def test_topological_sort(self) -> None:
"""
Test that topological sort works properly.
"""
# tuple convention: (outgoing, incoming)
graph = {
"1": (["4"], []),
"2": (["4"], []),
"3": (["5", "6"], []),
"4": (["7", "5"], ["1", "2"]),
"5": (["8"], ["4", "3"]),
"6": ([], ["3"]),
"7": (["8"], ["4"]),
"8": ([], ["7", "5"])
} # type: Dict[str, Tuple[List[str], List[str]]]
self.assertEqual(topological_sort(graph, ["1", "2", "3"]), ["1", "2", "3", "4", "6", "7", "5", "8"])
def test_topological_sort(self) -> None:
"""
Test that topological sort works properly.
"""
# tuple convention: (outgoing, incoming)
graph = {
"1": (["4"], []),
"2": (["4"], []),
"3": (["5", "6"], []),
"4": (["7", "5"], ["1", "2"]),
"5": (["8"], ["4", "3"]),
"6": ([], ["3"]),
"7": (["8"], ["4"]),
"8": ([], ["7", "5"])
} # type: Dict[str, Tuple[List[str], List[str]]]
self.assertEqual(topological_sort(graph, ["1", "2", "3"]), ["1", "2", "3", "4", "6", "7", "5", "8"])
def combine_configs(configs: Iterable[dict]) -> dict:
"""
Combine the given list of *unpacked* configs into a single config.
Later configs in the list will override the earlier configs.
:param configs: List of configs.
:param handle_meta: Handle meta configs?
:return: A loaded config dictionary.
"""
expanded_config_reduce = reduce(update_and_expand_meta, configs,
{}) # type: dict
expanded_config = deepdict(expanded_config_reduce) # type: dict
expanded_config_orig = deepdict(expanded_config) # type: dict
# Now, we need to handle lazy* metas.
lazy_metas = {}
meta_dict_keys = list(expanded_config.keys())
meta_keys = list(filter(lambda k: k.endswith("_meta"), meta_dict_keys))
# Graph to keep track of which lazy settings depend on others.
# key1 -> key2 means key2 depends on key1
graph = {} # type: Dict[str, Tuple[List[str], List[str]]]
meta_len = len("_meta")
for meta_key in meta_keys:
setting = meta_key[:-meta_len] # type: str
lazy_meta_type = expanded_config[meta_key] # type: str
assert lazy_meta_type.startswith(
"lazy"), "Should have only lazy metas left now"
# Create lazy_metas without the lazy part.
# e.g. what used to be a lazysubst just becomes a plain subst since everything is fully resolved now.
meta_type = lazy_meta_type[len("lazy"):]
lazy_metas[meta_key] = meta_type
lazy_metas[setting] = expanded_config[
setting] # copy over the template too
# Build the graph of which lazy settings depend on what.
# Always ensure that this lazy setting's node exists even if it has no dependencies.
if setting not in graph:
graph[setting] = ([], [])
for target_var in get_meta_directives()[meta_type].target_settings(
setting, expanded_config[setting]):
# Make sure the order in which we delete doesn't affect this
# search, since expanded_config might have some deleted stuff.
if target_var + "_meta" in expanded_config_orig:
# Add a dependency for target -> this setting
if target_var not in graph:
graph[target_var] = ([], [])
graph[target_var][0].append(setting)
graph[setting][1].append(target_var)
else:
# The target setting that this depends on is not a lazy setting.
pass
# Delete from expanded_config
del expanded_config[meta_key]
del expanded_config[setting]
if len(graph) > 0:
# Find all the starting nodes (no incoming edges).
starting_nodes = list(
map(lambda key_val: key_val[0],
filter(lambda key_val: len(key_val[1][1]) == 0,
graph.items())))
# Sort starting nodes for determinism.
starting_nodes = sorted(starting_nodes)
if len(starting_nodes) == 0:
raise ValueError("There appears to be a loop of lazy settings")
# List of settings to expand first according to topological sort.
settings_ordered = topological_sort(graph,
starting_nodes) # type: List[str]
def combine_meta(config_dict: dict, meta_setting: str) -> dict:
# Merge in the metas in the given order.
return update_and_expand_meta(
config_dict, {
meta_setting: lazy_metas[meta_setting],
meta_setting + "_meta": lazy_metas[meta_setting + "_meta"]
})
final_dict = reduce(combine_meta, settings_ordered,
expanded_config) # type: dict
else:
final_dict = deepdict(expanded_config)
# Remove any temporary keys.
for key in HammerDatabase.internal_keys():
if key in final_dict:
del final_dict[key]
return final_dict
def combine_configs(configs: Iterable[dict]) -> dict:
"""
Combine the given list of *unpacked* configs into a single config.
Later configs in the list will override the earlier configs.
:param configs: List of configs.
:param handle_meta: Handle meta configs?
:return: A loaded config dictionary.
"""
expanded_config_reduce = reduce(update_and_expand_meta, configs, {}) # type: dict
expanded_config = deepdict(expanded_config_reduce) # type: dict
expanded_config_orig = deepdict(expanded_config) # type: dict
# Now, we need to handle lazy* metas.
lazy_metas = {}
meta_dict_keys = list(expanded_config.keys())
meta_keys = list(filter(lambda k: k.endswith("_meta"), meta_dict_keys))
# Graph to keep track of which lazy settings depend on others.
# key1 -> key2 means key2 depends on key1
graph = {} # type: Dict[str, Tuple[List[str], List[str]]]
meta_len = len("_meta")
for meta_key in meta_keys:
setting = meta_key[:-meta_len] # type: str
lazy_meta_type = expanded_config[meta_key] # type: str
assert lazy_meta_type.startswith("lazy"), "Should have only lazy metas left now"
# Create lazy_metas without the lazy part.
# e.g. what used to be a lazysubst just becomes a plain subst since everything is fully resolved now.
meta_type = lazy_meta_type[len("lazy"):]
lazy_metas[meta_key] = meta_type
lazy_metas[setting] = expanded_config[setting] # copy over the template too
# Build the graph of which lazy settings depend on what.
# Always ensure that this lazy setting's node exists even if it has no dependencies.
if setting not in graph:
graph[setting] = ([], [])
for target_var in get_meta_directives()[meta_type].target_settings(setting, expanded_config[setting]):
# Make sure the order in which we delete doesn't affect this
# search, since expanded_config might have some deleted stuff.
if target_var + "_meta" in expanded_config_orig:
# Add a dependency for target -> this setting
if target_var not in graph:
graph[target_var] = ([], [])
graph[target_var][0].append(setting)
graph[setting][1].append(target_var)
else:
# The target setting that this depends on is not a lazy setting.
pass
# Delete from expanded_config
del expanded_config[meta_key]
del expanded_config[setting]
if len(graph) > 0:
# Find all the starting nodes (no incoming edges).
starting_nodes = list(
map(lambda key_val: key_val[0], filter(lambda key_val: len(key_val[1][1]) == 0, graph.items())))
# Sort starting nodes for determinism.
starting_nodes = sorted(starting_nodes)
if len(starting_nodes) == 0:
raise ValueError("There appears to be a loop of lazy settings")
# List of settings to expand first according to topological sort.
settings_ordered = topological_sort(graph, starting_nodes) # type: List[str]
def combine_meta(config_dict: dict, meta_setting: str) -> dict:
# Merge in the metas in the given order.
return update_and_expand_meta(config_dict, {
meta_setting: lazy_metas[meta_setting],
meta_setting + "_meta": lazy_metas[meta_setting + "_meta"]
})
final_dict = reduce(combine_meta, settings_ordered, expanded_config) # type: dict
else:
final_dict = deepdict(expanded_config)
# Remove any temporary keys.
for key in HammerDatabase.internal_keys():
if key in final_dict:
del final_dict[key]
return final_dict
