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Назад竜 TatSu users have suggested that a simple calculator, like the one in the documentation for PLY would be useful.
Here it is.
This is the original PLY grammar for arithmetic expressions:
expression : expression + term
| expression - term
| term
term : term * factor
| term / factor
| factor
factor : NUMBER
| ( expression )
And this is the input expression for testing:
3 + 5 * ( 10 - 20 )
The first step is to convert the grammar to 竜 TatSu syntax and style, add rules for lexical elements (number in this case), add a start rule that checks for end of input, and a directive to name the generated classes:
@@grammar::CALC
start
=
expression $
;
expression
=
| expression '+' term
| expression '-' term
| term
;
term
=
| term '*' factor
| term '/' factor
| factor
;
factor
=
| '(' expression ')'
| number
;
number
=
/\d+/
;
Cut expressions make a parser commit to a particular option after certain tokens have been seen. They make parsing more efficient, because other options are not tried. They also make error messages more precise, because errors will be reported closest to the point of failure in the input.
@@grammar::CALC
start
=
expression $
;
expression
=
| expression '+' ~ term
| expression '-' ~ term
| term
;
term
=
| term '*' ~ factor
| term '/' ~ factor
| factor
;
factor
=
| '(' ~ expression ')'
| number
;
number
=
/\d+/
;
We can now compile the grammar, and test the parser:
import json
from codecs import open
from pprint import pprint
import tatsu
def simple_parse():
grammar = open('grammars/calc_cut.ebnf').read()
parser = tatsu.compile(grammar)
ast = parser.parse('3 + 5 * ( 10 - 20 )')
print('# SIMPLE PARSE')
print('# AST')
pprint(ast, width=20, indent=4)
print()
print('# JSON')
print(json.dumps(ast, indent=4))
def main():
simple_parse()
if __name__ == '__main__':
main()
This is the output:
$ PYTHONPATH=../.. python calc.py
# SIMPLE PARSE
# AST
[ '3',
'+',
[ '5',
'*',
[ '(',
[ '10',
'-',
'20'],
')']]]
# JSON
[
"3",
"+",
[
"5",
"*",
[
"(",
[
"10",
"-",
"20"
],
")"
]
]
]
Dealing with ASTs that are lists of lists leads to code that is difficult to read, and error-prone. 竜 TatSu allows naming the elements in a rule to produce more humanly-readable ASTs and to allow for clearer semantics code. This is an annotated version of the grammar:
@@grammar::CALC
start
=
expression $
;
expression
=
| left:expression op:'+' ~ right:term
| left:expression op:'-' ~ right:term
| term
;
term
=
| left:term op:'*' ~ right:factor
| left:term '/' ~ right:factor
| factor
;
factor
=
| '(' ~ @:expression ')'
| number
;
number
=
/\d+/
;
This is the resulting AST:
# ANNOTATED AST
{ 'left': '3',
'op': '+',
'right': { 'left': '5',
'op': '*',
'right': { 'left': '10',
'op': '-',
'right': '20'}}}
Semantics for 竜 TatSu parsers are not specified in the grammar, but in a separate semantics class.
from tatsu.ast import AST
class CalcBasicSemantics(object):
def number(self, ast):
return int(ast)
def term(self, ast):
if not isinstance(ast, AST):
return ast
elif ast.op == '*':
return ast.left * ast.right
elif ast.op == '/':
return ast.left / ast.right
else:
raise Exception('Unknown operator', ast.op)
def expression(self, ast):
if not isinstance(ast, AST):
return ast
elif ast.op == '+':
return ast.left + ast.right
elif ast.op == '-':
return ast.left - ast.right
else:
raise Exception('Unknown operator', ast.op)
def parse_with_basic_semantics():
grammar = open('grammars/calc_annotated.ebnf').read()
parser = tatsu.compile(grammar)
ast = parser.parse(
'3 + 5 * ( 10 - 20 )',
semantics=CalcBasicSemantics()
)
print('# BASIC SEMANTICS RESULT')
pprint(ast, width=20, indent=4)
The result is:
# BASIC SEMANTICS RESULT
-47
Having semantic actions determine what was parsed with isinstance() or querying the AST for operators is not very pythonic, nor object oriented, and it leads to code that’s more difficult to maintain. It’s preferable to have one rule per expression kind, something that will be necessary if we want to build object models to use walkers and code generation.
@@grammar::CALC
start
=
expression $
;
expression
=
| addition
| subtraction
| term
;
addition
=
left:expression op:'+' ~ right:term
;
subtraction
=
left:expression op:'-' ~ right:term
;
term
=
| multiplication
| division
| factor
;
multiplication
=
left:term op:'*' ~ right:factor
;
division
=
left:term '/' ~ right:factor
;
factor
=
| '(' ~ @:expression ')'
| number
;
number
=
/\d+/
;
class CalcSemantics(object):
def number(self, ast):
return int(ast)
def addition(self, ast):
return ast.left + ast.right
def subtraction(self, ast):
return ast.left - ast.right
def multiplication(self, ast):
return ast.left * ast.right
def division(self, ast):
return ast.left / ast.right
def parse_factored():
grammar = open('grammars/calc_factored.ebnf').read()
parser = tatsu.compile(grammar)
ast = parser.parse(
'3 + 5 * ( 10 - 20 )',
semantics=CalcSemantics()
)
print('# FACTORED SEMANTICS RESULT')
pprint(ast, width=20, indent=4)
print()
The semantics implementation is simpler, and the results are the same:
# FACTORED SEMANTICS RESULT
-47
Binding semantics to grammar rules is powerful and versatile, but this approach risks tying the semantics to the parsing process, rather than to the objects that are parsed.
That is not a problem for simple languages, like the arithmetic expression language in this tutorial. But as the complexity of the parsed language increases, the number of grammar rules quickly becomes larger than the types of objects parsed.
竜 TatSu provides for the creation of typed object models directly from the parsing process, and for the navigation (walking) and transformation (like code generation) of those models in later passes.
The first step in the creation of an object model is to annotate the grammar rule names with the desired class names for the objects parsed:
@@grammar::Calc
start
=
expression $
;
expression
=
| addition
| subtraction
| term
;
addition::Add
=
left:term op:'+' ~ right:expression
;
subtraction::Subtract
=
left:term op:'-' ~ right:expression
;
term
=
| multiplication
| division
| factor
;
multiplication::Multiply
=
left:factor op:'*' ~ right:term
;
division::Divide
=
left:factor '/' ~ right:term
;
factor
=
| subexpression
| number
;
subexpression
=
'(' ~ @:expression ')'
;
number::int
=
/\d+/
;
The tatsu.objectmodel.Node descendants are synthetized at runtime using tatsu.semantics.ModelBuilderSemantics.
This is how the model looks like when generated with the tatsu.to_python_model() function:
from tatsu.objectmodel import Node
from tatsu.semantics import ModelBuilderSemantics
class CalcModelBuilderSemantics(ModelBuilderSemantics):
def __init__(self):
types = [
t for t in globals().values()
if type(t) is type and issubclass(t, ModelBase)
]
super(CalcModelBuilderSemantics, self).__init__(types=types)
class ModelBase(Node):
pass
class Add(ModelBase):
def __init__(self,
left=None,
op=None,
right=None,
**_kwargs_):
super(Add, self).__init__(
left=left,
op=op,
right=right,
**_kwargs_
)
class Subtract(ModelBase):
def __init__(self,
left=None,
op=None,
right=None,
**_kwargs_):
super(Subtract, self).__init__(
left=left,
op=op,
right=right,
**_kwargs_
)
class Multiply(ModelBase):
def __init__(self,
left=None,
op=None,
right=None,
**_kwargs_):
super(Multiply, self).__init__(
left=left,
op=op,
right=right,
**_kwargs_
)
class Divide(ModelBase):
def __init__(self,
left=None,
right=None,
**_kwargs_):
super(Divide, self).__init__(
left=left,
right=right,
**_kwargs_
)
The model that results from a parse can be printed, and walked:
from tatsu.walkers import NodeWalker
class CalcWalker(NodeWalker):
def walk_object(self, node):
return node
def walk__add(self, node):
return self.walk(node.left) + self.walk(node.right)
def walk__subtract(self, node):
return self.walk(node.left) - self.walk(node.right)
def walk__multiply(self, node):
return self.walk(node.left) * self.walk(node.right)
def walk__divide(self, node):
return self.walk(node.left) / self.walk(node.right)
def parse_and_walk_model():
grammar = open('grammars/calc_model.ebnf').read()
parser = tatsu.compile(grammar, asmodel=True)
model = parser.parse('3 + 5 * ( 10 - 20 )')
print('# WALKER RESULT IS:')
print(CalcWalker().walk(model))
print()
The above program produces this result:
# WALKER RESULT IS:
-47
Translation is one of the most common tasks in language processing. Analysis often sumarizes the parsed input, and walkers are good for that. In translation, the output can often be as verbose as the input, so a systematic approach that avoids bookkeeping as much as possible is convenient.
竜 TatSu provides support for template-based code generation (translation) in the tatsu.codegen module. Code generation works defining a translation class for each class in the model specified by the grammar.
The following code generator translates input expressions to the postfix instructions of a stack-based processor:
from tatsu.codegen import ModelRenderer
from tatsu.codegen import CodeGenerator
THIS_MODULE = sys.modules[__name__]
class PostfixCodeGenerator(CodeGenerator):
def __init__(self):
super(PostfixCodeGenerator, self).__init__(modules=[THIS_MODULE])
class Number(ModelRenderer):
template = '''\
PUSH {value}'''
class Add(ModelRenderer):
template = '''\
{left}
{right}
ADD'''
class Subtract(ModelRenderer):
template = '''\
{left}
{right}
SUB'''
class Multiply(ModelRenderer):
template = '''\
{left}
{right}
MUL'''
class Divide(ModelRenderer):
template = '''\
{left}
{right}
DIV'''
The code generator can be used thus:
from codegen import PostfixCodeGenerator
def parse_and_translate():
grammar = open('grammars/calc_model.ebnf').read()
parser = tatsu.compile(grammar, asmodel=True)
model = parser.parse('3 + 5 * ( 10 - 20 )')
postfix = PostfixCodeGenerator().render(model)
print('# TRANSLATED TO POSTFIX')
print(postfix)
Which results in:
# TRANSLATED TO POSTFIX
PUSH 3
PUSH 5
PUSH 10
PUSH 20
SUB
MUL
ADD