T^(hl dZddlZddlZddlZddlmZddlmZm Z m Z ddl Z ddl Z ddl m Z ddlmZmZmZddlmZdd lmZdd lmZdd lmZmZmZdd lmZmZmZm Z m!Z!m"Z"m#Z#dd l$m%Z%ddl&m'Z'e!jPe)Z*dZ+dZ,gdZ-dZ.dZ/eGddeZ0eGddeZ1eGddeZ2eGddeZ3dZ4dZ5Gdde jlZ7Gd d!e jlZ8Gd"d#e jlZ9dMd$e jtd%e;d&eGd+d,e jlZ?Gd-d.e jlZ@Gd/d0e jlZAGd1d2e jlZBGd3d4e jlZCGd5d6e jlZDGd7d8e jlZEGd9d:e jlZFGd;dZIed?eHd@GdAdBeGZJedCeHGdDdEeGZKedFeHGdGdHeGZLedIeHGdJdKeGe%ZMgdLZNy)NzPyTorch Swin Transformer model.N) dataclass)OptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BackboneOutput)PreTrainedModel) find_pruneable_heads_and_indicesmeshgridprune_linear_layer) ModelOutputadd_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings torch_int) BackboneMixin) SwinConfigrzµsoft/swin-tiny-patch4-window7-224)r1iztabby, tabby catceZdZUdZdZeejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y)SwinEncoderOutputa Swin 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__rrtorch FloatTensor__annotations__r rr!r"d/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/swin/modeling_swin.pyrr>s}26:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+