a €þd ã@sžddlZddlmZddlZddlmZddlZejddœdd„Zejdœdd „Z ej jdœd d „Z e dœd d „Z e dœdd„Zdaejdedœdd„ƒZdS)éN)Ú Generator)Údefault_generator)Ú new_stateÚreturncCst |¡dS)zýSets 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)rZ set_state)r©rúU/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torch/random.pyÚ set_rng_state s r)rcCst ¡S)zBReturns the random number generator state as a `torch.ByteTensor`.)rZ get_staterrrrÚ get_rng_statesr cCsNt|ƒ}ddl}|j ¡s&|j |¡ddl}|j ¡sD|j |¡t |¡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`. rN) ÚintÚ torch.cudaÚcudaÚ_is_in_bad_forkÚmanual_seed_allÚ torch.mpsÚmpsÚ manual_seedr©ÚseedÚtorchrrrrs     rcCsHt ¡}ddl}|j ¡s&|j |¡ddl}|j ¡sD|j |¡|S)zˆSets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG. rN) rrr r r rrrrrrrrr1s    rcCst ¡S)zSReturns the initial seed for generating random numbers as a Python `long`. )rÚ initial_seedrrrrrBsrFTÚfork_rngÚdevicesc csöddl}|sdVdS|dur\|j ¡}|dkrNtsNt dj|||d¡datt|ƒƒ}nt|ƒ}|  ¡}g}|D]}|  |j  |¡¡qtz6dVW|  |¡t ||ƒD]\}} |j  | |¡qªn.|  |¡t ||ƒD]\}} |j  | |¡qØ0dS)aë Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in. Args: devices (iterable of CUDA IDs): CUDA 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. rNéaôCUDA reports that you have {num_devices} available devices, and you have used {caller} without explicitly specifying which devices are being used. For safety, we initialize *every* CUDA device by default, which can be quite slow if you have a lot of GPUs. If you know that you are only making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES or the '{devices_kw}' keyword argument of {caller} with the set of devices you are actually using. For example, if you are using CPU only, set CUDA_VISIBLE_DEVICES= or devices=[]; if you are using GPU 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0]. To initialize all devices and suppress this warning, set the '{devices_kw}' keyword argument to `range(torch.cuda.device_count())`.)Ú num_devicesZcallerZ devices_kwT) r r Z device_countÚ_fork_rng_warned_alreadyÚwarningsÚwarnÚformatÚlistÚranger ÚappendrÚzip) rZenabledZ_callerZ _devices_kwrrZ cpu_rng_stateZgpu_rng_statesZdeviceZ gpu_rng_staterrrrLs6   õÿ  þ )NTrr)Ú contextlibÚtypingrrZtorch._CrrZTensorrr Z_Crr rrrÚcontextmanagerrrrrrÚs