:ju 8ddlZddlZddlZddlmZddlmZddlmZm Z ddl Z ddl m Z ddl mZmZmZmZej$eZdZdZdde ed ed ed efd Zdd Zddede ed dfdZdedededed eef dZdded eeeffdZ deeefd dfdZ!y)N)getstate)setstate)AnyOptional)_NUMPY_AVAILABLE) _get_rankrank_prefixed_messagerank_zero_onlyrank_zero_warnlseedworkersverbosereturnc|Atjjd}|d}|r7td|n( t |}nt|ts t |}t|cxkr tksnt |dtdt|r*tjtd|tt|tjd<tj |t"rddl}|jj!|t'j(|t |tjd <|S#t $rt dt |wxYw) anFunction that sets the seed for pseudo-random number generators in: torch, numpy, and Python's random module. In addition, sets the following environment variables: - ``PL_GLOBAL_SEED``: will be passed to spawned subprocesses (e.g. ddp_spawn backend). - ``PL_SEED_WORKERS``: (optional) is set to 1 if ``workers=True``. Args: seed: the integer value seed for global random state in Lightning. If ``None``, it will read the seed from ``PL_GLOBAL_SEED`` env variable. If ``None`` and the ``PL_GLOBAL_SEED`` env variable is not set, then the seed defaults to 0. If seed is not in bounds or cannot be cast to int, a ValueError is raised. workers: if set to ``True``, will properly configure all dataloaders passed to the Trainer with a ``worker_init_fn``. If the user already provides such a function for their dataloaders, setting this argument will have no influence. See also: :func:`~lightning_fabric.utilities.seed.pl_worker_init_function`. verbose: Whether to print a message on each rank with the seed being set. NPL_GLOBAL_SEEDrzNo seed found, seed set to z+Invalid seed specified via PL_GLOBAL_SEED: z& is not in bounds, numpy accepts from z to z Seed set to PL_SEED_WORKERS)osenvirongetr int ValueErrorrepr isinstancemin_seed_valuemax_seed_valueloginfor rstrrandomr rnumpytorch manual_seed)r r renv_seednps t/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/lightning_fabric/utilities/seed.pyseed_everythingr&s5& |::>>"23  D!>*D 1D |jjnn.4GCItCL'95I worker_idrankc||ntj}tj}||z }tj d|d|d|t |||d}tj|dtj|dd z|d ztrOddl }|jj|||g}|jd}|jj|yy) a.The worker_init_fn that Lightning automatically adds to your dataloader if you previously set the seed with ``seed_everything(seed, workers=True)``. See also the PyTorch documentation on `randomness in DataLoaders `_. Nz1Initializing random number generators of process z worker z with base seed )countr )r r-r! initial_seedrdebug_generate_seed_sequencer"rr rr SeedSequencegenerate_state) r,r- global_rank process_seed base_seed seed_sequencer$ss np_rng_seeds r%pl_worker_init_functionr?Us*$0C0CK%%'Ly(III ;K=QZP[[klukvw,Iy+UVWM mA&' KKq!R'=+;;< YY # #Y ;$G H''*  {# r+r;r9r0c|dz|dzz|z}||dz z}|dzdz}||dz z}|dzdz}||dz z}g}t|D]}|dzdzdz}|j| |S) zGenerates a sequence of seeds from a base seed, worker id and rank using hash-based mixing followed by the linear congruential generator (LCG) algorithm.r2!l Z_?zllSl 5gu& l-*PBr1)rangeappend)r;r,r9r0 combined_seedseeds_s r%r6r6qs"_b9KGM]b((M"%77MJM]b((M"%77MJM]b((M E 5\$&)<rhs 33 ?qqg!1(3-11PT1`c1h J$s$(3-$4$8ssUX]abe]f, d d38n  9DcN 9t 9r+