T^(h dZddlZddlZddlZddlmZddlmZm Z m Z ddl Z ddl Z ddl m Z mZddlmZmZmZddlmZdd lmZdd lmZdd lmZmZmZdd lmZmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&ddl'm(Z(e"jRe*Z+dZ,dZ-gdZ.dZ/dZ0eGddeZ1eGddeZ2eGddeZ3eGddeZ4dZ5dZ6dLde jde7d e8d!e jfd"Z9Gd#d$ejtZ;Gd%d&ejtZ<Gd'd(ejtZ=Gd)d*ejtZ>Gd+d,ejtZ?Gd-d.ejtZ@Gd/d0ejtZAGd1d2ejtZBGd3d4ejtZCGd5d6ejtZDGd7d8ejtZEGd9d:ejtZFGd;dZIe d?eHGd@dAeGZJe dBeHGdCdDeGZKe dEeHGdFdGeGZLe dHeHGdIdJeGe&ZMgdKZNy)Mz!PyTorch Swinv2 Transformer model.N) dataclass)OptionalTupleUnion)Tensornn)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) Swinv2Configrz(microsoft/swinv2-tiny-patch4-window8-256)r@iz Egyptian catceZdZUdZdZeejed<dZ ee ejdfed<dZ ee ejdfed<dZ ee ejdfed<y)Swinv2EncoderOutputa 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____doc__r rtorch FloatTensor__annotations__r!rr"r#h/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/swinv2/modeling_swinv2.pyrr>s}26:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+