|
6 | 6 | import datetime |
7 | 7 | import inspect |
8 | 8 | from enum import Enum |
9 | | -from typing import Any, Callable, Mapping, get_origin |
| 9 | +from typing import Any, Callable, Mapping, Type, get_args, get_origin |
10 | 10 |
|
11 | 11 | import numpy as np |
12 | 12 |
|
13 | 13 | from .typing import ( |
14 | 14 | KEY_FIELD_NAME, |
15 | 15 | TABLE_TYPES, |
16 | | - analyze_type_info, |
17 | | - encode_enriched_type, |
18 | | - is_namedtuple_type, |
19 | | - is_struct_type, |
20 | | - AnalyzedTypeInfo, |
21 | 16 | AnalyzedAnyType, |
| 17 | + AnalyzedBasicType, |
22 | 18 | AnalyzedDictType, |
23 | 19 | AnalyzedListType, |
24 | | - AnalyzedBasicType, |
| 20 | + AnalyzedStructType, |
| 21 | + AnalyzedTypeInfo, |
25 | 22 | AnalyzedUnionType, |
26 | 23 | AnalyzedUnknownType, |
27 | | - AnalyzedStructType, |
| 24 | + TypeKind, |
| 25 | + analyze_type_info, |
| 26 | + encode_enriched_type, |
| 27 | + is_namedtuple_type, |
28 | 28 | is_numpy_number_type, |
| 29 | + is_struct_type, |
29 | 30 | ) |
30 | 31 |
|
31 | 32 |
|
32 | | -def encode_engine_value(value: Any) -> Any: |
| 33 | +def encode_engine_value( |
| 34 | + value: Any, in_struct: bool = False, type_hint: Type[Any] | str | None = None |
| 35 | +) -> Any: |
33 | 36 | """Encode a Python value to an engine value.""" |
34 | 37 | if dataclasses.is_dataclass(value): |
35 | 38 | return [ |
36 | | - encode_engine_value(getattr(value, f.name)) |
| 39 | + encode_engine_value( |
| 40 | + getattr(value, f.name), in_struct=True, type_hint=f.type |
| 41 | + ) |
37 | 42 | for f in dataclasses.fields(value) |
38 | 43 | ] |
39 | 44 | if is_namedtuple_type(type(value)): |
40 | | - return [encode_engine_value(getattr(value, name)) for name in value._fields] |
| 45 | + annotations = type(value).__annotations__ |
| 46 | + return [ |
| 47 | + encode_engine_value( |
| 48 | + getattr(value, name), in_struct=True, type_hint=annotations.get(name) |
| 49 | + ) |
| 50 | + for name in value._fields |
| 51 | + ] |
41 | 52 | if isinstance(value, np.number): |
42 | 53 | return value.item() |
43 | 54 | if isinstance(value, np.ndarray): |
44 | 55 | return value |
45 | 56 | if isinstance(value, (list, tuple)): |
46 | | - return [encode_engine_value(v) for v in value] |
| 57 | + return [encode_engine_value(v, in_struct) for v in value] |
47 | 58 | if isinstance(value, dict): |
| 59 | + is_json_type = type_hint and any( |
| 60 | + isinstance(arg, TypeKind) and arg.kind == "Json" |
| 61 | + for arg in get_args(type_hint)[1:] |
| 62 | + ) |
| 63 | + |
| 64 | + # For empty dicts, check type hints if in a struct context |
| 65 | + # when no contexts are provided, return an empty dict as default |
48 | 66 | if not value: |
| 67 | + if in_struct: |
| 68 | + return value if is_json_type else [] |
49 | 69 | return {} |
50 | 70 |
|
51 | 71 | first_val = next(iter(value.values())) |
52 | 72 | if is_struct_type(type(first_val)): # KTable |
53 | 73 | return [ |
54 | | - [encode_engine_value(k)] + encode_engine_value(v) |
| 74 | + [encode_engine_value(k, in_struct)] + encode_engine_value(v, in_struct) |
55 | 75 | for k, v in value.items() |
56 | 76 | ] |
57 | 77 | return value |
|
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