g 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"j)e*Z+dZ,dZ-gdZ.dZ/dZ0eGddeZ1eGddeZ2eGddeZ3eGddeZ4dZ5dZ6dMd e j d!e7d"e8d#e j fd$Z9Gd%d&ej:Z;Gd'd(ej:Z<Gd)d*ej:Z=Gd+d,ej:Z>Gd-d.ej:Z?Gd/d0ej:Z@Gd1d2ej:ZAGd3d4ej:ZBGd5d6ej:ZCGd7d8ej:ZDGd9d:ej:ZEGd;deZGd?ZHd@ZIe dAeHGdBdCeGZJe dDeHGdEdFeGZKe dGeHGdHdIeGZLe dJeHGdKdLeGe&ZMdS)Nz!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 catceZdZUdZdZejed<dZe e ejdfed<dZ e e ejdfed<dZ e e ejdfed<dS)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 torch FloatTensor__annotations__r!rrr"r#f/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/swinv2/modeling_swinv2.pyrr>s2,0u(///=AM8E%"3S"89:AAA:>Ju0#567>>>FJHU5+