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How to use seems_right method in hypothesis

Best Python code snippet using hypothesis

lstar.py

Source:lstar.py

...258        # any of our transitions are wrong.259        while True:260            dfa = self.dfa261            states = [dfa.start]262            def seems_right(n):263                """After reading n characters from s, do we seem to be264                in the right state?265                We determine this by replacing the first n characters266                of s with the label of the state we expect to be in.267                If we are in the right state, that will replace a substring268                with an equivalent one so must produce the same answer.269                """270                if n > len(string):271                    return False272                # Populate enough of the states list to know where we are.273                while n >= len(states):274                    states.append(dfa.transition(states[-1], string[len(states) - 1]))275                return self.member(dfa.label(states[n]) + string[n:]) == correct_outcome276            assert seems_right(0)277            n = find_integer(seems_right)278            # We got to the end without ever finding ourself in a bad279            # state, so we must correctly match this string.280            if n == len(string):281                assert dfa.matches(string) == correct_outcome282                break283            # Reading n characters does not put us in a bad state but284            # reading n + 1 does. This means that the remainder of285            # the string that we have not read yet is an experiment286            # that allows us to distinguish the state that we ended287            # up in from the state that we should have ended up in.288            source = states[n]289            character = string[n]290            wrong_destination = states[n + 1]...

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