[c >ddlZddlmZddlmZddZeeddfdZdS)N) partition_all) no_defaultc\|tj||Stj|||SN) functoolsreduce)funcseqinitials 6lib/python3.11/site-packages/toolz/sandbox/parallel.py_reducer s24c***c7333c`|dksJ||}t||}|tkr%|tjt||}n&|tjt|||}t |}t |dkr|dSt||||S)a Reduce without guarantee of ordered reduction. inputs: ``binop`` - associative operator. The associative property allows us to leverage a parallel map to perform reductions in parallel. ``seq`` - a sequence to be aggregated ``default`` - an identity element like 0 for ``add`` or 1 for mul ``map`` - an implementation of ``map``. This may be parallel and determines how work is distributed. ``chunksize`` - Number of elements of ``seq`` that should be handled within a single function call ``combine`` - Binary operator to combine two intermediate results. If ``binop`` is of type (total, item) -> total then ``combine`` is of type (total, total) -> total Defaults to ``binop`` for common case of operators like add Fold chunks up the collection into blocks of size ``chunksize`` and then feeds each of these to calls to ``reduce``. This work is distributed with a call to ``map``, gathered back and then refolded to finish the computation. In this way ``fold`` specifies only how to chunk up data but leaves the distribution of this work to an externally provided ``map`` function. This function can be sequential or rely on multithreading, multiprocessing, or even distributed solutions. If ``map`` intends to serialize functions it should be prepared to accept and serialize lambdas. Note that the standard ``pickle`` module fails here. Example ------- >>> # Provide a parallel map to accomplish a parallel sum >>> from operator import add >>> fold(add, [1, 2, 3, 4], chunksize=2, map=map) 10 N)r r)map chunksize)rrrpartialr listlenfold)binopr defaultrrcombinechunksresultss r rr sP q= 9c * *F*#  gu - - #  gug > > > 7mmG 7||qDqzGW#CCCCrr)rtoolz.itertoolzr toolz.utilsrr rrrr r sx))))))""""""4444(SC>D>D>D>D>D>Dr