to_tap_class, and inspect fields instead of signature for data models

.github/workflows/tests.yml

@@ -36,7 +36,7 @@ jobs:
         git config --global user.name "Your Name"
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
-        python -m pip install -e .
+        python -m pip install -e ".[dev]"
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names

demo_data_model.py

@@ -0,0 +1,68 @@
+"""
+Example:
+
+python demo_data_model.py \
+--arg_str test \
+--arg_list x y z \
+--arg_bool \
+-arg 2
+"""
+from pydantic import BaseModel, Field
+from tap import tapify, convert_to_tap_class
+
+
+class Model(BaseModel):
+    """
+    My Pydantic Model which contains script args.
+    """
+
+    arg_str: str = Field(description="hello")
+    arg_bool: bool = Field(default=True, description=None)
+    arg_list: list[str] | None = Field(default=None, description="optional list")
+
+
+def main(model: Model) -> None:
+    print("Parsed args into Model:")
+    print(model)
+
+
+def to_number(string: str) -> float | int:
+    return float(string) if "." in string else int(string)
+
+
+class ModelTap(convert_to_tap_class(Model)):
+    # You can supply additional arguments here
+    argument_with_really_long_name: float | int = 3
+    "This argument has a long name and will be aliased with a short one"
+
+    def configure(self) -> None:
+        # You can still add special argument behavior
+        self.add_argument("-arg", "--argument_with_really_long_name", type=to_number)
+
+    def process_args(self) -> None:
+        # You can still validate and modify arguments
+        # (You should do this in the Pydantic Model. I'm just demonstrating that this functionality is still possible)
+        if self.argument_with_really_long_name > 4:
+            raise ValueError("nope")
+
+        # No auto-complete (and other niceties) for the super class attributes b/c this is a dynamic subclass. Sorry
+        if self.arg_bool:
+            self.arg_str += " processed"
+
+
+if __name__ == "__main__":
+    # You don't have to subclass tap_class_from_data_model(Model) if you just want a plain argument parser:
+    # ModelTap = tap_class_from_data_model(Model)
+    args = ModelTap(description="Script description").parse_args()
+    print("Parsed args:")
+    print(args)
+    # Run the main function. Pydantic BaseModels ignore arguments which aren't one of their fields instead of raising an
+    # error
+    model = Model(**args.as_dict())
+    main(model)
+
+
+# This works but doesn't show the field description, and immediately returns a Model instance instead of a Tap class
+# if __name__ == "__main__":
+#     model = tapify(Model)
+#     print(model)

setup.py

@@ -12,6 +12,11 @@
 with open("README.md", encoding="utf-8") as f:
     long_description = f.read()
 
+test_requirements = [
+    "pydantic >= 2.5.0",
+    "pytest",
+]
+
 setup(
     name="typed-argument-parser",
     version=__version__,
@@ -26,7 +31,8 @@
     packages=find_packages(),
     package_data={"tap": ["py.typed"]},
     install_requires=["typing-inspect >= 0.7.1", "docstring-parser >= 0.15"],
-    tests_require=["pytest"],
+    tests_require=test_requirements,
+    extras_require={"dev": test_requirements},
     python_requires=">=3.8",
     classifiers=[
         "Programming Language :: Python :: 3",

tap/__init__.py

@@ -1,6 +1,13 @@
 from argparse import ArgumentError, ArgumentTypeError
 from tap._version import __version__
 from tap.tap import Tap
-from tap.tapify import tapify
+from tap.tapify import tapify, convert_to_tap_class
 
-__all__ = ["ArgumentError", "ArgumentTypeError", "Tap", "tapify", "__version__"]
+__all__ = [
+    "ArgumentError",
+    "ArgumentTypeError",
+    "Tap",
+    "tapify",
+    "convert_to_tap_class",
+    "__version__",
+]