΅i=rSSKrSSKrSSKJrJrJr SSKJr SSKJ r \"S5r S/r "SS\ \ 5r g)N)IterableIteratorSized)TypeVar) IterDataPipe_TIterableWrapperIterDataPipecZ\rSrSrSrS S\\S\SS4SjjrS\ \4Sjr S\ 4S jr S r g) r a Wraps an iterable object to create an IterDataPipe. Args: iterable: Iterable object to be wrapped into an IterDataPipe deepcopy: Option to deepcopy input iterable object for each iterator. The copy is made when the first element is read in ``iter()``. .. note:: If ``deepcopy`` is explicitly set to ``False``, users should ensure that the data pipeline doesn't contain any in-place operations over the iterable instance to prevent data inconsistency across iterations. Example: >>> # xdoctest: +SKIP >>> from torchdata.datapipes.iter import IterableWrapper >>> dp = IterableWrapper(range(10)) >>> list(dp) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] iterabledeepcopyreturnNcXlX lg)N)r r )selfr r s _/home/james-whalen/.local/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py__init__$IterableWrapperIterDataPipe.__init__$s   c## URnUR(a![R"UR5nUShvN g![a [R "SSS9 N,f=fN*7f)NzlThe input iterable can not be deepcopied, please be aware of in-place modification would affect source data.) stacklevel)r r copy TypeErrorwarningswarn)r source_datas r__iter__$IterableWrapperIterDataPipe.__iter__(sbmm == "mmDMM:    Y   s4A3 A A3A1A3 A.+A3-A..A3c[UR[5(a[UR5$[ [ U5R S35e)Nz# instance doesn't have valid length) isinstancer rlenrtype__name__)rs r__len__#IterableWrapperIterDataPipe.__len__9s@ dmmU + +t}}% %4:..//RSTTr)r r )T)r# __module__ __qualname____firstlineno____doc__rrboolrrrintr$__static_attributes__rrr r sC*!"!!!(2,"UUr) rrcollections.abcrrrtypingr#torch.utils.data.datapipes.datapiperr__all__r r-rrr2s= 55< T] ( ).U,r"2.Ur