def test_direct_rule_call() -> None:
"""
Test regression where in direct rule call semantic action is
erroneously attached to both caller and callee.
"""
def grammar(): return rule1, rule2
def rule1(): return "a"
def rule2(): return rule1
call_count = [0]
class DummySemAction(SemanticAction):
def first_pass(self, parser, node, nodes):
call_count[0] += 1
return SemanticAction.first_pass(self, parser, node, nodes)
# Sem action is attached to rule2 only but
# this bug will attach it to rule1 also resulting in
# wrong call count.
rule2.sem = DummySemAction() # type: ignore
parser = ParserPython(grammar)
parse_tree = parser.parse("aa")
parser.getASG()
assert call_count[0] == 1, "Semantic action should be called once!"
def test_direct_rule_call():
'''
Test regression where in direct rule call semantic action is
erroneously attached to both caller and callee.
'''
def grammar(): return rule1, rule2
def rule1(): return "a"
def rule2(): return rule1
call_count = [0]
class DummySemAction(SemanticAction):
def first_pass(self, parser, node, nodes):
call_count[0] += 1
return SemanticAction.first_pass(self, parser, node, nodes)
# Sem action is attached to rule2 only but
# this bug will attach it to rule1 also resulting in
# wrong call count.
rule2.sem = DummySemAction()
parser = ParserPython(grammar)
parse_tree = parser.parse("aa")
parser.getASG()
assert call_count[0] == 1, "Semantic action should be called once!"
def test_default_action_disabled():
parser = ParserPython(grammar)
parser.parse('(-34) strmatch')
parser.getASG(defaults=False)
assert not p_removed
assert not number_str
assert parse_tree_node
def test_semantic_action_results():
global first_sar, third_sar
input = "4 3 3 3 a 3 3 b"
parser = ParserPython(grammar, reduce_tree=False)
result = parser.parse(input)
PTDOTExporter().exportFile(result, 'test_semantic_action_results_pt.dot')
parser.getASG()
assert isinstance(first_sar, SemanticActionResults)
assert len(first_sar.third) == 3
assert third_sar.third_str[0] == '3'
def main(debug=False):
parser = ParserPython(initial, debug=debug)
file_input = open("input.txt", 'r')
input_expr = file_input.read()
parse_tree = parser.parse(input_expr)
result = parser.getASG()
def main(debug=False):
# First we will make a parser - an instance of the calc parser model.
# Parser model is given in the form of python constructs therefore we
# are using ParserPython class.
parser = ParserPython(calc, debug=debug)
# An expression we want to evaluate
input_expr = "-(4-1)*5+(2+4.67)+5.89/(.2+7)"
# We create a parse tree out of textual input_expr
parse_tree = parser.parse(input_expr)
result = parser.getASG()
if debug:
# getASG will start semantic analysis.
# In this case semantic analysis will evaluate expression and
# returned value will be the result of the input_expr expression.
print("{} = {}".format(input_expr, result))
def main(debug=False):
# First we will make a parser - an instance of the calc parser model.
# Parser model is given in the form of python constructs therefore we
# are using ParserPython class.
parser = ParserPython(calc, debug=debug)
# An expression we want to evaluate
input_expr = "-(4-1)*5+(2+4.67)+5.89/(.2+7)"
# We create a parse tree out of textual input_expr
parse_tree = parser.parse(input_expr)
result = parser.getASG()
if debug:
# getASG will start semantic analysis.
# In this case semantic analysis will evaluate expression and
# returned value will be the result of the input_expr expression.
print("{} = {}".format(input_expr, result))
def language_from_str(language_def, metamodel):
"""
Constructs parser and initializes metamodel from language description
given in textX language.
Args:
language_def (str): A language description in textX.
metamodel (TextXMetaModel): A metamodel to initialize.
Returns:
Parser for the new language.
"""
if metamodel.debug:
metamodel.dprint("*** PARSING LANGUAGE DEFINITION ***")
# Check the cache for already conctructed textX parser
if metamodel.debug in textX_parsers:
parser = textX_parsers[metamodel.debug]
else:
# Create parser for TextX descriptions from
# the textX grammar specified in this module
parser = ParserPython(textx_model,
comment_def=comment,
ignore_case=False,
reduce_tree=False,
debug=metamodel.debug)
# Prepare regex used in keyword-like strmatch detection.
# See str_match_SA
flags = 0
if metamodel.ignore_case:
flags = re.IGNORECASE
parser.keyword_regex = re.compile(r'[^\d\W]\w*', flags)
# Cache it for subsequent calls
textX_parsers[metamodel.debug] = parser
# This is used during parser construction phase.
# Metamodel is filled in. Classes are created based on grammar rules.
parser.metamodel = metamodel
# Builtin rules representing primitive types
parser.root_rule_name = None
# Parse language description with textX parser
try:
parser.parse(language_def)
except NoMatch as e:
line, col = parser.pos_to_linecol(e.position)
raise TextXSyntaxError(text(e), line, col)
# Construct new parser based on the given language description.
lang_parser = parser.getASG()
# Meta-model is constructed. Validate semantics of metamodel.
parser.metamodel.validate()
# Meta-model is constructed. Here we connect meta-model and language
# parser for convenience.
lang_parser.metamodel = parser.metamodel
metamodel.parser = lang_parser
if metamodel.debug:
# Create dot file for debuging purposes
PMDOTExporter().exportFile(
lang_parser.parser_model,
"{}_parser_model.dot".format(metamodel.rootcls.__name__))
return lang_parser
def language_from_str(language_def, metamodel):
"""
Constructs parser and initializes metamodel from language description
given in textX language.
Args:
language_def (str): A language description in textX.
metamodel (TextXMetaModel): A metamodel to initialize.
Returns:
Parser for the new language.
"""
if metamodel.debug:
metamodel.dprint("*** PARSING LANGUAGE DEFINITION ***")
# Check the cache for already conctructed textX parser
if metamodel.debug in textX_parsers:
parser = textX_parsers[metamodel.debug]
else:
# Create parser for TextX descriptions from
# the textX grammar specified in this module
parser = ParserPython(textx_model, comment_def=comment,
ignore_case=False,
reduce_tree=False, debug=metamodel.debug)
# Prepare regex used in keyword-like strmatch detection.
# See str_match_SA
flags = 0
if metamodel.ignore_case:
flags = re.IGNORECASE
parser.keyword_regex = re.compile(r'[^\d\W]\w*', flags)
# Cache it for subsequent calls
textX_parsers[metamodel.debug] = parser
# This is used during parser construction phase.
# Metamodel is filled in. Classes are created based on grammar rules.
parser.metamodel = metamodel
# Builtin rules representing primitive types
parser.root_rule_name = None
# Parse language description with textX parser
try:
parser.parse(language_def)
except NoMatch as e:
line, col = parser.pos_to_linecol(e.position)
raise TextXSyntaxError(text(e), line, col)
# Construct new parser based on the given language description.
lang_parser = parser.getASG()
# Meta-model is constructed. Validate semantics of metamodel.
parser.metamodel.validate()
# Meta-model is constructed. Here we connect meta-model and language
# parser for convenience.
lang_parser.metamodel = parser.metamodel
metamodel.parser = lang_parser
if metamodel.debug:
# Create dot file for debuging purposes
PMDOTExporter().exportFile(
lang_parser.parser_model,
"{}_parser_model.dot".format(metamodel.rootcls.__name__))
return lang_parser
