Under python 3.3+, logging.warn is deprecated.
Use logging.warning instead.

Fixes: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/lark/parsers/lalr_analysis.py:87: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead

- Makes rule ordering the default ambiguity tie breaker.
    E.g.
    start: a | b
    a: "A"
    b: "A"

    will return:
    start
        a

    start: b | a
    a: "A"
    b: "A"

    will return
    start
        b

- Replaces the ambiguity='resolve__antiscore_sum' with a separate option: 'priority'.
    The priority option has 4 values: 'auto', 'none', 'normal', 'invert'.
    'Auto' maps to 'Normal' for CYK and Earley and 'None' for LALR.
    'None' filters your priorities and ignores them. This saves some extra tree walking on Earley.
    'Normal' uses your priorities untouched, mimicing the old behaviour.
    'Invert' negates your priorities, emulating the old 'resolve__antiscore_sum' behaviour.

    This allows you to use priority logic even when ambiguity=='explicit', to get a better idea
    of the shape of your tree; and to easily disable priorities without removing them from the
    grammar for testing (or performance).

- ambiguity='explicit' now correctly returns an ambiguous tree again, as 0.6 did.

Avoids recursion and improves performance slightly

We can replace Earley Columns with basic python sets
for improved performance and simplicity.

When using the same parser repeatedly for small parsers we incur
significant overhead by recreating the ForestVisitor each parser.
We can cache the Forest walker and re-use it by making it stateless.
Also, we can use slots for all of the Forest Walkers to reduce
construction delay and function call overhead.

types of ambiguity.
 - Rewritten along the lines of Elizabeth Scott's parser.
https://www.sciencedirect.com/science/article/pii/S1571066108001497

 - Implement SPPF trees per Elizabeth Scott and Bram van der Sanden's work.
http://www.bramvandersanden.com/post/2014/06/shared-packed-parse-forest/