# Pgen imports
from . import grammar, token, tokenize
+from typing import (
+ Any,
+ Dict,
+ IO,
+ Iterable,
+ Iterator,
+ List,
+ Optional,
+ Text,
+ Tuple,
+ Union,
+ Sequence,
+ NoReturn,
+)
+from blib2to3.pgen2 import grammar
+from blib2to3.pgen2.tokenize import GoodTokenInfo
+import os
+
+
+Path = Union[str, "os.PathLike[str]"]
+
class PgenGrammar(grammar.Grammar):
pass
class ParserGenerator(object):
- def __init__(self, filename, stream=None):
+
+ filename: Path
+ stream: IO[Text]
+ generator: Iterator[GoodTokenInfo]
+ first: Dict[Text, Optional[Dict[Text, int]]]
+
+ def __init__(self, filename: Path, stream: Optional[IO[Text]] = None) -> None:
close_stream = None
if stream is None:
stream = open(filename)
self.first = {} # map from symbol name to set of tokens
self.addfirstsets()
- def make_grammar(self):
+ def make_grammar(self) -> PgenGrammar:
c = PgenGrammar()
names = list(self.dfas.keys())
names.sort()
c.start = c.symbol2number[self.startsymbol]
return c
- def make_first(self, c, name):
+ def make_first(self, c: PgenGrammar, name: Text) -> Dict[int, int]:
rawfirst = self.first[name]
+ assert rawfirst is not None
first = {}
for label in sorted(rawfirst):
ilabel = self.make_label(c, label)
first[ilabel] = 1
return first
- def make_label(self, c, label):
+ def make_label(self, c: PgenGrammar, label: Text) -> int:
# XXX Maybe this should be a method on a subclass of converter?
ilabel = len(c.labels)
if label[0].isalpha():
c.tokens[itoken] = ilabel
return ilabel
- def addfirstsets(self):
+ def addfirstsets(self) -> None:
names = list(self.dfas.keys())
names.sort()
for name in names:
self.calcfirst(name)
# print name, self.first[name].keys()
- def calcfirst(self, name):
+ def calcfirst(self, name: Text) -> None:
dfa = self.dfas[name]
self.first[name] = None # dummy to detect left recursion
state = dfa[0]
- totalset = {}
+ totalset: Dict[str, int] = {}
overlapcheck = {}
for label, next in state.arcs.items():
if label in self.dfas:
else:
self.calcfirst(label)
fset = self.first[label]
+ assert fset is not None
totalset.update(fset)
overlapcheck[label] = fset
else:
totalset[label] = 1
overlapcheck[label] = {label: 1}
- inverse = {}
+ inverse: Dict[str, str] = {}
for label, itsfirst in overlapcheck.items():
for symbol in itsfirst:
if symbol in inverse:
inverse[symbol] = label
self.first[name] = totalset
- def parse(self):
+ def parse(self) -> Tuple[Dict[Text, List["DFAState"]], Text]:
dfas = {}
- startsymbol = None
+ startsymbol: Optional[str] = None
# MSTART: (NEWLINE | RULE)* ENDMARKER
while self.type != token.ENDMARKER:
while self.type == token.NEWLINE:
# print name, oldlen, newlen
if startsymbol is None:
startsymbol = name
+ assert startsymbol is not None
return dfas, startsymbol
- def make_dfa(self, start, finish):
+ def make_dfa(self, start: "NFAState", finish: "NFAState") -> List["DFAState"]:
# To turn an NFA into a DFA, we define the states of the DFA
# to correspond to *sets* of states of the NFA. Then do some
# state reduction. Let's represent sets as dicts with 1 for
assert isinstance(start, NFAState)
assert isinstance(finish, NFAState)
- def closure(state):
- base = {}
+ def closure(state: NFAState) -> Dict[NFAState, int]:
+ base: Dict[NFAState, int] = {}
addclosure(state, base)
return base
- def addclosure(state, base):
+ def addclosure(state: NFAState, base: Dict[NFAState, int]) -> None:
assert isinstance(state, NFAState)
if state in base:
return
states = [DFAState(closure(start), finish)]
for state in states: # NB states grows while we're iterating
- arcs = {}
+ arcs: Dict[str, Dict[NFAState, int]] = {}
for nfastate in state.nfaset:
for label, next in nfastate.arcs:
if label is not None:
state.addarc(st, label)
return states # List of DFAState instances; first one is start
- def dump_nfa(self, name, start, finish):
+ def dump_nfa(self, name: Text, start: "NFAState", finish: "NFAState") -> None:
print("Dump of NFA for", name)
todo = [start]
for i, state in enumerate(todo):
else:
print(" %s -> %d" % (label, j))
- def dump_dfa(self, name, dfa):
+ def dump_dfa(self, name: Text, dfa: Sequence["DFAState"]) -> None:
print("Dump of DFA for", name)
for i, state in enumerate(dfa):
print(" State", i, state.isfinal and "(final)" or "")
for label, next in sorted(state.arcs.items()):
print(" %s -> %d" % (label, dfa.index(next)))
- def simplify_dfa(self, dfa):
+ def simplify_dfa(self, dfa: List["DFAState"]) -> None:
# This is not theoretically optimal, but works well enough.
# Algorithm: repeatedly look for two states that have the same
# set of arcs (same labels pointing to the same nodes) and
changes = True
break
- def parse_rhs(self):
+ def parse_rhs(self) -> Tuple["NFAState", "NFAState"]:
# RHS: ALT ('|' ALT)*
a, z = self.parse_alt()
if self.value != "|":
z.addarc(zz)
return aa, zz
- def parse_alt(self):
+ def parse_alt(self) -> Tuple["NFAState", "NFAState"]:
# ALT: ITEM+
a, b = self.parse_item()
while self.value in ("(", "[") or self.type in (token.NAME, token.STRING):
b = d
return a, b
- def parse_item(self):
+ def parse_item(self) -> Tuple["NFAState", "NFAState"]:
# ITEM: '[' RHS ']' | ATOM ['+' | '*']
if self.value == "[":
self.gettoken()
else:
return a, a
- def parse_atom(self):
+ def parse_atom(self) -> Tuple["NFAState", "NFAState"]:
# ATOM: '(' RHS ')' | NAME | STRING
if self.value == "(":
self.gettoken()
self.raise_error(
"expected (...) or NAME or STRING, got %s/%s", self.type, self.value
)
+ assert False
- def expect(self, type, value=None):
+ def expect(self, type: int, value: Optional[Any] = None) -> Text:
if self.type != type or (value is not None and self.value != value):
self.raise_error(
"expected %s/%s, got %s/%s", type, value, self.type, self.value
self.gettoken()
return value
- def gettoken(self):
+ def gettoken(self) -> None:
tup = next(self.generator)
while tup[0] in (tokenize.COMMENT, tokenize.NL):
tup = next(self.generator)
self.type, self.value, self.begin, self.end, self.line = tup
# print token.tok_name[self.type], repr(self.value)
- def raise_error(self, msg, *args):
+ def raise_error(self, msg: str, *args: Any) -> NoReturn:
if args:
try:
msg = msg % args
class NFAState(object):
- def __init__(self):
+ arcs: List[Tuple[Optional[Text], "NFAState"]]
+
+ def __init__(self) -> None:
self.arcs = [] # list of (label, NFAState) pairs
- def addarc(self, next, label=None):
+ def addarc(self, next: "NFAState", label: Optional[Text] = None) -> None:
assert label is None or isinstance(label, str)
assert isinstance(next, NFAState)
self.arcs.append((label, next))
class DFAState(object):
- def __init__(self, nfaset, final):
+ nfaset: Dict[NFAState, Any]
+ isfinal: bool
+ arcs: Dict[Text, "DFAState"]
+
+ def __init__(self, nfaset: Dict[NFAState, Any], final: NFAState) -> None:
assert isinstance(nfaset, dict)
assert isinstance(next(iter(nfaset)), NFAState)
assert isinstance(final, NFAState)
self.isfinal = final in nfaset
self.arcs = {} # map from label to DFAState
- def addarc(self, next, label):
+ def addarc(self, next: "DFAState", label: Text) -> None:
assert isinstance(label, str)
assert label not in self.arcs
assert isinstance(next, DFAState)
self.arcs[label] = next
- def unifystate(self, old, new):
+ def unifystate(self, old: "DFAState", new: "DFAState") -> None:
for label, next in self.arcs.items():
if next is old:
self.arcs[label] = new
- def __eq__(self, other):
+ def __eq__(self, other: Any) -> bool:
# Equality test -- ignore the nfaset instance variable
assert isinstance(other, DFAState)
if self.isfinal != other.isfinal:
return False
return True
- __hash__ = None # For Py3 compatibility.
+ __hash__: Any = None # For Py3 compatibility.
-def generate_grammar(filename="Grammar.txt"):
+def generate_grammar(filename: Path = "Grammar.txt") -> PgenGrammar:
p = ParserGenerator(filename)
return p.make_grammar()
projects / etc / vim.git / blobdiff