rh dZddlZddlZddlmZddlmZmZddl Z ddl Z ddl m Z ddl m Z ddlmZdd lmZdd lmZmZmZdd lmZmZmZmZd d lmZej:eZeedGddeZ eedGddeZ!eedGddeZ"dZ#dZ$Gdde jJZ&Gdde jJZ'Gdde jJZ(d?d e jRd!e*d"e+d#e jRfd$Z,Gd%d&e jJZ-Gd'd(e jJZ.Gd)d*e jJZ/Gd+d,e jJZ0Gd-d.e jJZ1Gd/d0e jJZ2Gd1d2e jJZ3Gd3d4eZ4Gd5d6e jJZ5eGd7d8eZ6eGd9d:e6Z7ed;Gd<d=e6Z8gd>Z9y)@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_introceZdZUdZdZeejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y)DonutSwinEncoderOutputa 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__rrtorch FloatTensor__annotations__rtuplerr/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/donut/modeling_donut_swin.pyrr(s}6:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+