ZjvSSKrSSKJr SSKJr SSKrSSKJr SSKJs J r SSKJ r SSK J r SSKJr SSKJr SS KJrJr SS KJr SS KJr SS KJrJr SS KJr SSKJ r SSK!J"r"J#r#J$r$J%r% SSK&J'r'J(r( SSK)J*r*J+r+ SSK,J-r- \#"SS9\"SS\555r.\#"SS9\"SS\555r/\\#"SS9"SS\555r0\"S5"SS \Rb55r2"S!S"\Rb5r3"S#S$\Rb5r4"S%S&\Rb5r5SwS'\ S(\ S)\ S*\ S+\6\7S,\ 4 S-jjr8"S.S/\Rb5r9"S0S1\Rb5r:"S2S3\Rb5r;"S4S5\Rb5r<"S6S7\Rb5r="S8S9\Rb5r>SxS:\RbS;\RS<\RS'\RS=\RS-S>\?S-S?\?S@\\"4SAjjr@"SBSC\Rb5rA"SDSE\Rb5rB"SFSG\Rb5rC"SHSI\Rb5rD"SJSK\Rb5rE"SLSM\Rb5rFSNrG"SOSP\Rb5rH"SQSR\Rb5rI"SSST\Rb5rJ"SUSV\Rb5rK"SWSX\Rb5rL\#"SYSZ\55rM"S[S\\M5rNSyS]\RS^\RS_\OS,\R4S`jjrP"SaSb\M5rQSzScjrRSd\7Se\RSf\7S,\R4SgjrSSh\RSf\?S,\R4SijrT"SjSk\M5rUS{SljrV\#"SmS9"SnSo\M55rW\#"SpS9\"SqSr\555rX\#"SsS9"StSu\M55rY/SvQrZg)|N)Callable) dataclass)Tensor)initialization)ACT2CLS) load_backbone)center_to_corners_formatcorners_to_center_format)use_kernel_forward_from_hub) ModelOutput)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)#compile_compatible_method_lru_cache)TransformersKwargsauto_docstringtorch_compilable_check torch_int)can_return_tuplemerge_with_config_defaults)OutputRecordercapture_outputs) Deimv2Configa& Base class for outputs of the Deimv2Decoder. This class adds two attributes to BaseModelOutputWithCrossAttentions, namely: - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) - a stacked tensor of intermediate reference points. ) custom_introc\rSrSr%SrSr\RS-\S'Sr \RS-\S'Sr \RS-\S'Sr \RS-\S'Sr \RS-\S'Sr \RS-\S 'Sr\\RS-\S 'Sr\\RS-\S 'Sr\\RS-\S 'S rg)Deimv2DecoderOutput-a intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): Stacked intermediate hidden states (output of each layer of the decoder). intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): Stacked intermediate logits (logits of each layer of the decoder). intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): Stacked intermediate reference points (reference points of each layer of the decoder). intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): Stacked intermediate predicted corners (predicted corners of each layer of the decoder). initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): Stacked initial reference points (initial reference points of each layer of the decoder). cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. Nlast_hidden_stateintermediate_hidden_statesintermediate_logitsintermediate_reference_pointsintermediate_predicted_cornersinitial_reference_points hidden_states attentionscross_attentions)__name__ __module__ __qualname____firstlineno____doc__r torch FloatTensor__annotations__r!r"r#r$r%r&tupler'r(__static_attributes__r){/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/deimv2/modeling_deimv2.pyrr-s"37u((4/6;? 1 1D 8?48**T18>B!5#4#4t#;B?C"E$5$5$<C9=e//$6=59M5**+d2926Je''(4/68<eE--.5