support generics and forward references

.github/workflows/code_check.yaml

@@ -46,5 +46,5 @@ jobs:
           benchmark-data-dir-path: performance/${{ matrix.python-version }}
           comment-always: false
           alert-threshold: '130%'
-          comment-on-alert: false
-          fail-on-alert: true
+          comment-on-alert: true
+          fail-on-alert: false

.gitignore

@@ -6,6 +6,7 @@ build/
 .idea
 
 venv*
+dacite-env*
 
 .benchmarks
 benchmark.json

README.md

@@ -54,12 +54,14 @@ assert user == User(name='John', age=30, is_active=True)
 Dacite supports following features:
 
 - nested structures
-- (basic) types checking
+- (basic) type checking
 - optional fields (i.e. `typing.Optional`)
 - unions
+- generics
 - forward references
 - collections
 - custom type hooks
+- case conversion
 
 ## Motivation
 
@@ -109,6 +111,7 @@ Configuration is a (data) class with following fields:
 - `check_types`
 - `strict`
 - `strict_unions_match`
+- `convert_key`
 
 The examples below show all features of `from_dict` function and usage
 of all `Config` parameters.
@@ -233,6 +236,71 @@ result = from_dict(data_class=B, data=data)
 assert result == B(a_list=[A(x='test1', y=1), A(x='test2', y=2)])
 ```
 
+### Generics
+
+Dacite supports generics: (multi-)generic dataclasses, but also dataclasses that inherit from a generic dataclass, or dataclasses that have a generic dataclass field.
+
+```python
+T = TypeVar('T')
+U = TypeVar('U')
+
+@dataclass
+class X:
+    a: str
+
+
+@dataclass
+class A(Generic[T, U]):
+    x: T
+    y: list[U]
+
+data = {
+    'x': {
+        'a': 'foo',
+    },
+    'y': [1, 2, 3]
+}
+
+result = from_dict(data_class=A[X, int], data=data)
+
+assert result == A(x=X(a='foo'), y=[1,2,3])
+
+
+@dataclass
+class B(A[X, int]):
+    z: str
+
+data = {
+    'x': {
+        'a': 'foo',
+    },
+    'y': [1, 2, 3],
+    'z': 'bar'
+}
+
+result = from_dict(data_class=B, data=data)
+
+assert result == B(x=X(a='foo'), y=[1,2,3], z='bar')
+
+
+@dataclass
+class C:
+    z: A[X, int]
+
+data = {
+    'z': {
+        'x': {
+            'a': 'foo',
+        },
+        'y': [1, 2, 3],
+    }
+}
+
+result = from_dict(data_class=C, data=data)
+
+assert result == C(z=A(x=X(a='foo'), y=[1,2,3]))
+```
+
 ### Type hooks
 
 You can use `Config.type_hooks` argument if you want to transform the input 
@@ -313,30 +381,17 @@ data = from_dict(X, {"y": {"s": "text"}}, Config(forward_references={"Y": Y}))
 assert data == X(Y("text"))
 ```
 
-### Types checking
+### Type checking
 
-There are rare cases when `dacite` built-in type checker can not validate 
-your types (e.g. custom generic class) or you have such functionality 
-covered by other library and you don't want to validate your types twice. 
-In such case you can disable type checking with `Config(check_types=False)`.
-By default types checking is enabled.
+If you want to trade-off type checking for speed, you can disabled type checking by setting `check_types` to `False`.
 
 ```python
-T = TypeVar('T')
-
-
-class X(Generic[T]):
-    pass
-
-
 @dataclass
 class A:
-    x: X[str]
-
-
-x = X[str]()
+    x: str
 
-assert from_dict(A, {'x': x}, config=Config(check_types=False)) == A(x=x)
+# won't throw an error even though the type is wrong
+from_dict(A, {'x': 4}, config=Config(check_types=False)) 
 ```
 
 ### Strict mode
@@ -354,6 +409,30 @@ returns instance of this type. It means that it's possible that there are other
 matching types further on the `Union` types list. With `strict_unions_match` 
 only a single match is allowed, otherwise `dacite` raises `StrictUnionMatchError`.
 
+## Convert key
+
+You can pass a callable to the `convert_key` configuration parameter to convert camelCase to snake_case.
+
+```python
+def to_camel_case(key: str) -> str:
+    first_part, *remaining_parts = key.split('_')
+    return first_part + ''.join(part.title() for part in remaining_parts)
+
+@dataclass
+class Person:
+    first_name: str
+    last_name: str
+
+data = {
+    'firstName': 'John',
+    'lastName': 'Doe'
+}
+
+result = from_dict(Person, data, Config(convert_key=to_camel_case))
+
+assert result == Person(first_name='John', last_name='Doe') 
+```
+
 ## Exceptions
 
 Whenever something goes wrong, `from_dict` will raise adequate
@@ -392,33 +471,33 @@ first within an issue.
 
 Clone `dacite` repository:
 
-```
-$ git clone git@github.com:konradhalas/dacite.git
+```bash
+git clone git@github.com:konradhalas/dacite.git
 ```
 
 Create and activate virtualenv in the way you like:
 
-```
-$ python3 -m venv dacite-env
-$ source dacite-env/bin/activate
+```bash
+python3 -m venv dacite-env
+source dacite-env/bin/activate
 ```
 
 Install all `dacite` dependencies:
 
-```
-$ pip install -e .[dev]
+```bash
+pip install -e .[dev]
 ```
 
 And, optionally but recommended, install pre-commit hook for black:
 
-```
-$ pre-commit install
+```bash
+pre-commit install
 ```
 
 To run tests you just have to fire:
 
-```
-$ pytest
+```bash
+pytest
 ```
 
 ### Performance testing

dacite/config.py

@@ -19,6 +19,7 @@ class Config:
     check_types: bool = True
     strict: bool = False
     strict_unions_match: bool = False
+    convert_key: Callable[[str], str] = field(default_factory=lambda: lambda x: x)
 
     @cached_property
     def hashable_forward_references(self) -> Optional[FrozenDict]:

dacite/core.py

@@ -1,14 +1,13 @@
 from dataclasses import is_dataclass
 from itertools import zip_longest
-from typing import TypeVar, Type, Optional, get_type_hints, Mapping, Any, Collection, MutableMapping
+from typing import TypeVar, Type, Optional, Mapping, Any, Collection, MutableMapping
 
 from dacite.cache import cache
 from dacite.config import Config
 from dacite.data import Data
 from dacite.dataclasses import (
     get_default_value_for_field,
     DefaultValueNotFoundError,
-    get_fields,
     is_frozen,
 )
 from dacite.exceptions import (
@@ -33,6 +32,8 @@
     is_subclass,
 )
 
+from dacite.generics import get_concrete_type_hints, get_fields, orig
+
 T = TypeVar("T")
 
 
@@ -47,21 +48,26 @@ def from_dict(data_class: Type[T], data: Data, config: Optional[Config] = None)
     init_values: MutableMapping[str, Any] = {}
     post_init_values: MutableMapping[str, Any] = {}
     config = config or Config()
+
     try:
-        data_class_hints = cache(get_type_hints)(data_class, localns=config.hashable_forward_references)
+        data_class_hints = cache(get_concrete_type_hints)(data_class, localns=config.hashable_forward_references)
     except NameError as error:
         raise ForwardReferenceError(str(error))
+
     data_class_fields = cache(get_fields)(data_class)
+
     if config.strict:
         extra_fields = set(data.keys()) - {f.name for f in data_class_fields}
         if extra_fields:
             raise UnexpectedDataError(keys=extra_fields)
+
     for field in data_class_fields:
         field_type = data_class_hints[field.name]
-        if field.name in data:
+        key = config.convert_key(field.name)
+
+        if key in data:
             try:
-                field_data = data[field.name]
-                value = _build_value(type_=field_type, data=field_data, config=config)
+                value = _build_value(type_=field_type, data=data[key], config=config)
             except DaciteFieldError as error:
                 error.update_path(field.name)
                 raise
@@ -74,13 +80,17 @@ def from_dict(data_class: Type[T], data: Data, config: Optional[Config] = None)
                 if not field.init:
                     continue
                 raise MissingValueError(field.name)
+
         if field.init:
             init_values[field.name] = value
         elif not is_frozen(data_class):
             post_init_values[field.name] = value
+
     instance = data_class(**init_values)
+
     for key, value in post_init_values.items():
         setattr(instance, key, value)
+
     return instance
 
 
@@ -95,7 +105,7 @@ def _build_value(type_: Type, data: Any, config: Config) -> Any:
         data = _build_value_for_union(union=type_, data=data, config=config)
     elif is_generic_collection(type_):
         data = _build_value_for_collection(collection=type_, data=data, config=config)
-    elif cache(is_dataclass)(type_) and isinstance(data, Mapping):
+    elif cache(is_dataclass)(orig(type_)) and isinstance(data, Mapping):
         data = from_dict(data_class=type_, data=data, config=config)
     for cast_type in config.cast:
         if is_subclass(type_, cast_type):