# Buggy code
def get_parfor_reductions(
    parfor,
    parfor_params,
    calltypes,
    reductions=None,
    reduce_varnames=None,
    param_uses=None,
    param_nodes=None,
    var_to_param=None,
):
    """find variables that are updated using their previous values and an array
    item accessed with parfor index, e.g. s = s+A[i]
    """
    if reductions is None:
        reductions = {}
    if reduce_varnames is None:
        reduce_varnames = []

    # for each param variable, find what other variables are used to update it
    # also, keep the related nodes
    if param_uses is None:
        param_uses = defaultdict(list)
    if param_nodes is None:
        param_nodes = defaultdict(list)
    if var_to_param is None:
        var_to_param = {}

    blocks = wrap_parfor_blocks(parfor)
    topo_order = find_topo_order(blocks)
    topo_order = topo_order[1:]  # ignore init block
    unwrap_parfor_blocks(parfor)

    for label in reversed(topo_order):
        for stmt in reversed(parfor.loop_body[label].body):
            if isinstance(stmt, ir.Assign) and (
                stmt.target.name in parfor_params or stmt.target.name in var_to_param
            ):
                lhs = stmt.target.name
                rhs = stmt.value
                cur_param = lhs if lhs in parfor_params else var_to_param[lhs]
                used_vars = []
                if isinstance(rhs, ir.Var):
                    used_vars = [rhs.name]
                elif isinstance(rhs, ir.Expr):
                    used_vars = [v.name for v in stmt.value.list_vars()]
                param_uses[cur_param].extend(used_vars)
                for v in used_vars:
                    var_to_param[v] = cur_param
                # save copy of dependent stmt
                stmt_cp = copy.deepcopy(stmt)
                if stmt.value in calltypes:
                    calltypes[stmt_cp.value] = calltypes[stmt.value]
                param_nodes[cur_param].append(stmt_cp)
            if isinstance(stmt, Parfor):
                # recursive parfors can have reductions like test_prange8
                get_parfor_reductions(
                    stmt,
                    parfor_params,
                    calltypes,
                    reductions,
                    reduce_varnames,
                    param_uses,
                    param_nodes,
                    var_to_param,
                )
    for param, used_vars in param_uses.items():
        # a parameter is a reduction variable if its value is used to update it
        # check reduce_varnames since recursive parfors might have processed
        # param already
        if param in used_vars and param not in reduce_varnames:
            reduce_varnames.append(param)
            param_nodes[param].reverse()
            reduce_nodes = get_reduce_nodes(param, param_nodes[param])
            gri_out = guard(get_reduction_init, reduce_nodes)
            if gri_out is not None:
                init_val, redop = gri_out
            else:
                init_val = None
                redop = None
            reductions[param] = (init_val, reduce_nodes, redop)
    return reduce_varnames, reductions


# Traceback (AssertionError)
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/errors.py in new_error_context(fmt_, *args, **kwargs)
661     try:
--> 662         yield
663     except NumbaError as e:

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/lowering.py in lower_block(self, block)
257                                    loc=self.loc, errcls_=defaulterrcls):
--> 258                 self.lower_inst(inst)
259

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/lowering.py in lower_inst(self, inst)
409                 if isinstance(inst, _class):
--> 410                     func(self, inst)
411                     return

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/npyufunc/parfor.py in _lower_parfor_parallel(lowerer, parfor)
97     parfor_redvars, parfor_reddict = numba.parfor.get_parfor_reductions(
---> 98         parfor, parfor.params, lowerer.fndesc.calltypes)
99

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/parfor.py in get_parfor_reductions(parfor, parfor_params, calltypes, reductions, reduce_varnames, param_uses, param_nodes, var_to_param)
2968             param_nodes[param].reverse()
-> 2969             reduce_nodes = get_reduce_nodes(param, param_nodes[param])
2970             init_val = guard(get_reduction_init, reduce_nodes)

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/parfor.py in get_reduce_nodes(name, nodes)
3017                 non_red_args = [ x for (x, y) in args if y.name != name ]
-> 3018                 assert len(non_red_args) == 1
3019                 args = [ (x, y) for (x, y) in args if x != y.name ]

AssertionError:

During handling of the above exception, another exception occurred:

LoweringError                             Traceback (most recent call last)
<ipython-input-79-3ff90f149e7e> in <module>
1 n_sims = 100
----> 2 level_data_simulations = run_simulations(n_players, n_sims, t_levels, t_player, player, max_level)

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/dispatcher.py in _compile_for_args(self, *args, **kws)
393                     e.patch_message(''.join(e.args) + help_msg)
394             # ignore the FULL_TRACEBACKS config, this needs reporting!
--> 395             raise e
396
397     def inspect_llvm(self, signature=None):

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/dispatcher.py in _compile_for_args(self, *args, **kws)
350                 argtypes.append(self.typeof_pyval(a))
351         try:
--> 352             return self.compile(tuple(argtypes))
353         except errors.TypingError as e:
354             # Intercept typing error that may be due to an argument

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler_lock.py in _acquire_compile_lock(*args, **kwargs)
30         def _acquire_compile_lock(*args, **kwargs):
31             with self:
---> 32                 return func(*args, **kwargs)
33         return _acquire_compile_lock
34

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/dispatcher.py in compile(self, sig)
691
692             self._cache_misses[sig] += 1
--> 693             cres = self._compiler.compile(args, return_type)
694             self.add_overload(cres)
695             self._cache.save_overload(sig, cres)

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/dispatcher.py in compile(self, args, return_type)
74
75     def compile(self, args, return_type):
---> 76         status, retval = self._compile_cached(args, return_type)
77         if status:
78             return retval

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/dispatcher.py in _compile_cached(self, args, return_type)
88
89         try:
---> 90             retval = self._compile_core(args, return_type)
91         except errors.TypingError as e:
92             self._failed_cache[key] = e

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/dispatcher.py in _compile_core(self, args, return_type)
106                                       args=args, return_type=return_type,
107                                       flags=flags, locals=self.locals,
--> 108                                       pipeline_class=self.pipeline_class)
109         # Check typing error if object mode is used
110         if cres.typing_error is not None and not flags.enable_pyobject:

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in compile_extra(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)
970     pipeline = pipeline_class(typingctx, targetctx, library,
971                               args, return_type, flags, locals)
--> 972     return pipeline.compile_extra(func)
973
974

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in compile_extra(self, func)
388         self.lifted = ()
389         self.lifted_from = None
--> 390         return self._compile_bytecode()
391
392     def compile_ir(self, func_ir, lifted=(), lifted_from=None):

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in _compile_bytecode(self)
901         """
902         assert self.func_ir is None
--> 903         return self._compile_core()
904
905     def _compile_ir(self):

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in _compile_core(self)
888         self.define_pipelines(pm)
889         pm.finalize()
--> 890         res = pm.run(self.status)
891         if res is not None:
892             # Early pipeline completion

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler_lock.py in _acquire_compile_lock(*args, **kwargs)
30         def _acquire_compile_lock(*args, **kwargs):
31             with self:
---> 32                 return func(*args, **kwargs)
33         return _acquire_compile_lock
34

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in run(self, status)
264                     # No more fallback pipelines?
265                     if is_final_pipeline:
--> 266                         raise patched_exception
267                     # Go to next fallback pipeline
268                     else:

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in run(self, status)
255                 try:
256                     event("-- %s" % stage_name)
--> 257                     stage()
258                 except _EarlyPipelineCompletion as e:
259                     return e.result

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in stage_nopython_backend(self)
762         """
763         lowerfn = self.backend_nopython_mode
--> 764         self._backend(lowerfn, objectmode=False)
765
766     def stage_compile_interp_mode(self):

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in _backend(self, lowerfn, objectmode)
701             self.library.enable_object_caching()
702
--> 703         lowered = lowerfn()
704         signature = typing.signature(self.return_type, *self.args)
705         self.cr = compile_result(

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in backend_nopython_mode(self)
688                 self.calltypes,
689                 self.flags,
--> 690                 self.metadata)
691
692     def _backend(self, lowerfn, objectmode):

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/compiler.py in native_lowering_stage(targetctx, library, interp, typemap, restype, calltypes, flags, metadata)
1141         lower = lowering.Lower(targetctx, library, fndesc, interp,
1142                                metadata=metadata)
-> 1143         lower.lower()
1144         if not flags.no_cpython_wrapper:
1145             lower.create_cpython_wrapper(flags.release_gil)

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/lowering.py in lower(self)
175         if self.generator_info is None:
176             self.genlower = None
--> 177             self.lower_normal_function(self.fndesc)
178         else:
179             self.genlower = self.GeneratorLower(self)

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/lowering.py in lower_normal_function(self, fndesc)
216         # Init argument values
217         self.extract_function_arguments()
--> 218         entry_block_tail = self.lower_function_body()
219
220         # Close tail of entry block

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/lowering.py in lower_function_body(self)
241             bb = self.blkmap[offset]
242             self.builder.position_at_end(bb)
--> 243             self.lower_block(block)
244
245         self.post_lower()

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/lowering.py in lower_block(self, block)
256             with new_error_context('lowering "{inst}" at {loc}', inst=inst,
257                                    loc=self.loc, errcls_=defaulterrcls):
--> 258                 self.lower_inst(inst)
259
260     def create_cpython_wrapper(self, release_gil=False):

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/contextlib.py in __exit__(self, type, value, traceback)
128                 value = type()
129             try:
--> 130                 self.gen.throw(type, value, traceback)
131             except StopIteration as exc:
132                 # Suppress StopIteration *unless* it's the same exception that

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/errors.py in new_error_context(fmt_, *args, **kwargs)
668         from numba import config
669         tb = sys.exc_info()[2] if config.FULL_TRACEBACKS else None
--> 670         six.reraise(type(newerr), newerr, tb)
671
672

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numba/six.py in reraise(tp, value, tb)
657         if value.__traceback__ is not tb:
658             raise value.with_traceback(tb)
--> 659         raise value
660
661 else:

LoweringError: Failed in nopython mode pipeline (step: nopython mode backend)


File "<ipython-input-78-9c8bcba8955a>", line 4:
def run_simulations(n_p, n_s, t_lvl, t_plr, plr,mx_lvl):
<source elided>
#    lvl_sims = np.empty(shape=(np.shape(lvl)[0], np.shape(lvl)[1], n_s))
for s in prange(n_s):
^

[1] During: lowering "id=0[LoopNest(index_variable = parfor_index.10, range = (0, n_s, 1))]{12: <ir.Block at <ipython-input-78-9c8bcba8955a> (4)>}Var(parfor_index.10, <ipython-input-78-9c8bcba8955a> (4))" at <ipython-input-78-9c8bcba8955a> (4)
-------------------------------------------------------------------------------
This should not have happened, a problem has occurred in Numba's internals.
You are currently using Numba version 0.45.1.

Please report the error message and traceback, along with a minimal reproducer
at: https://github.com/numba/numba/issues/new

If more help is needed please feel free to speak to the Numba core developers
directly at: https://gitter.im/numba/numba

Thanks in advance for your help in improving Numba!

# Fixed code
def get_parfor_reductions(
    func_ir,
    parfor,
    parfor_params,
    calltypes,
    reductions=None,
    reduce_varnames=None,
    param_uses=None,
    param_nodes=None,
    var_to_param=None,
):
    """find variables that are updated using their previous values and an array
    item accessed with parfor index, e.g. s = s+A[i]
    """
    if reductions is None:
        reductions = {}
    if reduce_varnames is None:
        reduce_varnames = []

    # for each param variable, find what other variables are used to update it
    # also, keep the related nodes
    if param_uses is None:
        param_uses = defaultdict(list)
    if param_nodes is None:
        param_nodes = defaultdict(list)
    if var_to_param is None:
        var_to_param = {}

    blocks = wrap_parfor_blocks(parfor)
    topo_order = find_topo_order(blocks)
    topo_order = topo_order[1:]  # ignore init block
    unwrap_parfor_blocks(parfor)

    for label in reversed(topo_order):
        for stmt in reversed(parfor.loop_body[label].body):
            if isinstance(stmt, ir.Assign) and (
                stmt.target.name in parfor_params or stmt.target.name in var_to_param
            ):
                lhs = stmt.target.name
                rhs = stmt.value
                cur_param = lhs if lhs in parfor_params else var_to_param[lhs]
                used_vars = []
                if isinstance(rhs, ir.Var):
                    used_vars = [rhs.name]
                elif isinstance(rhs, ir.Expr):
                    used_vars = [v.name for v in stmt.value.list_vars()]
                param_uses[cur_param].extend(used_vars)
                for v in used_vars:
                    var_to_param[v] = cur_param
                # save copy of dependent stmt
                stmt_cp = copy.deepcopy(stmt)
                if stmt.value in calltypes:
                    calltypes[stmt_cp.value] = calltypes[stmt.value]
                param_nodes[cur_param].append(stmt_cp)
            if isinstance(stmt, Parfor):
                # recursive parfors can have reductions like test_prange8
                get_parfor_reductions(
                    func_ir,
                    stmt,
                    parfor_params,
                    calltypes,
                    reductions,
                    reduce_varnames,
                    param_uses,
                    param_nodes,
                    var_to_param,
                )
    for param, used_vars in param_uses.items():
        # a parameter is a reduction variable if its value is used to update it
        # check reduce_varnames since recursive parfors might have processed
        # param already
        if param in used_vars and param not in reduce_varnames:
            reduce_varnames.append(param)
            param_nodes[param].reverse()
            reduce_nodes = get_reduce_nodes(param, param_nodes[param], func_ir)
            gri_out = guard(get_reduction_init, reduce_nodes)
            if gri_out is not None:
                init_val, redop = gri_out
            else:
                init_val = None
                redop = None
            reductions[param] = (init_val, reduce_nodes, redop)
    return reduce_varnames, reductions