bi_ dZddlZddlmZmZmZmZddlmZddl m Z m Z m Z e rddl Z ddlmZmZmZmZej&eZ ddlmZ d&d eeefd eeed d fd eeedfdeddedf dZdefdZdZddde de ddfdZ!de dddddedfd Z"d!Z#ejHd"Z%d'd#eefd$Z&d'd#eefd%Z'y#eef$rd ZYwxYw)(z1 PyTorch utilities: Utilities related to PyTorch N)ListOptionalTupleUnion)logging)is_torch_availableis_torch_npu_availableis_torch_version)fftnfftshiftifftn ifftshift)allow_in_graphc|SN)clss V/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/utils/torch_utils.pymaybe_allow_in_graphr#s shape generatorztorch.Generatordevicez torch.devicedtypez torch.dtypelayoutz torch.layoutc t|trtj|}|}|d}|xstj}|xstjd}|t|t s|jj n|djj }||j k7r,|dk(r'd}|dk7rFtjd|d|d|dn&||j k7r|d k(rtd |d |d t|t rt|d k(r|d}t|t rcd|d dz}t|Dcgc]}tj||||||!} }tj| dj|} | Stj|||||j|} | Scc}w)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 zl. Note that one can probably slightly 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)r)rrrrdim) isinstancestrtorchrstridedlisttypeloggerinfo ValueErrorlenrangerandncatto) rrrrr rand_device batch_sizegen_device_typeilatentss r randn_tensorr7's&#f%KqJ  $u}}F  *u||E*F7A)T7R)**//XabcXdXkXkXpXp fkk )o.FK XY_X`aKKQ(Sggmfnnvx  +60I1&9Z[jZkklmn n)T"s9~':aL )T"uQRy :&  KK1kQV_e f  ))G+..v6 N++eyTYbhillmst N s$F=returnctddsttdsyt|tjj j S)z:Check whether the module was compiled with torch.compile()rs//VV::   H %DNR37%)'+ /  /d#457HHIJ/ U3./ 0/ M " / ^ $ /dH$HF ++C+++D,,(6,KY, )*,6   HSMHSME ()s C CC