfigzS DonutSwin encoder's outputs, with potential hidden states and attentions. ) custom_introc\rSrSr%SrSr\\R\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'S rg) DonutSwinEncoderOutput'a 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__rtuplerr__static_attributes__rg/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/donut/modeling_donut_swin.pyrr's}6:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+