+ j#^RIt^RIHt^RIHtHtHt^RIHtH t ^RI t ^RI H t ^RI Ht^RIHt^RIHt^R IHtHtR R .t] !R R R7t]!R4!RR ]],44tRt]!R4!RR ]44tR#)N) namedtuple)CallableIteratorSized)AnyTypeVar)default_collate)functional_datapipe)dataframe_wrapper) IterDataPipe)_check_unpickable_fnvalidate_input_colCollatorIterDataPipeMapperIterDataPipe_T_coT) covariantmapc|aa]tRt^toRtR V3RlV3RllltRtV3RlRltV3RlRltV3R lt R t Vt V;t #) rao Applies a function over each item from the source DataPipe (functional name: ``map``). The function can be any regular Python function or partial object. Lambda function is not recommended as it is not supported by pickle. Args: datapipe: Source Iterable DataPipe fn: Function being applied over each item input_col: Index or indices of data which ``fn`` is applied, such as: - ``None`` as default to apply ``fn`` to the data directly. - Integer(s) is used for list/tuple. - Key(s) is used for dict. output_col: Index of data where result of ``fn`` is placed. ``output_col`` can be specified only when ``input_col`` is not ``None`` - ``None`` as default to replace the index that ``input_col`` specified; For ``input_col`` with multiple indices, the left-most one is used, and other indices will be removed. - Integer is used for list/tuple. ``-1`` represents to append result at the end. - Key is used for dict. New key is acceptable. Example: >>> # xdoctest: +SKIP >>> from torchdata.datapipes.iter import IterableWrapper, Mapper >>> def add_one(x): ... return x + 1 >>> dp = IterableWrapper(range(10)) >>> # Invocation via functional form is preferred ... map_dp_1 = dp.map(add_one) >>> list(map_dp_1) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> # We discourage the usage of `lambda` functions as they are not serializable with `pickle` >>> # Use `functools.partial` or explicitly define the function instead >>> map_dp_2 = Mapper(dp, lambda x: x + 1) >>> list(map_dp_2) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] c*<V^8dQhRS[RS[RR/#)datapipefnreturnNr r)format __classdict__s"i/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/utils/data/datapipes/iter/callable.py __annotate__MapperIterDataPipe.__annotate__Hs)*** *  *cd<\PPR4\SV`4Wn\ V4W nW0nVfVe \R4h\V\\34'd%\V4^8d \R4hV^,pW@n\W#4R#)zpython.data_pipes.mapNz3`output_col` must be None when `input_col` is None.z3`output_col` must be a single-element list or tuple)torch_C_log_api_usage_oncesuper__init__rr r input_col ValueError isinstancelisttuplelen output_colr)selfrrr'r- __class__s&&&&&rr&MapperIterDataPipe.__init__Hs $$%<=  R "  !7RS S j4- 0 0:" !VWW#AJ$2)r caVPf VPfVPS4#VPfVPS4pM\VP\\ 34'dX\ ;QJd!.V3RlVP4FNK 5M!V3RlVP44pVP!V!pM"VPSVP,4p\S\ 4'dRp\ S4oMRpVPfv\VP\\ 34'd@VSVP^,&\ VPR,RR7FpSVK MBVSVP&M2VPR8XdSPV4MVSVP&V'd \ S4#S#)Nc36<"TFpSV,xK R#5iN).0coldatas& r /MapperIterDataPipe._apply_fn..gs=nscnsTF:NN)reverse)r'r-rr)r*r+sortedappend)r.r7resargst_flagidxs&f r _apply_fnMapperIterDataPipe._apply_fn`sY >> !doo&=774= >> !''$-C u 6 65=dnn=55=dnn==D''4.C''$t~~./C dE " "F:DF ?? "$..4-88*-T^^A&'!$.."4dCCS D(+T^^$"$ C (+T__%%uT{.$.r c0<V^8dQhRS[S[,/#rr)rr)rrs"rrrs''(5/'r c#Z"VPFpVPV4xK R#5ir3)rrC)r.r7s& r__iter__MapperIterDataPipe.__iter__s"MMD..& &"s)+c <V^8dQhRS[/#rF)int)rrs"rrrsUUUr c\VP\4'd\VP4#\ \ V4P R24h)z# instance doesn't have valid length)r)rrr, TypeErrortype__name__)r.s&r__len__MapperIterDataPipe.__len__s@ dmmU + +t}}% %4:..//RSTTr c2<V^8dQh/S[;R&S[;R&#)rrrr)rrs"rrrs"TUV LWr )rrr'r-)NN) rO __module__ __qualname____firstlineno____doc__r&rCrHrP__annotate_func____static_attributes____classdictcell__ __classcell__r/rs@@rrrs9&V**0!/F''UUYr cT\VP4^8d \R4hV^,p\P!V4p.p.pVFpWc9gK \R4h VFpW`9d,\ W,4'g \R4hW,pM!^RIHpVPP4pVP\V44V!W&,4p VPV 4K \RV4p V !V!p V # \dp \R4T hRp ? ii;i)r:z%Only supports one DataFrame per batchzConversion keys mismatchz5Collate (DF)DataPipe requires callable as dict valuesNz?unable to import default collation function from the TorchArrow CollateResult)r,items RuntimeError df_wrapper get_columnscallabletorcharrow.pytorchpytorchrecDefault Exceptionr>strr) conversionitemdf columns_name tuple_names tuple_valuesname collation_fntapevaluetpl_clsr+s && r_collate_helperrus 4::BCC aB))"-LKL  #9: :  J,--"K&+L 0"ww0 3t9%RX&E")0+6G \ "E L "U s D  D' D""D'collatecJaa]tRt^toRt]R3V3RlV3RllltRtVtV;t #)ra Collates samples from DataPipe to Tensor(s) by a custom collate function (functional name: ``collate``). By default, it uses :func:`torch.utils.data.default_collate`. .. note:: While writing a custom collate function, you can import :func:`torch.utils.data.default_collate` for the default behavior and `functools.partial` to specify any additional arguments. Args: datapipe: Iterable DataPipe being collated collate_fn: Customized collate function to collect and combine data or a batch of data. Default function collates to Tensor(s) based on data type. Example: >>> # xdoctest: +SKIP >>> # Convert integer data to float Tensor >>> class MyIterDataPipe(torch.utils.data.IterDataPipe): ... def __init__(self, start, end): ... super(MyIterDataPipe).__init__() ... assert end > start, "this example only works with end >= start" ... self.start = start ... self.end = end ... ... def __iter__(self): ... return iter(range(self.start, self.end)) ... ... def __len__(self): ... return self.end - self.start >>> ds = MyIterDataPipe(start=3, end=7) >>> print(list(ds)) [3, 4, 5, 6] >>> def collate_fn(batch): ... return torch.tensor(batch, dtype=torch.float) >>> collated_ds = CollateIterDataPipe(ds, collate_fn=collate_fn) >>> print(list(collated_ds)) [tensor(3.), tensor(4.), tensor(5.), tensor(6.)] Nc<V^8dQhRS[RS[RS[3,S[S[S[,S[S[,3,,R,RS[R,RR/#)rrri.N collate_fnr)r rrdictrh)rrs"rr!CollatorIterDataPipe.__annotate__sb:::S#X& sSy(S.( )* : tO : :r c<Ve\SV`WR7R#\V4'd\SV`WR7R#\P!\ V4p\SV`WR7R#)N)r)r%r&rb functoolspartialru)r.rriryr/s&&&&rr&CollatorIterDataPipe.__init__sZ  ! G X  5 ##  9'.. K   9r r4) rOrSrTrUrVr r&rXrYrZr[s@@rrrs"%X!&* :::r )r} collectionsrcollections.abcrrrtypingrrr"torch.utils.data._utils.collater %torch.utils.data.datapipes._decoratorr $torch.utils.data.datapipes.dataframer r`#torch.utils.data.datapipes.datapiper 'torch.utils.data.datapipes.utils.commonr r__all__rrrurr4r rrs"55 ;EP<  4(UoUe,oUoUd(VY::-:: ::r