tjk&<ddlZddlZddlmZddlmZddlZgdZerddlm Z ddl m Z dej ddfd Z dej fd Zdejjfd Zdejjfd Zdefd ZddZdefdZdaej ddefdZdejdzfdZdS)N) Generator) TYPE_CHECKING) set_rng_state get_rng_state manual_seedseed initial_seedfork_rngthread_safe_generator) WorkerInfo)default_generator new_statereturnc.tj|dS)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 J/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/random.pyrrs *****c(tjS)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_staterrrr's  & ( ((rc t|S)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)rs rrr1s T " ""rct|}ddl}|js|j|ddl}|js|j|ddl}|j s|j |ddl }|j s|j |t|tj|S)Nr)int torch.cudacuda_is_in_bad_forkmanual_seed_all torch.mpsmpsr torch.xpuxpu torch.mtiamtia_seed_custom_devicer rtorchs rrr>s  t99D : % % ' ') ""4((( 9 $ $ & &$ d### 9 $ $ & &( !!$''' : % % ' ') ""4(((  ( . ..rctj}ddl}|js|j|ddl}|js|j|ddl }|j s|j |ddl }|j s|j |t||S)zSets the seed for generating random numbers to a non-deterministic random number on all devices. Returns a 64 bit number used to seed the RNG. rN)r rrrrrrr rr!r"r#r$r%r&s rrrYs  ! # #D : % % ' ') ""4((( 9 $ $ & &$ d### 9 $ $ & &( !!$''' : % % ' ') ""4((( Krct|}tj}t t|rt t|}d}d}t ||rEt ||r5t ||st |||d Sd Sd|d}|d|d|d|dz }t j|td d Sd S) 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 rr%r%ws' t99D(@@BBu)** >#E+>??** $n 5 5 >' ~; ;  >>7,n==?? A:)>::4@@@@@ A Am':lllG m>mm.mmH[mmm mG M';1 = = = = = = > >rc(tjS)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 r rrrr r s  ) + ++rFTr devicesc#* K|dkrdVdStj}|p ||jnd}tj| t d|ddz|sdVdS| }|dkrts|d|d |d |d |d |d |d|d|d|d|d|d}tj |ddatt|}nt|}tj } fd|D} dVtj|t|| D]\} } | | dS#tj|t|| D]\} } | | wxYw)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` metaNrztorch 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())`.r,Tc:g|]}|Sr)r).0device device_mods r zfork_rng..s'PPPf11&99PPPr)r' acceleratorcurrent_acceleratortypeget_device_module RuntimeError device_count_fork_rng_warned_alreadyupperr2r3listrangerrzip) r;enabled_caller _devices_kw device_typeacc num_devicesr9 cpu_rng_statedevice_rng_statesrBdevice_rng_staterCs @rr r s6f    / / 1 1CJco#((6K(55J K{ K K K< =      --// ??#;?$$&& @ @{ @ @!( @ @5@5F5F5H5H @ @7B6G6G6I6I @ @)4(9(9(;(; @ @ $$&& @ @ BM @ @ el @ @#((** @ @8C @ @!, @ @ @  M'a 0 0 0 0'+ $u[))**w--'))MPPPPPPP?  M***(+G5F(G(G ? ? $F$  $ $%5v > > > > ? ? M***(+G5F(G(G ? ? $F$  $ $%5v > > > > ?s GAHcfddlm}|}||jdkr|j |jjSdS)asReturns 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]:] r)get_worker_infoNthread)torch.utils.datarZ worker_methodrngtorch_generator)rZ worker_infos rr r sO0100000%4_%6%6K  % 1 1 O '.. 4r)rN)NTr r;N) contextlibr2collections.abcrtypingrr'__all__torch.utils.data._utils.workerr torch._Cr Tensorrrr.rrrrr%r rKcontextmanagerr r rrrris%%%%%%    :999999&&&&&& +U\ +d + + + +)u|)))) #+ # # # #/ux1////6c<>>>>2,c,,,,!     R?R?  R?R?R?R?j!u5!!!!!!r