# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
# Licensed to PSF under a Contributor Agreement.
"""This module defines the data structures used to represent a grammar.
These are a bit arcane because they are derived from the data
structures used by Python's 'pgen' parser generator.
There's also a table here mapping operators to their names in the
token module; the Python tokenize module reports all operators as the
fallback token code OP, but the parser needs the actual token code.
from . import token, tokenize
"""Pgen parsing tables conversion class.
Once initialized, this class supplies the grammar tables for the
parsing engine implemented by parse.py. The parsing engine
accesses the instance variables directly. The class here does not
provide initialization of the tables; several subclasses exist to
do this (see the conv and pgen modules).
The load() method reads the tables from a pickle file, which is
much faster than the other ways offered by subclasses. The pickle
file is written by calling dump() (after loading the grammar
tables using a subclass). The report() method prints a readable
representation of the tables to stdout, for debugging.
The instance variables are as follows:
symbol2number -- a dict mapping symbol names to numbers. Symbol
numbers are always 256 or higher, to distinguish
them from token numbers, which are between 0 and
number2symbol -- a dict mapping numbers to symbol names;
these two are each other's inverse.
states -- a list of DFAs, where each DFA is a list of
states, each state is a list of arcs, and each
arc is a (i, j) pair where i is a label and j is
a state number. The DFA number is the index into
this list. (This name is slightly confusing.)
Final states are represented by a special arc of
the form (0, j) where j is its own state number.
dfas -- a dict mapping symbol numbers to (DFA, first)
pairs, where DFA is an item from the states list
above, and first is a set of tokens that can
begin this grammar rule (represented by a dict
whose values are always 1).
labels -- a list of (x, y) pairs where x is either a token
number or a symbol number, and y is either None
or a string; the strings are keywords. The label
number is the index in this list; label numbers
are used to mark state transitions (arcs) in the
start -- the number of the grammar's start symbol.
keywords -- a dict mapping keyword strings to arc labels.
tokens -- a dict mapping token numbers to arc labels.
self.labels = [(0, "EMPTY")]
def dump(self, filename):
"""Dump the grammar tables to a pickle file.
dump() recursively changes all dict to OrderedDict, so the pickled file
is not exactly the same as what was passed in to dump(). load() uses the
pickled file to create the tables, but only changes OrderedDict to dict
at the top level; it does not recursively change OrderedDict to dict.
So, the loaded tables are different from the original tables that were
passed to load() in that some of the OrderedDict (from the pickled file)
are not changed back to dict. For parsing, this has no effect on
performance because OrderedDict uses dict's __getitem__ with nothing in
with open(filename, "wb") as f:
d = _make_deterministic(self.__dict__)
def load(self, filename):
"""Load the grammar tables from a pickle file."""
"""Load the grammar tables from a pickle bytes object."""
self.__dict__.update(pickle.loads(pkl))
for dict_attr in ("symbol2number", "number2symbol", "dfas", "keywords",
"tokens", "symbol2label"):
setattr(new, dict_attr, getattr(self, dict_attr).copy())
new.labels = self.labels[:]
new.states = self.states[:]
"""Dump the grammar tables to standard output, for debugging."""
from pprint import pprint
pprint(self.symbol2number)
pprint(self.number2symbol)
print "start", self.start
def _make_deterministic(top):
if isinstance(top, dict):
return collections.OrderedDict(
sorted(((k, _make_deterministic(v)) for k, v in top.iteritems())))
if isinstance(top, list):
return [_make_deterministic(e) for e in top]
if isinstance(top, tuple):
return tuple(_make_deterministic(e) for e in top)
# Map from operator to number (since tokenize doesn't do this)
for line in opmap_raw.splitlines():
opmap[op] = getattr(token, name)
It is recommended that you Edit text format, this type of Fix handles quite a lot in one request
