jg!SrSSKrSSKrSSKJr SSKJr SSKJr SSK r SSK J s J r SSK J r SSKJr SS KJr SS KJr SS KJr SS KJrJr SS KJrJr SSKJr SSKJ r SSK!J"r"J#r#J$r$J%r%J&r&J'r' SSK(J)r) SSK*J+r+ SSK,J-r-J.r.J/r/ \&R`"\15r2Sr3Sr4Sr5S\ RlS\ Rl4Sjr7\$"SS9\"SS\"555r8\$"SS9\"S S!\"555r9\$\"S"S#\"555r:"S$S%\ Rv5r<"S&S'\ Rv5r="S(S)\ Rv5r>"S*S+\ Rv5r?"S,S-\ Rv5r@"S.S/\ Rv5rA"S0S1\ Rv5rB"S2S3\ Rv5rC"S4S5\ Rv5rD"S6S7\5rE"S8S9\ Rv5rF"S:S;\ Rv5rG"S<S=\ Rv5rH"S>S?\ Rv5rISfS@\ RvSA\ RlSB\ RlSC\ RlSD\ RlS-SE\JSF\J4SGjjrK"SHSI\ Rv5rL"SJSK\ Rv5rM"SLSM\ Rv5rN"SNSO\ Rv5rO"SPSQ\ Rv5rP"SRSS\5rQ"STSU\ Rv5rR"SVSW\ Rv5rS\$"SXSY\55rT"SZS[\T5rU\$"S\S9"S]S^\T55rV\$"S_S`\T55rW\$"SaSb\T55rX\$"ScSd\T55rY/SeQrZg)gzPyTorch CLAP model.N)Callable) dataclass)Any)nn)initialization)ACT2FN)create_bidirectional_mask)GradientCheckpointingLayer)BaseModelOutputBaseModelOutputWithPooling)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)apply_chunking_to_forward) ModelOutputTransformersKwargsauto_docstringcan_return_tuplelogging torch_int)merge_with_config_defaults)capture_outputs)ClapAudioConfig ClapConfigClapTextConfigcURup#nUSS2SS2SSS24RSSUS5nURX#U-U5nU$)aI Interpolate data in time domain. This is used to compensate the resolution reduction in downsampling of a CNN. Args: hidden_states (`torch.FloatTensor` of shape (batch_size, time_length, classes_num)): Input hidden states ratio (`int`): The ratio of the length of the output to the length of the input. Nr)shaperepeatreshape) hidden_statesratio batch_size time_length classes_num upsampleds ڴC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\transformers/models/clap/modeling_clap.py interpolater)/sT.;-@-@*ZkaD!m,33Aq%CI!!*E.A;OI cURup#pEURX#U-XU-X5nURSSSSSS5R5RSXU5nU$)a2 Returns the resized hidden states. The output shape should be `(batch_size * num_windows, window_size, window_size, num_channels)` Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, height, width, num_channels)`): Input hidden states window_size (`int`): Window size rrrrviewpermute contiguous)r" window_sizer$heightwidth num_channelswindowss r(window_partitionr9@so/<.A.A+J!&&k);8LkM##Aq!Q15@@BGGKfrsG Nr*cURSnURSX!-X1-XU5nURSSSSSS5R5RSX#U5nU$)a_ Merges windows to produce higher resolution features. Args: windows (`torch.FloatTensor` of shape `(num_windows * batch_size, window_size, window_size, num_channels)`): Input windows window_size (`int`): Window size height (`int`): Height of the resized audio width (`int`): Width of the resized audio r/rrrr,r-r.r0)r8r4r5r6r7s r(window_reverser;Use==$Lll2v4e6JKfrsGooaAq!Q/::<AA"fUabG Nr*logitsreturnc[R"[U5URS9n[R R X5$)Ndevice)torcharangelenr@r functional cross_entropy)r<labelss r(contrastive_lossrGjs/ \\#f+fmm