a 7Sic@sddlZddlmZddlZddlmZddlZejddddZejddd Z ej jdd d Z e dd d Z e dddZdaejdedddZdS)N) Generator)default_generator) new_statereturncCst|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)rrH/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/random.py set_rng_state s r )rcCstS)zBReturns the random number generator state as a `torch.ByteTensor`.)r get_staterrrr get_rng_statesr cCs0t|}ddl}|js&|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)int torch.cudacuda_is_in_bad_forkmanual_seed_allr manual_seedseedtorchrrrrs   rcCs*t}ddl}|js&|j||S)zSets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG. rN)rrr rrrrrrrr-s   rcCstS)zSReturns the initial seed for generating random numbers as a Python `long`. )r initial_seedrrrrr:srFTfork_rngdevicesc csddl}|sdVdS|dur\|j}|dkrNtsNtdj|||ddatt|}nt|}| }g}|D]}| |j |qtz6dVW| |t ||D]\}} |j | |qn.| |t ||D]\}} |j | |q0dS)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 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. rNaCUDA 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())`.) num_devicesZcallerZ devices_kwT) r r device_count_fork_rng_warned_alreadywarningswarnformatlistranger appendr zip) renabledZ_callerZ _devices_kwrrZ cpu_rng_stateZgpu_rng_statesdeviceZ gpu_rng_staterrrrDs6      )NTrr) contextlibtypingrrtorch._CrrTensorr r _Crr rrrcontextmanagerrrrrrs