[Th,SSKrSSKrSSKJr SSKrSSKJr S\RSS4SjrS\R4Sjr S\RR4Sjr S\ 4S jr SS jrS\ 4S jrS q\R"SS\4S jj5rg)N) Generator)default_generator new_statereturnc0[R"U5 g)zSets the random number generator state. .. note:: This function only works for CPU. For CUDA, please use :func:`torch.manual_seed`, which works for both CPU and CUDA. Args: new_state (torch.ByteTensor): The desired state N)r set_state)rs D/var/www/auris/envauris/lib/python3.13/site-packages/torch/random.py set_rng_stater s *c,[R"5$)zReturns the random number generator state as a `torch.ByteTensor`. .. note:: The returned state is for the default generator on CPU only. See also: :func:`torch.random.fork_rng`. )r get_stater r get_rng_staters  & & ((r c[U5nSSKnURR5(dURR U5 SSKnUR R5(dUR RU5 SSKnURR5(dURR U5 [U5 [R"U5$)aSets the seed for generating random numbers on all devices. Returns a `torch.Generator` object. Args: seed (int): The desired seed. Value must be within the inclusive range `[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError is raised. Negative inputs are remapped to positive values with the formula `0xffff_ffff_ffff_ffff + seed`. rN) int torch.cudacuda_is_in_bad_forkmanual_seed_all torch.mpsmps manual_seed torch.xpuxpu_seed_custom_devicerseedtorchs r rr s t9D :: % % ' ' ""4( 99 $ $ & & d# 99 $ $ & & !!$'  ( ( ..r c[R"5nSSKnURR 5(dURR U5 SSKnURR 5(dURRU5 SSK nURR 5(dURR U5 [U5 U$)zSets the seed for generating random numbers to a non-deterministic random number on all devices. Returns a 64 bit number used to seed the RNG. rN) rrrrrrrrrrrrrs r rr?s  ! ! #D :: % % ' ' ""4( 99 $ $ & & d# 99 $ $ & & !!$' Kr c[U5n[RR5n[ [U5(a[ [U5nSnSn[ X#5(a8[ X$5(a([ X#5"5(d[ X$5"U5 g g SUS3nUSUSUSUS3- n[ R"U[S S 9 g g ) zSets the seed to generate random numbers for custom device. Args: seed (int): The desired seed. See [Note: support the custom device with privateuse1] rrzSet seed for `z0` device does not take effect, please add API's `z` and `z` to `z` device module.) stacklevelN) rr_C_get_privateuse1_backend_namehasattrgetattrwarningswarn UserWarning)rcustom_backend_namecustom_device_mod_bad_fork_name_seed_all_namemessages r rrXs t9D((@@Bu)**#E+>?** $ 5 5' ; ; ,=??):4@@'':&;;klG >*'.1AH[G\\lm mG MM';1 =+r c,[R"5$)zReturns the initial seed for generating random numbers as a Python `long`. .. note:: The returned seed is for the default generator on CPU only. )r initial_seedrr r r1r1qs  ) ) ++r Fc## US:XaSv g[R"U5Rn[[US5nUc[ SUS3S-5eU(dSv gUcUR 5nUS:a[ (dUR5SUSUS UR5S UR5S UR5S UR5S USUSUR5SUSUS3n[R"U5 Sq[[U55nO [U5n[R"5nUV s/sHoRU 5PM n n Sv [R"U5 [X 5HupURX5 M gs sn f![R"U5 [X 5HupURX5 M f=f7f)aQ Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in. Args: devices (iterable of Device IDs): devices for which to fork the RNG. CPU RNG state is always forked. By default, :meth:`fork_rng` operates on all devices, but will emit a warning if your machine has a lot of devices, since this function will run very slowly in that case. If you explicitly specify devices, this warning will be suppressed enabled (bool): if ``False``, the RNG is not forked. This is a convenience argument for easily disabling the context manager without having to delete it and unindent your Python code under it. device_type (str): device type str, default is `cuda`. As for custom device, see details in [Note: support the custom device with privateuse1] metaNztorch has no module of `z`, you should register z,a module by `torch._register_device_module`.z reports that you have z& available devices, and you have used z_ without explicitly specifying which devices are being used. For safety, we initialize *every* zA device by default, which can be quite slow if you have a lot of z5s. If you know that you are only making use of a few z' devices, set the environment variable z_VISIBLE_DEVICES or the 'z' keyword argument of z with the set of devices you are actually using. For example, if you are using CPU only, set device.upper()_VISIBLE_DEVICES= or devices=[]; if you are using device 0 only, set zb_VISIBLE_DEVICES=0 or devices=[0]. To initialize all devices and suppress this warning, set the 'z#' keyword argument to `range(torch.z.device_count())`.T)rdevicetyper' RuntimeError device_count_fork_rng_warned_alreadyupperr(r)listrangerr zip) devicesenabled_caller _devices_kw device_type device_mod num_devicesr/ cpu_rng_stater5device_rng_statesdevice_rng_states r fork_rngrH}s2f ,,{+00K T2J&{m3J K< =     --/ ?#;#;$$&''>{mL!!( *55@5F5F5H4IJ66A6G6G6I5JK((3(9(9(;'< @  MM' "'+ $u[)*w-'')MHOPf11&9P?  M*(+G(G $F  $ $%5 >)H Q M*(+G(G $F  $ $%5 >)Hs+E G1F,)G1,F10AG11=G..G1)rN)NTrHr>r) contextlibr(collections.abcrrtorch._CrTensorr rr$rrrrr1r9contextmanagerrHrr r rNs% & +U\\ +d +)u||)/++/>c2>2,c,!     M?  M?M?r