U ,-e2@sddlZddlmZddlZddlmZddlZejddddZejddd Z ej jdd d Z e dd d Z ddddZe 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)rZ set_state)rrM/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/torch/random.py set_rng_state s r)rcCstS)zBReturns the random number generator state as a `torch.ByteTensor`.)rZ get_staterrrr get_rng_statesr cCsVt|}ddl}|js&|j|ddl}|jsD|j|t|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_all torch.mpsmps manual_seed_seed_custom_devicerseedtorchrrrrs     rcCsPt}ddl}|js&|j|ddl}|jsD|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. rN) rrr r r rrrrrrrrrr3s    rcCst|}tj}tt|rtt|}d}d}t||r^t||r^t||st|||n8d|d}|d|d|d|d7}tj|td d d 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] r rzSet seed for `z0` device does not take effect, please add API's `z` and `z` to `z` device module.) stacklevelN) r r_CZ_get_privateuse1_backend_namehasattrgetattrwarningswarn UserWarning)rZcustom_backend_nameZcustom_device_modZ_bad_fork_nameZ_seed_all_namemessagerrrrFs     rcCstS)zSReturns the initial seed for generating random numbers as a Python `long`. )r initial_seedrrrrr ]sr FTfork_rngdevicesr c csJt|j}tt|d}|dkr4td|dd|sBdVdS|dkr|}|dkrts|d|d|d|d |d |d |d |d |d|d|d|d}t |dat t |}nt |}t }g} |D]} | | | qz dVW5t|t|| D]\} } || | q,XdS)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 RuntimeErrorZ device_count_fork_rng_warned_alreadyupperrrlistranger appendrzip) r"enabledZ_callerZ _devices_kwZ device_typeZ device_modZ num_devicesrZ cpu_rng_stateZdevice_rng_statesr$Zdevice_rng_staterrrr!gs4    d   )NTr!r"r ) contextlibtypingrrZtorch._CrrZTensorrr rrr rrr r'contextmanagerr!rrrrs