1;jiZ dZddlmZmZmZmZddlmZddlm Z m Z e rddl Z ddl m Z mZmZmZejeZ ddlmZn#eef$rd ZYnwxYw d d eeefd eeed d fd eddeddedf dZdefdZdddededdfdZdedddddedfdZdS)!z1 PyTorch utilities: Utilities related to PyTorch )ListOptionalTupleUnion)logging)is_torch_availableis_torch_versionN)fftnfftshiftifftn ifftshift)allow_in_graphc|S)N)clss U/root/voice-cloning/.venv/lib/python3.11/site-packages/diffusers/utils/torch_utils.pymaybe_allow_in_graphr"s shape generatorztorch.Generatordevicez torch.devicedtypez torch.dtypelayoutz torch.layoutc .|d}p tj|ptjd}tts jjndjj}||jkr3|dkr-d|dkr$t d|d|d|dn'||jkr|d krtd |d |d ttrtd krdttrVdd dzfdt|D}tj |d |}n,tj  |}|S)zA helper function to create random tensors on the desired `device` with the desired `dtype`. When passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor is always created on the CPU. rcpuNmpszBThe passed generator was created on 'cpu' even though a tensor on zB was expected. Tensors will be created on 'cpu' and then moved to zk. Note that one can probably slighly speed up this function by passing a generator that was created on the z device.cudazCannot generate a z! tensor from a generator of type .r)rc Ng|]!}tj|"S)rrrr)torchrandn).0irrr rand_devicers r z randn_tensor..KsD    K1kQV_e f f f   rdimr!)r"stridedr isinstancelisttypeloggerinfo ValueErrorlenrangecattor#) rrrrr batch_sizegen_device_typelatentsr&s `` `` @r randn_tensorr8&sKqJ  $u}F  *u|E**F7A)T7R7Rp)*//XabcXdXkXp fk ) )o.F.FK wY_wwKQwwflwww  + +60I0Im&mm[jmmmnn n)T""!s9~~':':aL )T""uuQRRy         :&&   )G+++..v66+eyTYbhiiillmstt NrreturnctddsttdsdSt|tjjjS)z:Check whether the module was compiled with torch.compile()ros0///////////>>>>>>>>;LLL::::::::::::  H % %DDDDDDD()NR'+%)'+ --  -d#457HHIJ- ^ $- M " - ^ $ ----`H$HHHH++C++++++>,,(6,KY, )*,,,,,,sA AA