pPjwdZddlmZddlmZmZddlmZedZ d dZ d dZ Gdd ee e Z y ) z&Helpers for manipulations with graphs.) annotations)IteratorSet)TypeVarTc#Ktgigdfd |D]}|vs|Ed{y7w)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/. c3Kt|<j|j||D]D}|vr|Ed{|vs|dks)j|dkrFd|k(r?jt|d}|d=j ||yy7w)N)lenappendpopsetupdate) vwscc boundariesdfsedges identifiedindexstacks U/home/oem/CyberBrain26-unified/.venv/lib/python3.12/site-packages/mypy/graph_utils.pyrz*strongly_connected_components..dfs!su:a Q%(#q %A~q6!!*$AhB/NN$AhB/  % b>U1X % NN eE!HJ'(CeAhj!   c "I & "s%A CC C C%CACN)rrreturnIterator[set[T]])r)verticesrrrrrrrs ` @@@@@rstrongly_connected_componentsr sQ"JEEJ& E>1v   s%A AAAci|D]}t|}|D]}||< i}|D]>}t}|D]}|jfd||D!||t|<@|S)zLUse original edges to organize SCCs in a graph by dependencies between them.c3(K|] }| ywN).0xsccsmaps r zprepare_sccs..Fs5q 5s) frozensetrr)sccsrr scc_frozenrdatadepsr$s @r prepare_sccsr+9sG$s^  $A#GAJ $$79D$$'E 6A KK5E!H5 5 6#Ys^ $ Kc(eZdZdZddZddZddZy) 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} ci}i}t}|jD]\}}|j|t|}|||<|dk(r|j |||vrg||<|D]<}||vr||j ||g||<||vs'd||<|j |>||_||_||_t|t|z |_ y)Nr) ritemsdiscardr addr in_degreerevready remaining) selfr)r3r4r5itemr*degdeps r__init__ztopsort.__init__ns#% "**, 'JD$ LL d)C!IdOax $3D  '#:HOOD) $vCH$)* # # ' '$# Y#e*4r,c|Sr r!)r7s r__iter__ztopsort.__iter__s r,c|j}|sP|jdk(s;Jd|jjDcgc] \}}|dkDs |c}}t|j}|j }t }|D]0}||D]&}||dz } | ||<| dk(s|j|(2|xjt|zc_||_|Scc}}w)Nrz#A cyclic dependency exists amongst ) r5r6r3r0 StopIterationr4rr2r ) r7r5kr9r3r4 new_readyr8 dependentnew_degs r__next__ztopsort.__next__s >>Q& 5$(NN$8$8$:F&!ScAgAFIK & NN hhE  -D Y - #I.2'. )$a<MM),  - - #i.(  Gs C C N)r)zdict[T, set[T]]rNone)rr)rzset[T])__name__ __module__ __qualname____doc__r;r=rEr!r,rr.r.Ks D5:r,r.N)rzAbstractSet[T]rdict[T, list[T]]rr)r'z list[set[T]]rrKrz)dict[AbstractSet[T], set[AbstractSet[T]]])rJ __future__rcollections.abcrr AbstractSettypingrrrr+rr.r!r,rrPsl,"8 CL++%5++\ /.$Vhs1vVr,