iwdZddlmZddlmZmZddlmZedZ dd Z ddZ Gddee e Z dS)z&Helpers for manipulations with graphs.) annotations)IteratorSet)TypeVarTverticesAbstractSet[T]edgesdict[T, list[T]]returnIterator[set[T]]c#Ktgigdfd |D]}|vr|Ed{VdS)aCompute Strongly Connected Components of a directed graph. Args: vertices: the labels for the vertices edges: for each vertex, gives the target vertices of its outgoing edges Returns: An iterator yielding strongly connected components, each represented as a set of vertices. Each input vertex will occur exactly once; vertices not part of a SCC are returned as singleton sets. From https://code.activestate.com/recipes/578507/. vrr r c3Kt|<|||D]T}|vr|Ed{V|vr8|dkr&|dk&Ud|krWt|d}|d=||VdSdS)N)lenappendpopsetupdate) rwscc boundariesdfsr identifiedindexstacks O/root/projects/qq-shell/venv/lib64/python3.11/site-packages/mypy/graph_utils.pyrz*strongly_connected_components..dfs!s8u::a Q%(###q % %A~~3q66!!!!!!!!*$$AhB//NN$$$AhB// b>U1X % % NN   eE!HJJ'((CeAhjj!   c " " "IIIII & %N)rrr r )r)rr rrrrrrs ` @@@@@rstrongly_connected_componentsr s"JEEJ& E>>s1vv       rsccs list[set[T]])dict[AbstractSet[T], set[AbstractSet[T]]]ci|D]}t|}|D]}||<i}|D]N}t}|D])}|fd||D*||t|<O|S)zLUse original edges to organize SCCs in a graph by dependencies between them.c3(K|] }|V dSN).0xsccsmaps r zprepare_sccs..Fs'55q 555555r) frozensetrr)r!r r scc_frozenrdatadepsr*s @r prepare_sccsr09sG$$s^^  $ $A#GAJJ $68D$$$'EE 6 6A KK5555E!H555 5 5 5 5#Ys^^ Krc*eZdZdZd dZd dZdd Zd S)topsortaTopological sort using Kahn's algorithm. Uses in-degree counters and a reverse adjacency list, so the total work is O(V + E). Implemented as a class rather than a generator for better mypyc compilation. Args: data: A map from vertices to all vertices that it has an edge connecting it to. NOTE: dependency sets in this data structure are modified in place to remove self-dependencies. Orphans are handled internally and are not added to `data`. Returns: An iterator yielding sets of vertices that have an equivalent ordering. Example: Suppose the input has the following structure: {A: {B, C}, B: {D}, C: {D}} The algorithm treats orphan dependencies as if normalized to: {A: {B, C}, B: {D}, C: {D}, D: {}} It will yield the following values: {D} {B, C} {A} r.dict[T, set[T]]r Noneci}i}t}|D]\}}||t|}|||<|dkr||||vrg||<|D]F}||vr|||"|g||<||vrd||<||G||_||_||_t|t|z |_ dS)Nr) ritemsdiscardraddr in_degreerevready remaining) selfr.r9r:r;itemr/degdeps r__init__ztopsort.__init__ns#% "**,, ' 'JD$ LL   d))C!IdOaxx $3D  ' '#::HOOD)))) $vCH$)* # # '# Y#e**4rr c|Sr&r')r=s r__iter__ztopsort.__iter__s rset[T]c|j}|s@|jdks.Jdd|jDt|j}|j}t }|D]8}||D]-}||dz }|||<|dkr||.9|xjt|zc_||_|S)Nrz#A cyclic dependency exists amongst c$g|] \}}|dk |S)rr')r(kr?s r z$topsort.__next__..s!FFF&!ScAggAgggr) r;r<r9r6 StopIterationr:rr8r)r=r;r9r: new_readyr> dependentnew_degs r__next__ztopsort.__next__s  >Q&&&KFFDN$8$8$:$:FFFKK'&& N hEE  - -D Y - - #I.2'. )$a<<MM),,,  - #i..(  rN)r.r3r r4)r r )r rD)__name__ __module__ __qualname____doc__rArCrNr'rrr2r2Ks[  D5555:rr2N)rr r r r r )r!r"r r r r#)rR __future__rcollections.abcrr AbstractSettypingrrr r0rr2r'rrrWs,,""""""88888888 GCLL++++\$VVVVVhs1vVVVVVr