+ jk&^RIt^RIt^RIHt^RIHt^RIt.ROt]'d^RIH t ^RI H t RRlt RR lt R R ltR R ltRRltRRltRRltRs]P(RRRll4tRRltR#)N Generator) TYPE_CHECKING) WorkerInfo)default_generatorc<V^8dQhR\PRR/#) new_statereturnNtorchTensor)formats"M/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/random.py __annotate__rs + +U\\ +d +c2\P!V4R#)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)r s&r set_rng_staters *rc8V^8dQhR\P/#r r r )rs"rrr's))u||)rc,\P!4#)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_staterr get_rng_stater's  & & ((rcLV^8dQhR\PP/#rr _Cr)rs"rrr1s # #++ #rc\V4#)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`. )_manual_seed_impl)seeds&r manual_seedr"1s T ""rcLV^8dQhR\PP/#rr)rs"rrr>s//uxx11/rcR\V4p^RIpVPP4'gVPP V4^RIpVP P4'gVP PV4^RIpVPP4'gVPP V4^RI pVPP4'gVPP V4\V4\P!V4#)rN)int torch.cudacuda_is_in_bad_forkmanual_seed_all torch.mpsmpsr" torch.xpuxpu torch.mtiamtia_seed_custom_devicerr!r s& rr r >s t9D :: % % ' ' ""4( 99 $ $ & & d# 99 $ $ & & !!$' :: % % ' ' ""4(  ( ( ..rc$V^8dQhR\/#rr%)rs"rrrYscrc>\P!4p^RIpVPP 4'gVPP V4^RIpVPP 4'gVPPV4^RI pVPP 4'gVPP V4^RI pVPP 4'gVPP V4\V4V#)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. N)rr!r&r'r(r)r*r+r"r,r-r.r/r0r1s rr!r!Ys  ! ! #D :: % % ' ' ""4( 99 $ $ & & d# 99 $ $ & & !!$' :: % % ' ' ""4( KrcV^8dQhRR/#r r Nr)rs"rrrws>>>rc\V4p\PP4p\ \V4'd\ \V4pRpRp\ W#4'd=\ W$4'd,\ W#4!4'g\ W$4!V4R #R #RV R2pVRV RV RV R2, p\ P!V\^R 7R #R #) zSets the seed to generate random numbers for custom device. Args: seed (int): The desired seed. See [Note: support the custom device with privateuse1] r(r)zSet seed for `z0` device does not take effect, please add API's `z` and `z` to `z` device module. stacklevelN) r%r r_get_privateuse1_backend_namehasattrgetattrwarningswarn UserWarning)r!custom_backend_namecustom_device_mod_bad_fork_name_seed_all_namemessages& rr0r0ws t9D((@@Bu)**#E+>?** $ 5 5' ; ; ,=??):4@@'':&;;klG >*'.1AH[G\\lm mG MM';1 =+rc$V^8dQhR\/#rr3)rs"rrrs,,c,rc,\P!4#)zReturns the initial seed for generating random numbers as a Python `long`. .. note:: The returned seed is for the default generator on CPU only. )r initial_seedrrrrHrHs  ) ) ++rFc$V^8dQhR\/#rr)rs"rrrsR?R?  R?rc#@"VR8XdRxR#\PP4pT;'gVe VPMRp\P!V4pVf\ RV R2R,4hV'gRxR#VfVP 4pV^8d\'gVP4 RV RV R VP4 R VP4 R VP4 R VP4 R V RV RVP4 RV RV R2p\P!V^R7Rs\\V44pM \V4p\P!4p VU u.uFqPV 4NK p p Rx\P!V 4\W 4FwrVPW4K R#uup i \P!T 4\Y 4FwrTPY4K i;i5i)a 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 ``None``, in which case the type is taken from :func:`torch.accelerator.current_accelerator`, falling back to ``"cuda"`` when the type cannot be determined. As for supported devices, see details in :ref:`accelerator` metaNr'ztorch 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())`.r9T)r acceleratorcurrent_acceleratortypeget_device_module RuntimeError device_count_fork_rng_warned_alreadyupperr>r?listrangerrzip) devicesenabled_caller _devices_kw device_typeacc device_mod num_devicesrE cpu_rng_statedevicedevice_rng_statesdevice_rng_states &&&&& rfork_rngrcs+6f     / / 1CJJco#((6K((5J&{m3J K< =     --/ ?#;#;$$&''>{mL!!( *55@5F5F5H4IJ66A6G6G6I5JK((3(9(9(;'< @  MM'a 0'+ $u[)*w-'')MHOPf11&9P?  M*(+G(G $F  $ $%5 >)H Q M*(+G(G $F  $ $%5 >)Hs=4HA H,H/C H<GHGAH=HHcFV^8dQhR\PR,/#r6)r r)rs"rrrs!!u5!rc^RIHpV!4pVe6VPR8Xd%VPeVPP#R#)a'Returns a thread-safe random number generator for use in DataLoader workers. This function provides a convenient way for transforms and user code to use thread-safe random number generation without manually checking worker context. When called in a DataLoader thread worker, returns the worker's thread-local :class:`torch.Generator`. When called in the main process or process workers, returns ``None`` (which causes PyTorch functions to use the default global RNG). Returns: Optional[torch.Generator]: Thread-local generator in thread workers, None otherwise. Example:: >>> from torch.random import thread_safe_generator >>> generator = thread_safe_generator() >>> torch.randint(0, 10, (5,), generator=generator) Example with transforms:: >>> from torch.random import thread_safe_generator >>> class MyRandomTransform: ... def __call__(self, img): ... generator = thread_safe_generator() ... offset = torch.randint(0, 10, (2,), generator=generator) ... return img[..., offset[0]:, offset[1]:] )get_worker_infoNthread)torch.utils.datarf worker_methodrngtorch_generator)rf worker_infos rthread_safe_generatorrms@01%4%6K  % % 1 OO '... r)rrr"r!rHrcrm)NTrcrWN) contextlibr>collections.abcrtypingrr __all__torch.utils.data._utils.workerrtorch._Crrrr"r r!r0rHrRcontextmanagerrcrmrrrruss%  9& +) #/6<>2,! R?R?j!r