#hddlZddlmZddlZddlmZddlZdejddfdZdejfdZ dej jfdZ de fd Z dd Zde fd Zd aejddefdZdS)N) Generator)default_generator new_statereturnc.tj|dS)zSets the random number generator state. .. note: This function only works for CPU. For CUDA, please use torch.manual_seed(seed), which works for both CPU and CUDA. Args: new_state (torch.ByteTensor): The desired state N)r set_state)rs L/var/www/html/auto_sub_bot/venv/lib/python3.11/site-packages/torch/random.py set_rng_stater s *****c(tjS)zBReturns the random number generator state as a `torch.ByteTensor`.)r get_stater r get_rng_staters  & ( ((r ct|}ddl}|js|j|ddl}|js|j|t|dr3|j s|j |t|tj|S)aSets the seed for generating random numbers. 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`. rNxpu) int torch.cudacuda_is_in_bad_forkmanual_seed_all torch.mpsmps manual_seedhasattrr_seed_custom_devicerseedtorchs r rrs t99D : % % ' ') ""4((( 9 $ $ & &$ d###ue(UY%>%>%@%@( !!$'''  ( . ..r ctj}ddl}|js|j|ddl}|js|j|t|dr3|j s|j |t||S)zSets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG. rNr) rrrrrrrrrrrrrs r rr6s  ! # #D : % % ' ') ""4((( 9 $ $ & &$ d###ue(UY%>%>%@%@( !!$''' Kr ct|}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_namergetattrwarningswarn UserWarning)rcustom_backend_namecustom_device_mod_bad_fork_name_seed_all_namemessages r rrLs" t99D(@@BBu)** >#E+>??** $n 5 5 >'BSUc:d:d >=7,n==?? A:)>::4@@@@@ A Am':lllG m>mm.mmH[mmm mG M';1 = = = = = = > >r c(tjS)zSReturns the initial seed for generating random numbers as a Python `long`. )r initial_seedrr r r0r0cs  ) + ++r FTfork_rngdevicesrc#.Ktj|j}tt|d}|t d|ddz|sdVdS||}|dkrt s|d|d|d|d |d |d |d |d |d|d|d|d}tj |datt|}nt|}tj }g} |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. deivce_type (str): device type str, default is `cuda`. As for custom device, see details in [Note: support the custom device with privateuse1] Nztorch 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(listrangerappendr zip) r2enabled_caller _devices_kw device_type device_mod num_devicesr. cpu_rng_statedevice_rng_statesr5device_rng_states r r1r1ms&,{++0K T22JZkZZZIJKK K   --// ??#;?%++-- G Gk G G(/ G GI=N=N=P=P G G0;/@/@/B/B G G &++-- G G IT G G ls G G*//11 G G?J G G(3 G G GG M' " " "'+ $u[))**w--'))MCC  !9!9&!A!ABBBB?  M***(+G5F(G(G ? ? $F$  $ $%5v > > > > ? ? M***(+G5F(G(G ? ? $F$  $ $%5v > > > > ?s GAH)rN)NTr1r2r) contextlibtypingrr'torch._CrrTensorr rr$rrrrr0r9contextmanagerr1rr r rMs=&&&&&&  +U\ +d + + + +)u|)))) /+////8c,>>>>.,c,,,,! A?A?ktA?A?A?A?A?A?r