^j=rddlZddlZddlmZmZmZddlmZddlm Z edZ dgZ Gdde e Z y)N)IterableIteratorSized)TypeVar) IterDataPipe_TIterableWrapperIterDataPipecJeZdZdZd deededdfdZdeefdZ de fdZ y) 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] iterabledeepcopyreturnNc ||_||_y)N)r r )selfr r s p/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py__init__z$IterableWrapperIterDataPipe.__init__$s    c#K|j}|jr tj|j}|Ed{y#t$rt j ddY-wxYw7+w)NzlThe input iterable can not be deepcopied, please be aware of in-place modification would affect source data.) stacklevel)r r copy TypeErrorwarningswarn)r source_datas r__iter__z$IterableWrapperIterDataPipe.__iter__(scmm == "mmDMM:    Y   s3A.AA.A,A. A)&A.(A))A.ct|jtrt|jSt t |j d)Nz# instance doesn't have valid length) isinstancer rlenrtype__name__)rs r__len__z#IterableWrapperIterDataPipe.__len__9s= dmmU +t}}% %4:..//RSTTr)T) r __module__ __qualname____doc__rrboolrrrintr!rrr r sC*!"!!!(2,"UUr) rrcollections.abcrrrtypingr#torch.utils.data.datapipes.datapiperr__all__r r'rrr,s= 55< T] ( ).U,r"2.Ur