211 lines
6.4 KiB
Python
211 lines
6.4 KiB
Python
# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
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# Licensed to PSF under a Contributor Agreement.
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"""This module defines the data structures used to represent a grammar.
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These are a bit arcane because they are derived from the data
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structures used by Python's 'pgen' parser generator.
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There's also a table here mapping operators to their names in the
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token module; the Python tokenize module reports all operators as the
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fallback token code OP, but the parser needs the actual token code.
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"""
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# Python imports
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import collections
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import pickle
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# Local imports
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from . import token
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class Grammar(object):
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"""Pgen parsing tables conversion class.
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Once initialized, this class supplies the grammar tables for the
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parsing engine implemented by parse.py. The parsing engine
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accesses the instance variables directly. The class here does not
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provide initialization of the tables; several subclasses exist to
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do this (see the conv and pgen modules).
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The load() method reads the tables from a pickle file, which is
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much faster than the other ways offered by subclasses. The pickle
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file is written by calling dump() (after loading the grammar
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tables using a subclass). The report() method prints a readable
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representation of the tables to stdout, for debugging.
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The instance variables are as follows:
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symbol2number -- a dict mapping symbol names to numbers. Symbol
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numbers are always 256 or higher, to distinguish
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them from token numbers, which are between 0 and
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255 (inclusive).
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number2symbol -- a dict mapping numbers to symbol names;
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these two are each other's inverse.
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states -- a list of DFAs, where each DFA is a list of
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states, each state is a list of arcs, and each
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arc is a (i, j) pair where i is a label and j is
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a state number. The DFA number is the index into
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this list. (This name is slightly confusing.)
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Final states are represented by a special arc of
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the form (0, j) where j is its own state number.
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dfas -- a dict mapping symbol numbers to (DFA, first)
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pairs, where DFA is an item from the states list
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above, and first is a set of tokens that can
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begin this grammar rule (represented by a dict
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whose values are always 1).
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labels -- a list of (x, y) pairs where x is either a token
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number or a symbol number, and y is either None
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or a string; the strings are keywords. The label
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number is the index in this list; label numbers
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are used to mark state transitions (arcs) in the
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DFAs.
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start -- the number of the grammar's start symbol.
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keywords -- a dict mapping keyword strings to arc labels.
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tokens -- a dict mapping token numbers to arc labels.
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"""
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def __init__(self):
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self.symbol2number = {}
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self.number2symbol = {}
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self.states = []
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self.dfas = {}
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self.labels = [(0, "EMPTY")]
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self.keywords = {}
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self.tokens = {}
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self.symbol2label = {}
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self.start = 256
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def dump(self, filename):
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"""Dump the grammar tables to a pickle file.
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dump() recursively changes all dict to OrderedDict, so the pickled file
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is not exactly the same as what was passed in to dump(). load() uses the
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pickled file to create the tables, but only changes OrderedDict to dict
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at the top level; it does not recursively change OrderedDict to dict.
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So, the loaded tables are different from the original tables that were
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passed to load() in that some of the OrderedDict (from the pickled file)
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are not changed back to dict. For parsing, this has no effect on
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performance because OrderedDict uses dict's __getitem__ with nothing in
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between.
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"""
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with open(filename, "wb") as f:
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d = _make_deterministic(self.__dict__)
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pickle.dump(d, f, 2)
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def load(self, filename):
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"""Load the grammar tables from a pickle file."""
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with open(filename, "rb") as f:
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d = pickle.load(f)
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self.__dict__.update(d)
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def loads(self, pkl):
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"""Load the grammar tables from a pickle bytes object."""
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self.__dict__.update(pickle.loads(pkl))
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def copy(self):
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"""
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Copy the grammar.
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"""
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new = self.__class__()
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for dict_attr in ("symbol2number", "number2symbol", "dfas", "keywords",
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"tokens", "symbol2label"):
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setattr(new, dict_attr, getattr(self, dict_attr).copy())
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new.labels = self.labels[:]
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new.states = self.states[:]
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new.start = self.start
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return new
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def report(self):
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"""Dump the grammar tables to standard output, for debugging."""
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from pprint import pprint
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print("s2n")
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pprint(self.symbol2number)
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print("n2s")
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pprint(self.number2symbol)
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print("states")
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pprint(self.states)
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print("dfas")
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pprint(self.dfas)
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print("labels")
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pprint(self.labels)
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print("start", self.start)
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def _make_deterministic(top):
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if isinstance(top, dict):
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return collections.OrderedDict(
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sorted(((k, _make_deterministic(v)) for k, v in top.items())))
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if isinstance(top, list):
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return [_make_deterministic(e) for e in top]
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if isinstance(top, tuple):
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return tuple(_make_deterministic(e) for e in top)
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return top
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# Map from operator to number (since tokenize doesn't do this)
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opmap_raw = """
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( LPAR
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) RPAR
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[ LSQB
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] RSQB
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: COLON
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, COMMA
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; SEMI
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+ PLUS
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- MINUS
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* STAR
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/ SLASH
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| VBAR
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& AMPER
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< LESS
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> GREATER
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= EQUAL
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. DOT
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% PERCENT
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` BACKQUOTE
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{ LBRACE
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} RBRACE
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@ AT
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@= ATEQUAL
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== EQEQUAL
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!= NOTEQUAL
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<> NOTEQUAL
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<= LESSEQUAL
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>= GREATEREQUAL
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~ TILDE
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^ CIRCUMFLEX
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<< LEFTSHIFT
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>> RIGHTSHIFT
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** DOUBLESTAR
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+= PLUSEQUAL
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-= MINEQUAL
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*= STAREQUAL
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/= SLASHEQUAL
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%= PERCENTEQUAL
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&= AMPEREQUAL
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|= VBAREQUAL
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^= CIRCUMFLEXEQUAL
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<<= LEFTSHIFTEQUAL
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>>= RIGHTSHIFTEQUAL
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**= DOUBLESTAREQUAL
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// DOUBLESLASH
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//= DOUBLESLASHEQUAL
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-> RARROW
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"""
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opmap = {}
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for line in opmap_raw.splitlines():
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if line:
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op, name = line.split()
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opmap[op] = getattr(token, name)
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