sg2dZddlmZddlmZmZddlZddlmZddlm Z ddl m Z m Z m Z mZdd lmZd d lmZd ZeGd de ZGdde ZGddej,ZGddej,ZGddej,ZGddej,ZGddej,ZdZdZe deGddeZy) zPyTorch ViTMatte model.) dataclass)OptionalTupleN)nn)PreTrainedModel) ModelOutputadd_start_docstrings%add_start_docstrings_to_model_forwardreplace_return_docstrings) load_backbone)VitMatteConfigrceZdZUdZdZeejed<dZ ejed<dZ ee ejed<dZ ee ejed<y)ImageMattingOutputa Class for outputs of image matting models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Loss. alphas (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Estimated alpha values. 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, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage. 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 layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nlossalphas hidden_states attentions) __name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rrrra/var/www/html/venv/lib/python3.12/site-packages/transformers/models/vitmatte/modeling_vitmatte.pyrr&sb()-D(5$$ %, $FE  $8