how this parsing engine works.
"""
+import copy
+from contextlib import contextmanager
# Local imports
-from . import token
+from . import grammar, token, tokenize
from typing import (
+ cast,
+ Any,
Optional,
Text,
- Sequence,
- Any,
Union,
Tuple,
Dict,
List,
+ Iterator,
Callable,
Set,
+ TYPE_CHECKING,
)
from blib2to3.pgen2.grammar import Grammar
from blib2to3.pytree import NL, Context, RawNode, Leaf, Node
+if TYPE_CHECKING:
+ from blib2to3.driver import TokenProxy
+
Results = Dict[Text, NL]
Convert = Callable[[Grammar, RawNode], Union[Node, Leaf]]
return Node(type=node[0], children=node[3], context=node[2])
+class Recorder:
+ def __init__(self, parser: "Parser", ilabels: List[int], context: Context) -> None:
+ self.parser = parser
+ self._ilabels = ilabels
+ self.context = context # not really matter
+
+ self._dead_ilabels: Set[int] = set()
+ self._start_point = copy.deepcopy(self.parser.stack)
+ self._points = {ilabel: copy.deepcopy(self._start_point) for ilabel in ilabels}
+
+ @property
+ def ilabels(self) -> Set[int]:
+ return self._dead_ilabels.symmetric_difference(self._ilabels)
+
+ @contextmanager
+ def switch_to(self, ilabel: int) -> Iterator[None]:
+ self.parser.stack = self._points[ilabel]
+ try:
+ yield
+ except ParseError:
+ self._dead_ilabels.add(ilabel)
+ finally:
+ self.parser.stack = self._start_point
+
+ def add_token(
+ self, tok_type: int, tok_val: Optional[Text], raw: bool = False
+ ) -> None:
+ func: Callable[..., Any]
+ if raw:
+ func = self.parser._addtoken
+ else:
+ func = self.parser.addtoken
+
+ for ilabel in self.ilabels:
+ with self.switch_to(ilabel):
+ args = [tok_type, tok_val, self.context]
+ if raw:
+ args.insert(0, ilabel)
+ func(*args)
+
+ def determine_route(
+ self, value: Optional[Text] = None, force: bool = False
+ ) -> Optional[int]:
+ alive_ilabels = self.ilabels
+ if len(alive_ilabels) == 0:
+ *_, most_successful_ilabel = self._dead_ilabels
+ raise ParseError("bad input", most_successful_ilabel, value, self.context)
+
+ ilabel, *rest = alive_ilabels
+ if force or not rest:
+ return ilabel
+ else:
+ return None
+
+
class ParseError(Exception):
"""Exception to signal the parser is stuck."""
self.grammar = grammar
self.convert = convert or lam_sub
- def setup(self, start: Optional[int] = None) -> None:
+ def setup(self, proxy: "TokenProxy", start: Optional[int] = None) -> None:
"""Prepare for parsing.
This *must* be called before starting to parse.
self.stack: List[Tuple[DFAS, int, RawNode]] = [stackentry]
self.rootnode: Optional[NL] = None
self.used_names: Set[str] = set()
+ self.proxy = proxy
def addtoken(self, type: int, value: Optional[Text], context: Context) -> bool:
"""Add a token; return True iff this is the end of the program."""
# Map from token to label
- ilabel = self.classify(type, value, context)
+ ilabels = self.classify(type, value, context)
+ assert len(ilabels) >= 1
+
+ # If we have only one state to advance, we'll directly
+ # take it as is.
+ if len(ilabels) == 1:
+ [ilabel] = ilabels
+ return self._addtoken(ilabel, type, value, context)
+
+ # If there are multiple states which we can advance (only
+ # happen under soft-keywords), then we will try all of them
+ # in parallel and as soon as one state can reach further than
+ # the rest, we'll choose that one. This is a pretty hacky
+ # and hopefully temporary algorithm.
+ #
+ # For a more detailed explanation, check out this post:
+ # https://tree.science/what-the-backtracking.html
+
+ with self.proxy.release() as proxy:
+ counter, force = 0, False
+ recorder = Recorder(self, ilabels, context)
+ recorder.add_token(type, value, raw=True)
+
+ next_token_value = value
+ while recorder.determine_route(next_token_value) is None:
+ if not proxy.can_advance(counter):
+ force = True
+ break
+
+ next_token_type, next_token_value, *_ = proxy.eat(counter)
+ if next_token_type == tokenize.OP:
+ next_token_type = grammar.opmap[cast(str, next_token_value)]
+
+ recorder.add_token(next_token_type, next_token_value)
+ counter += 1
+
+ ilabel = cast(int, recorder.determine_route(next_token_value, force=force))
+ assert ilabel is not None
+
+ return self._addtoken(ilabel, type, value, context)
+
+ def _addtoken(
+ self, ilabel: int, type: int, value: Optional[Text], context: Context
+ ) -> bool:
# Loop until the token is shifted; may raise exceptions
while True:
dfa, state, node = self.stack[-1]
# No success finding a transition
raise ParseError("bad input", type, value, context)
- def classify(self, type: int, value: Optional[Text], context: Context) -> int:
- """Turn a token into a label. (Internal)"""
+ def classify(self, type: int, value: Optional[Text], context: Context) -> List[int]:
+ """Turn a token into a label. (Internal)
+
+ Depending on whether the value is a soft-keyword or not,
+ this function may return multiple labels to choose from."""
if type == token.NAME:
# Keep a listing of all used names
assert value is not None
self.used_names.add(value)
# Check for reserved words
- ilabel = self.grammar.keywords.get(value)
- if ilabel is not None:
- return ilabel
+ if value in self.grammar.keywords:
+ return [self.grammar.keywords[value]]
+ elif value in self.grammar.soft_keywords:
+ assert type in self.grammar.tokens
+ return [
+ self.grammar.soft_keywords[value],
+ self.grammar.tokens[type],
+ ]
+
ilabel = self.grammar.tokens.get(type)
if ilabel is None:
raise ParseError("bad token", type, value, context)
- return ilabel
+ return [ilabel]
def shift(
self, type: int, value: Optional[Text], newstate: int, context: Context
projects / etc / vim.git / blobdiff