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feat(tracer): support more python types
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6 files changed

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‎README.md‎

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@@ -18,6 +18,15 @@ python trace.py <path to python file>
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however you probably want to use it in combination with CodeTracer, which would be released soon.
2020

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### Supported value types
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The tracer currently understands a subset of Python's built‑in types. It can
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record integers, booleans, strings, lists, floats, tuples, byte strings,
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complex numbers and simple objects (via ``__dict__`` introspection). Values that
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don't fall in these categories are stored as raw strings.
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Dictionary and set types are not yet handled.
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## Future directions
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The current Python support is an unfinished prototype. We can finish it. In the future, it may be expanded to function in a way to similar to the more complete implementations, e.g. [Noir](https://github.com/blocksense-network/noir/tree/blocksense/tooling/tracer).

‎src/trace.py‎

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@@ -61,6 +61,10 @@ def _register_builtin_types(self) -> None:
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self._register_type(12, "Bool")
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self._register_type(9, "Symbol")
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self._register_type(24, "No type")
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self._register_type(8, "Float")
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self._register_type(27, "Tuple")
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self._register_type(16, "Bytes")
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self._register_type(6, "Complex")
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# -------------------------------------------------------------------- paths
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def _register_path(self, path: str) -> int:
@@ -119,12 +123,18 @@ def _ensure_type(self, kind: int, name: str) -> int:
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return self.types[name]
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def _value(self, val: Any) -> Dict[str, Any]:
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if isinstance(val, int):
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return {"kind": "Int", "type_id": self.types["Integer"], "i": val}
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if isinstance(val, bool):
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return {"kind": "Bool", "type_id": self.types["Bool"], "b": val}
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if isinstance(val, int):
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return {"kind": "Int", "type_id": self.types["Integer"], "i": val}
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if isinstance(val, float):
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type_id = self._ensure_type(8, "Float")
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return {"kind": "Float", "type_id": type_id, "f": val}
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if isinstance(val, str):
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return {"kind": "String", "type_id": self.types["String"], "text": val}
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if isinstance(val, (bytes, bytearray)):
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type_id = self._ensure_type(16, "Bytes")
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return {"kind": "Raw", "type_id": type_id, "r": str(val)}
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if isinstance(val, list):
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type_id = self._ensure_type(0, "Array")
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return {
@@ -133,8 +143,29 @@ def _value(self, val: Any) -> Dict[str, Any]:
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"elements": [self._value(v) for v in val],
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"is_slice": False,
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}
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if isinstance(val, tuple):
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type_id = self._ensure_type(27, "Tuple")
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return {
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"kind": "Tuple",
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"type_id": type_id,
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"elements": [self._value(v) for v in val],
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}
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if isinstance(val, complex):
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type_id = self._ensure_type(6, "Complex")
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return {
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"kind": "Struct",
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"type_id": type_id,
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"field_values": [self._value(val.real), self._value(val.imag)],
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}
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if val is None:
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return {"kind": "None", "type_id": self.types["No type"]}
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if hasattr(val, "__dict__"):
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type_id = self._ensure_type(6, val.__class__.__name__)
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return {
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"kind": "Struct",
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"type_id": type_id,
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"field_values": [self._value(v) for v in val.__dict__.values()],
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}
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type_id = self._ensure_type(16, "Object")
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return {"kind": "Raw", "type_id": type_id, "r": str(val)}
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‎tests/fixtures/array_sum.json‎

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}
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}
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},
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{
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"Type": {
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"kind": 8,
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"lang_type": "Float",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Type": {
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"kind": 27,
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"lang_type": "Tuple",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Type": {
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"kind": 16,
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"lang_type": "Bytes",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Type": {
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"kind": 6,
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"lang_type": "Complex",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Path": ""
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},
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",
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"variable_id": 0,
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"value": {
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"kind": "Sequence",
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"type_id": 5,
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"type_id": 9,
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"elements": [
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{
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"kind": "Int",

‎tests/fixtures/calc.json‎

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}
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}
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},
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{
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"Type": {
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"kind": 8,
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"lang_type": "Float",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Type": {
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"kind": 27,
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"lang_type": "Tuple",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Type": {
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"kind": 16,
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"lang_type": "Bytes",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Type": {
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"kind": 6,
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"lang_type": "Complex",
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"specific_info": {
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"kind": "None"
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}
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}
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},
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{
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"Path": ""
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},

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