+ &j3^RIt^RIHt^RIHt^RIt^RIHt^RI H t ^RI H t R.t ]!RRR 7t!R R] ],4tR#) N)Iterator)TypeVar)Dataset)SamplerDistributedSampler_T_coT) covariantcpa]tRt^toRtR V3RlRlltV3RlRltV3RlRltV3R lR ltR t Vt R#) raSampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the original dataset that is exclusive to it. .. note:: Dataset is assumed to be of constant size and that any instance of it always returns the same elements in the same order. Args: dataset: Dataset used for sampling. num_replicas (int, optional): Number of processes participating in distributed training. By default, :attr:`world_size` is retrieved from the current distributed group. rank (int, optional): Rank of the current process within :attr:`num_replicas`. By default, :attr:`rank` is retrieved from the current distributed group. shuffle (bool, optional): If ``True`` (default), sampler will shuffle the indices. seed (int, optional): random seed used to shuffle the sampler if :attr:`shuffle=True`. This number should be identical across all processes in the distributed group. Default: ``0``. drop_last (bool, optional): if ``True``, then the sampler will drop the tail of the data to make it evenly divisible across the number of replicas. If ``False``, the sampler will add extra indices to make the data evenly divisible across the replicas. Default: ``False``. .. warning:: In distributed mode, calling the :meth:`set_epoch` method at the beginning of each epoch **before** creating the :class:`DataLoader` iterator is necessary to make shuffling work properly across multiple epochs. Otherwise, the same ordering will be always used. Example:: >>> # xdoctest: +SKIP >>> sampler = DistributedSampler(dataset) if is_distributed else None >>> loader = DataLoader(dataset, shuffle=(sampler is None), ... sampler=sampler) >>> for epoch in range(start_epoch, n_epochs): ... if is_distributed: ... sampler.set_epoch(epoch) ... train(loader) Nc^<V^8dQhRS[RS[R,RS[R,RS[RS[RS[RR/#) dataset num_replicasNrankshuffleseed drop_lastreturn)rintbool)format __classdict__s"t/Users/jameslopez/projects/CWCArchive/cwc-podcast/.venv/lib/python3.14/site-packages/torch/utils/data/distributed.py __annotate__DistributedSampler.__annotate__BsY'''Dj'Dj '  '  '' 'cVf<\P!4'g \R4h\P!4pVf<\P!4'g \R4h\P!4pW28gV^8d\ RV RV^, R24hWnW nW0n^Vn W`n VP'd}\VP 4VP,^8wdR\P!\VP 4VP, VP, 4VnM?\P!\VP 4VP, 4VnVPVP,VnW@nWPnR#)Nz,Requires distributed package to be availablez Invalid rank z%, rank should be in the interval [0, ])dist is_available RuntimeErrorget_world_sizeget_rank ValueErrorr rrepochrlenmathceil num_samples total_sizerr)selfr rrrrrs&&&&&&&r__init__DistributedSampler.__init__BsT  $$&&"#QRR..0L <$$&&"#QRR==?D  4!8v%J>>c$,,/$2C2CCqH $yyT\\"T%6%66$:K:KK D  $yyT\\):T=N=N)NOD **T->->>  rc0<V^8dQhRS[S[,/#r r)rr)rrs"rrrks""(5/"rcVP'd|\P!4pVPVPVP ,4\P !\VP4VR7P4pM'\\\VP444pVP'gpVP\V4, pV\V48:d W"RV, pMFW"\P!V\V4, 4,RV, pMVRVPp\V4VP8wd&\!R\V4 RVP R24hW P"VPVP$1,p\V4VP&8wd&\!R\V4 RVP& R24h\)V4#)) generatorNzNumber of indices (z) does not match total_size ()zNumber of subsampled indices (z) does not match num_samples ()rtorch Generator manual_seedrr$randpermr%r tolistlistrangerr)r&r'AssertionErrorrrr(iter)r*gindices padding_sizes& r__iter__DistributedSampler.__iter__ks <<<!A MM$))djj0 1nnS%6!DKKMG5T\\!234G~~~??S\9Ls7|+=L11dii s7|0K&LL!\ /0G w<4?? * %c'l^3PQUQ`Q`Paabc  ))doo8I8IIJ w<4++ + 0W>\]a]m]m\nnop  G}rc <V^8dQhRS[/#r.r)rrs"rrrs   rcVP#)N)r()r*s&r__len__DistributedSampler.__len__src$<V^8dQhRS[RR/#)r r$rNrA)rrs"rrrs  s t rc WnR#)z Set the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas use a different random ordering for each epoch. Otherwise, the next iteration of this sampler will yield the same ordering. Args: epoch (int): Epoch number. N)r$)r*r$s&&r set_epochDistributedSampler.set_epochs  r) r rr$rr(rrrr))NNTrF) __name__ __module__ __qualname____firstlineno____doc__r+r>rCrG__static_attributes____classdictcell__)rs@rrrs5.`''R""H    r)r&collections.abcrtypingrr2torch.distributed distributedrtorch.utils.data.datasetrtorch.utils.data.samplerr__all__rrrrrXsD $ ,,  4(LLr