o biJ! @s ddlZddlmZddlmZmZmZmZmZm Z m Z m Z e r>ddl m Z mZzddlZWn ey7YnwddlmZGdddZe d Zeed fZGd d d e eZed ZGdddeZddededdddfddZddefddZddefddZdS)N)deepcopy)ListCallableIteratorUnionOptionalGenericTypeVar TYPE_CHECKING) TerminalDefToken)Literalc@s^eZdZUeed<eed<eed<eed<eed<eed<eed<ded <eed <d d Zd S)Metaemptylinecolumn start_posend_line end_columnend_poszList[TerminalDef]orig_expansion match_treecCs d|_dS)NT)rselfr=/home/ubuntu/.local/lib/python3.10/site-packages/lark/tree.py__init__ z Meta.__init__N)__name__ __module__ __qualname__bool__annotations__intrrrrrrs  r_Leaf_T Tree[_Leaf_T]c @s2eZdZUdZeed<ded<d9dedddeeddfdd Ze defd d Z d d Z ddZ ddZ d:dedefddZd9dedddfddZddZddZddZdefd d!Zd;d#d$Zd%d&ZdTreeaThe 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, end_line, end_column, start_pos, end_pos, container_line, container_column, container_end_line, container_end_column) container_* attributes consider all symbols, including those that have been inlined in the tree. For example, in the rule 'a: _A B _C', the regular attributes will mark the start and end of B, but the container_* attributes will also include _A and _C in the range. However, rules that contain 'a' will consider it in full, including _A and _C for all attributes. datazList[Branch[_Leaf_T]]childrenNmetareturncCs||_||_||_dSN)r(r)_meta)rr(r)r*rrrr9s z Tree.__init__cCs|jdur t|_|jSr,)r-rrrrrr*>s z Tree.metacCsd|j|jfS)Nz Tree(%r, %r)r(r)rrrr__repr__Dsz Tree.__repr__cCs|jSr,r(rrrr _pretty_labelGszTree._pretty_labelccs|||Vt|jdkr't|jdts'd|jddVdSdV|jD]}t|tr@||d|EdHq-||d|dVq-dS)Nr r  )r1lenr) isinstancer'_pretty)rlevel indent_strnrrrr6Js  z Tree._pretty r8cCsd|d|S)z]Returns an indented string representation of the tree. Great for debugging. r)joinr6)rr8rrrprettyVsz Tree.prettyparentzrich.tree.TreecCs ||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)rr>rrr__rich__]s z Tree.__rich__cCsh|r |d|jd}n ddl}|j|j}|jD]}t|tr(||q|d|dq|S)Nz[bold]z[/bold]rz[green]z[/green])addr( rich.treetreer'r)r5r?)rr>rCrichcrrrr?js   z Tree._richcCs0z |j|jko |j|jkWStyYdSw)NF)r(r)AttributeErrorrotherrrr__eq__ys  z Tree.__eq__cCs ||k Sr,rrGrrr__ne__rz Tree.__ne__cCst|jt|jfSr,)hashr(tupler)rrrr__hash__sz Tree.__hash__Iterator[Tree[_Leaf_T]]csP|g}t|D]}|t|<|fddt|jD7}q~ttS)zDepth-first iteration. Iterates over all the subtrees, never returning to the same node twice (Lark's parse-tree is actually a DAG). cs&g|]}t|trt|vr|qSr)r5r'id).0rEsubtreesrr s  z&Tree.iter_subtrees..)dictrOreversedr)listvalues)rqueuesubtreerrQr iter_subtreess zTree.iter_subtreesccsT|g}|j}|j}|r(|}t|tsq |Vt|jD]}||q|s dSdS)ztBreadth-first iteration. Iterates over all the subtrees, return nodes in order like pretty() does. N)appendpopr5r'rUr))rstack stack_append stack_popnodechildrrriter_subtrees_topdowns   zTree.iter_subtrees_topdownpredCallable[[Tree[_Leaf_T]], bool]cCst||S)z?Returns all nodes of the tree that evaluate pred(node) as true.)filterrZ)rrcrrr find_predszTree.find_predcs|fddS)z?Returns all nodes of the tree whose data equals the given data.cs |jkSr,r0)tr0rrs z Tree.find_data..)rf)rr(rr0r find_dataszTree.find_datacGsZd}tt|jdddD]}|j|}t|tr*|j|vr*|j|j||d<d}q |S)z\Expand (inline) children with any of the given data values. Returns True if anything changedFr T)ranger4r)r5r'r()r data_valueschangedirarrrexpand_kids_by_datas zTree.expand_kids_by_dataz!Callable[[Branch[_Leaf_T]], bool]ccsB|jD]}t|tr||D]}|Vqq||r|VqdS)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)r)r5r' scan_values)rrcrErgrrrrps  zTree.scan_valuescCst||jt|j||jdS)N)r*)typer(rr)r-)rmemorrr __deepcopy__szTree.__deepcopy__r&cCst||j|jSr,)rqr(r)rrrrcopysz Tree.copycCs||_||_dSr,r.)rr(r)rrrsets zTree.setr,)r:)r+rN)rcrdr+rN)r+r&) rr r!__doc__strr#rrrpropertyr*r/r1r6r=r@r?rIrJr$rMrZrbrfrirorr%rprsrtrurrrrr'$s2        r'r c@seZdZdZdS) SlottedTree)r(r)ruler-N)rr r! __slots__rrrrrysryLRrCfilenamerankdirzLiteral["TB", "LR", "BT", "RL"]r+cK t||fi|}||dSr,)pydot__tree_to_graph write_pngrCr}r~kwargsgraphrrrpydot__tree_to_pngrcKrr,)rwriterrrrpydot__tree_to_dotrrc sTddljd d|d|dgfddfdd|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_typer~cs4jdt|d}dd7<||S)Nr)labelr )Noderepradd_node)leafr`)rrnpydotrrnew_leafs z&pydot__tree_to_graph..new_leafcst|jd@}|dO}fdd|jD}jddd||jd}dd 7<||D] }||q3|S) Niics&g|]}t|tr |n|qSr)r5r')rPra) _to_pydotrrrrSsz;pydot__tree_to_graph.._to_pydot..rfilledz#%x)style fillcolorrr )rKr(r)rradd_edgeEdge)rYcolorsubnodesr`subnoderrrnrrrrrs  z'pydot__tree_to_graph.._to_pydotr)rDot)rCr~rrrrrs r)r|)sysrtrtypingrrrrrrr r lexerr r rD ImportErrorrrr%Branchr' ParseTreeryrwrrrrrrrs* (    1