) \ddlZddlmZddlmZmZmZmZmZm Z m Z m Z e r)ddl m Z mZ ddlZej"dk\rddlmZnddlmZddlmZGd d Ze d Zeed fZGd de eZedZGddeZddededdddfdZddefdZddefdZy#e$rY|wxYw)N)deepcopy)ListCallableIteratorUnionOptionalGenericTypeVar TYPE_CHECKING) TerminalDefToken))Literal) OrderedDictcneZdZUeed<eed<eed<eed<eed<eed<eed<ded <eed <d Zy ) Metaemptylinecolumn start_posend_line end_columnend_poszList[TerminalDef]orig_expansion match_treecd|_y)NT)rselfs lib/third_party/lark/tree.py__init__z Meta.__init__ s  N)__name__ __module__ __qualname__bool__annotations__intr"r#r!rrs6 K I KNMO L''r#r_Leaf_T Tree[_Leaf_T]c eZdZUdZeed<ded<d"dedddeeddfdZe defd Z d Z d Z d Z d#d edefdZd"dedddfdZdZdZdZdefdZd$dZdZd%dZdeddfdZdZdddeefdZdZd&d Zdeddddfd!Zy)'TreeaThe main tree class. Creates a new tree, and stores "data" and "children" in attributes of the same name. Trees can be hashed and compared. Parameters: data: The name of the rule or alias children: List of matched sub-rules and terminals meta: Line & Column numbers (if ``propagate_positions`` is enabled). meta attributes: line, column, start_pos, end_line, end_column, end_pos datazList[Branch[_Leaf_T]]childrenNmetareturnc.||_||_||_yN)r/r0_meta)r r/r0r1s r!r"z Tree.__init__8s    r#cP|jt|_|jSr4)r5rrs r!r1z Tree.meta=s :: DJzzr#c<d|jd|jdS)NzTree(z, )r/r0rs r!__repr__z Tree.__repr__Cs!%DMM::r#c|jSr4r/rs r! _pretty_labelzTree._pretty_labelFs yyr#c#|K||z|jt|jdk(r3t|jdtsd|jddyd|jD]?}t|tr|j |dz|Ed{1||dzz|dAy7w)Nr r  )r=lenr0 isinstancer._pretty)r level indent_strns r!rCz Tree._prettyIsE!"4#5#5#7"899 t}}  ":dmmA6F+Mt}}Q'(+ +J]]a& yyq*==='q121#R88 #=sB B<"B:#B<rEcDdj|jd|S)z]Returns an indented string representation of the tree. Great for debugging. r)joinrC)r rEs r!prettyz Tree.prettyUs wwt||Az233r#parentzrich.tree.Treec$|j|S)zReturns a tree widget for the 'rich' library. Example: :: from rich import print from lark import Tree tree = Tree('root', ['node1', 'node2']) print(tree) )_rich)r rKs r!__rich__z Tree.__rich__\szz&!!r#c,|r |jd|jd}n)ddl}|jj |j}|j D]9}t |tr|j|%|jd|d;|S)Nz[bold]z[/bold]rz[green]z[/green])addr/ rich.treetreer.r0rBrM)r rKrRrichcs r!rMz Tree._richisx ::tyyk9:D 99>>$)),DA!T" 71#X./   r#c |j|jk(xr|j|jk(S#t$rYywxYw)NF)r/r0AttributeErrorr others r!__eq__z Tree.__eq__xs> 99 *Nt}}/N N  s36 AAc||k( Sr4r*rWs r!__ne__z Tree.__ne__~sEM""r#cVt|jt|jfSr4)hashr/tupler0rs r!__hash__z Tree.__hash__sTYYdmm 4566r#Iterator[Tree[_Leaf_T]]c |g}t}|D]S}||t|<|t|jDcgc]!}t |t rt||vr|#c}z }U~tt |jScc}w)zDepth-first iteration. Iterates over all the subtrees, never returning to the same node twice (Lark's parse-tree is actually a DAG). )ridreversedr0rBr.listvalues)r queuesubtreessubtreerTs r! iter_subtreeszTree.iter_subtreess =G$+HR[ ! '*:*:!;H!;A#At,Ah1F!;H HE X__./00 Hs&B c#K|g}|j}|j}|rB|}t|ts|t |j D] }|| |rAyyw)ztBreadth-first iteration. Iterates over all the subtrees, return nodes in order like pretty() does. N)appendpoprBr.rcr0)r stack stack_append stack_popnodechilds r!iter_subtrees_topdownzTree.iter_subtrees_topdowns] || II ;DdD)J!$--0U#1 s AA#!A#predc6t||jS)z?Returns all nodes of the tree that evaluate pred(node) as true.)filterri)r rss r! find_predzTree.find_predsdD..011r#c,|jfdS)z?Returns all nodes of the tree whose data equals the given data.c"|jk(Sr4r<)tr/s r!z Tree.find_data..s $r#)rv)r r/s `r! find_datazTree.find_datas~~677r#cd}tt|jdz ddD]O}|j|}t|ts#|j |vs2|j|j||dzd}Q|S)z\Expand (inline) children with any of the given data values. Returns True if anything changedFr T)rangerAr0rBr.r/)r data_valueschangedirqs r!expand_kids_by_datazTree.expand_kids_by_datasos4==)!+R4AMM!$E%&5::+D',~~ a!$ 5 r#z!Callable[[Branch[_Leaf_T]], bool]c#K|jD]:}t|tr|j|D]}|.||s7|<yw)zReturn all values in the tree that evaluate pred(value) as true. This can be used to find all the tokens in the tree. Example: >>> all_tokens = tree.scan_values(lambda v: isinstance(v, Token)) N)r0rBr. scan_values)r rsrTrys r!rzTree.scan_valuessFA!T"t,AG-7G s AA A czt||jt|j||jS)N)r1)typer/rr0r5)r memos r! __deepcopy__zTree.__deepcopy__s*tDz$))XdmmT%BTTr#cNt||j|jSr4)rr/r0rs r!copyz Tree.copystDz$))T]]33r#c ||_||_yr4r9)r r/r0s r!setzTree.sets   r#r4)z )r2r`)rszCallable[[Tree[_Leaf_T]], bool]r2r`)r2r,) r$r%r&__doc__strr(rrr"propertyr1r:r=rCrJrNrMrYr[r)r_rirrrvr{rrr+rrrrr*r#r!r.r.(s  I%%S,C8TX>cg d ; 944c4 "h'78 "BR "  #7#71 $ 28c8&?8  CQXHY U4!!'>!4!r#r.rceZdZdZy) SlottedTree)r/r0ruler5N)r$r%r& __slots__r*r#r!rrs3Ir#rrRfilenamerankdirzLiteral["TB", "LR", "BT", "RL"]r2c @t||fi|}|j|yr4)pydot__tree_to_graph write_pngrRrrkwargsgraphs r!pydot__tree_to_pngrs w 9& 9E OOHr#c @t||fi|}|j|yr4)rwriters r!pydot__tree_to_dotrs w 9& 9E KKr#c xddljdd|d|dgfdfd|S)aCreates a colorful image that represents the tree (data+children, without meta) Possible values for `rankdir` are "TB", "LR", "BT", "RL", corresponding to directed graphs drawn from top to bottom, from left to right, from bottom to top, and from right to left, respectively. `kwargs` can be any graph attribute (e. g. `dpi=200`). For a list of possible attributes, see https://www.graphviz.org/doc/info/attrs.html. rNdigraph) graph_typercjdt|}dxxdz cc<j||S)Nr)labelr )Noderepradd_node)leafrprrpydots r!new_leafz&pydot__tree_to_graph..new_leafs;zz!A$d4jz1 !  t r#ct|jdz}|dz}|jDcgc]"}t|tr|n |$}} j ddd|z|j}dxxdz cc<j ||D]#}j j||%|Scc}w)Niirfilledz#%x)style fillcolorrr ) r]r/r0rBr.rradd_edgeEdge) rhcolorrqsubnodesrpsubnode _to_pydotrrrrs r!rz'pydot__tree_to_graph.._to_pydotsW\\"X- ")!1!13!1)35$(?Ie$Xe_T!1 3zz!A$h%%-w||z\ !  tG NN5::dG4 5  3s'Cr*)rDot)rRrrrrrrrs @@@@@r!rrsF EII FG Fv FE A  dO Lr#)LR) sysrrtypingrrrrrr r r lexerr rrS ImportError version_infortyping_extensions collectionsrrr+Branchr. ParseTreerrrrrr*r#r!rs ]]])  6!"-$   )  w' (i!77 i!X M 4$4TS;\pt T &t&w    sB##B+*B+