fThe SrSSKrSSKJr SSKJrJrJrJrJ r SSK r SSK r SSK J r SSK JrJrJr SSKJr SS KJrJrJrJrJr SS KJr SS KJrJrJr SS KJ r S SK!J"r" \RF"\$5r%\"SS\55r&\"SS\55r'\"SS\55r(\"SS\55r)"SS\ RT5r+"SS\ RT5r,"SS\ RT5r-SIS\ R\S\/S\0S \ R\4S!jjr1"S"S#\ RT5r2"S$S%\ RT5r3"S&S'\ RT5r4"S(S)\ RT5r5S*\ R\S+\\6S,\\6S \ R\4S-jr7"S.S/\ RT5r8S*\ R\S0\\6\64S1\\6\64S2\\\6S \ R\4 S3jr9\"S4S5\55r:"S6S7\ RT5r;\"S8S9\:55r<"S:S;\ RT5r="S<S=\ RT5r>\"S>S?9"S@SA\:55r?\"SBS?9"SCSD\:55r@\"SES?9"SFSG\:\ 55rA/SHQrBg)JzPyTorch Hiera model.N) dataclass)DictListOptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BackboneOutputBaseModelOutputBaseModelOutputWithPoolingImageClassifierOutput ModelOutput)PreTrainedModel)auto_docstringlogging torch_int) BackboneMixin) HieraConfigc\rSrSr%SrSr\\R\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'S rg) HieraEncoderOutput+a Hiera 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)`. Thesre are the unrolled hidden states of the model. 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, height, width, hidden_size)`. These are the reshaped and re-rolled hidden states of the model. 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____firstlineno____doc__rrtorch FloatTensor__annotations__rrr r!__static_attributes__r"`/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/hiera/modeling_hiera.pyrr+s}26:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+