diff --git a/other/franca/rule_translator.py b/other/franca/rule_translator.py index 5dc5280..b8466b0 100644 --- a/other/franca/rule_translator.py +++ b/other/franca/rule_translator.py @@ -50,10 +50,7 @@ # does not work in our current translation table format because it is a key in a dict. Plain arrays are not a hashable # value and therefore can't be used as a key. Similarly list(SomeClass) -> a type is not hashable. -# (Use frozen dataclasses to make them hashable. The attributes are given values at construction time _only_.) -@dataclass(frozen=True) -class ListOf: - itemtype: type +# (Use frozen dataclasses to make them hashable. The attributes are given values at construction time only.) # Map to Unsupported to make a node type unsupported @dataclass(frozen=True) @@ -99,8 +96,11 @@ class Preparation: # Here an array of tuples is used. (Format is subject to change) # (FROM-class on the left, TO-class on the right) # *optional* transformation function as third argument - [ ('thiss', 'thatt'), + # Special case: Preparation(myfunc), which calls any function at that point in the list + [ + ('thiss', 'thatt'), ('name', 'thename', capitalize_name_string), + Preparation(pre_counter_init), ('zero_based_counter', 'one_based_counter', lambda x : x + 1), ('thing', None) ] @@ -116,20 +116,6 @@ class Preparation: } """ -# ----------------------------------------------------------------------------- -# In the following table it is possible to list additional functions that are required but cannot be covered by the -# one-to-one object mapping above. A typical example is to recursively loop over a major container, *and* its children -# containers create a flat list of items. Non-obvious mappings can be handled by processing the AST several times. -# Example: if in the input AST has typedefs defined on the global scope, as well as inside of a namespace/interface, but -# in the output AST we want them all collected on a global scope, then the direct mapping between AST objects does not -# apply well since that only creates a result that is analogous to the structure of the input AST. -# ----------------------------------------------------------------------------- - -# NOTE: This is not yet implemented -> when the need arises -ast_translation = { - -} - # ---------------------------------------------------------------------------- # HELPER FUNCTIONS # ---------------------------------------------------------------------------- @@ -146,23 +132,6 @@ def _log(level, string): def is_builtin(x): return x.__class__.__module__ == 'builtins' -# This is really supposed to check if the instance is one of the AST classes, or possibly it could check if it is a class -# defined in the mapping table. For now, however, this simple check for "not builtin" works well enough. -def is_composite_object(mapping_table, x): - return not is_builtin(x) - -# FIXME: Unused, but could be used for error checking -def has_mapping(mapping_table, x): - return mapping_table['type_map'].get(x.__class__) is not None - -# flatmap: Call function for each item in input_array, and flatten the result -# into one array. The passed function is expected to return an array for each call. -def flatmap(function, input_array): - return [y for x in input_array for y in function(x)] - -def underscore_combine_name(parent, item): - return parent + "_" + item - # This function is used by the general translation to handle multiple mappings with the same target attribute. # We don't want to overwrite and destroy the previous value with a new one, and if the target is a list # then it is useful to be able to add to that list at multiple occasions -> append to it. @@ -193,9 +162,9 @@ def set_attr(attrs_dict, attr_key, attr_value): # be optional, like: # first, second, *maybe_more = (...tuple...) # But it's preferrable that a single item, like Preparation(), should not need to be a tuple at all, -# so let's add some logic: - -# Returns: (preparation_function, input_arg, output_arg, field_transform) +# so let's add some logic. +# This one always returns the full 4-value tuple: +# (preparation_function, input_arg, output_arg, field_transform) def eval_mapping(type_map_entry): if isinstance(type_map_entry, Preparation): @@ -208,41 +177,48 @@ def eval_mapping(type_map_entry): return (None, input_arg, output_arg, field_transform) -# Common code - how to handle different composite value types, and lists +# Additional named helpers to make logic very visible. +# (We're at this time not concerned with performance hit of calling some extra functions) +def dataclass_has_field(_class, attr): + return attr in _class.__dataclass_fields__ + +# The following two functions are mutually recursive (transform -> transform_value_common -> transform) +# but you can think of it primarily as the main function, transform(), calling itself as it +# decends down the tree of nodes/values that neeed converting. +# This _common function is here only to avoid repeated code for the type-specific handling def transform_value_common(mapping_table, value, field_transform): - # OrderedDict is used at least by Franca AST + + # OrderedDict is used at least by Franca AST -> return a list of transformed items if isinstance(value, OrderedDict): value = [transform(mapping_table, item) for name, item in value.items()] + # A list in input yields a list in output, transforming each item elif isinstance(value, list): value = [transform(mapping_table, item) for item in value] - else: # Plain attribute -> use transformation function if it was defined + # Plain attribute -> use transformation function if it was defined + else: value = field_transform(value) return value -# Additional named helpers to make logic very visible. -# (We're at this time not concerned with performance hit of calling some extra functions) -def dataclass_has_field(_class, attr): - return attr in _class.__dataclass_fields__ def transform(mapping_table, input_obj): - # Builtin types (str, int, ...) are assumed to be just values to copy without any change + # Builtin types (str, int, ...) are assumed to be just values that shall be copied without any change if is_builtin(input_obj): return input_obj # Find a translation rule in the metadata for (from_class, to_class), mappings in mapping_table['type_map'].items(): - # Uses linear-search in mapping table until we find something matching input object. + # Use linear-search in mapping table until we find something matching input object. # Since the translation table is reasonably short, it should be OK for now. if from_class != input_obj.__class__: - _log("INFO", f"Type mapping found: {from_class=} -> {to_class=}") continue # Continuing here with a matching mapping definition... + _log("INFO", f"Type mapping found: {from_class=} -> {to_class=}") # Comment: Here we might create an empty instance of the class and fill it with values using setattr(), but # that won't work since the redesign using dataclasses. The AST classes now have a default constructor that @@ -255,7 +231,7 @@ def transform(mapping_table, input_obj): # To remember the args we have converted done_attrs = set() - # First loop: Perform explicitly defined attribute conversions that are specified in the translation table. + # First loop: Perform explicitly defined attribute conversions listed in each entry for preparation_function, input_attr, output_attr, field_transform in [eval_mapping(m) for m in mappings]: _log("INFO", f"Attribute mapping found: {input_attr=} -> {output_attr=} with {field_transform=}") @@ -293,16 +269,15 @@ def transform(mapping_table, input_obj): # Second loop: Any attributes that have the _same name_ in the input and output classes are assumed to be # mappable to each other. Identical names do not need to be listed in the translation table unless they - # need a custom transformation. Here we find all matching names and map them (with recursive - # transformation, as needed), but of course skip all attributes that have been handled explicitly - # (done_attrs). global_attribute_map also defines globally which attributes are considered identical. + # need a custom transformation. So here we can find all matching names and map them (with recursive + # transformation, as needed), but of course skip all attributes that have been handled by explicit rule + # (done_attrs). global_attribute_map also defines which attribute names shall be considered the same. global_attribute_map = mapping_table['global_attribute_map'] - # Checking all fields defined in input object. + # Checking all fields in input object, except fields that were handled and stored in done_attrs for attr, value in vars(input_obj).items(): - # ... unless already handled by explicit rule if attr in done_attrs: continue @@ -311,16 +286,18 @@ def transform(mapping_table, input_obj): if dataclass_has_field(to_class, attr): _log("DEBUG", f"Performing global or same-name auto-conversion for {attr=} from {from_class.__name__} to {to_class.__name__}\n") - # (No transform function for same-name translations (this might change) => therefore an identity lambda) + # (No transform function for same-name translations (this might change) => therefore use identity lambda) set_attr(attributes, attr, transform_value_common(mapping_table, value, lambda _ : _)) continue _log_if(attr is not None, "WARN", f"Attribute '{attr}' from Input AST:{input_obj.__class__.__name__} was not used in IFEX:{to_class.__name__}") - # attributes now filled with key/values. Instantiate "to_class" object, and return it. + + # Both loops done. Attributes now filled with key/values. Instantiate "to_class" object and return it. _log("DEBUG", f"Creating and returning object of type {to_class} with {attributes=}") return to_class(**attributes) + no_rule = f"no translation rule found for object {input_obj} of class {input_obj.__class__.__name__}" _log("ERROR:", no_rule) raise typeerror(no_rule)