pydantic-bind 1.0.5
C++/pybind generation from Pydantic classes
License: https://www.apache.org/licenses/LICENSE-2.0

# pydantic-bind

# Table of Contents
1. [Overview](#Overview)
2. [Getting Started](#Getting-Started)
3. [Why Not Protobufs ?](#Why-Not-Protobufs)
4. [No Copy](#No-Copy)
5. [Supported Types](#Supported-Types)
6. [Inheritance](#Inheritance)
7. [Msgpack](#Msgpack)
8. [Namespaces](#Namespaces)
9. [Generated Code](#Generated-Code)
10. [Other Languages](#Other-Languages)


## Overview

Python is the language of choice for  finance, data science etc. Python calling C++ (and increasingly, Rust) is a
common pattern, leveraging packages such as
[pybind11](https://pybind11.readthedocs.io/en/stable/index.html) .

A common problem is a how best to represent data to be shared between python and C++ code. One would like idiomatic
representations in each language and this may be necessary to fully utilise certain python packages. E.g.,
[FastAPI](https://fastapi.tiangolo.com) is a popular way to create REST services, using Open API definitions derived
from [pydantic](https://docs.pydantic.dev/latest/) classes. Therefore, a data model authored using pydantic classes,
or native python dataclasses, from which sensible C++ structs and appropriate marshalling can automatically be
generated, is desirable.

This package provides such tools: a cmake rule allows you to generate C++ structs (with msgpack serialisation) and
corresponding pybind11 bindings.

Python functions allow you to naviagte between the C++ pybind11 objects and the native python objects. There is also an
option for all python operations to be directed to an owned pybind11 object (see [No Copy](#No-Copy)).

Note that the typcal python developer experience is now somewhat changed, in that it's necessary to build/install
the project. I personally use JetBrains CLion, in place of PyCharm for such projects.

For an example of the kind of behaviour-less object model this package is intended to help,
please see (the rather nascent) [fin-data-model](https://github.com/nickyoung-github/fin-data-model)


## Getting Started

`pydantic_bind` adds a custom cmake rule: `pydantic_bind_add_package(<package path>)`

This rule will do the following:
- scan for sub-packages
- scan each sub-package for all .py files
- add custom steps for generating .cpp/.h files from any of the following, encounted in the .py files:
  - dataclasses
  - classes derived from pydantic's BaseModel
  - enums

C++ directory and namespace structure will match the python package structure (see [Namespaces](#Namespaces)).

You can create an instance of the pybind11 class from your original using `get_pybind_instance()`, e.g.,

*my_class.py:*

    from dataclasses import dataclass

    @dataclass
    clas MyClass:
        my_int: int
        my_string: str | None

*CMakeLists.txt:*

    cmake_minimum_required(VERSION 3.9)
    project(my_project)
    
    set(CMAKE_CXX_STANDARD 20)
    
    find_package(python3 REQUIRED COMPONENTS Interpreter Development)
    find_package(pydantic_bind REQUIRED COMPONENTS HINTS "${python3_SITELIB}")
    
    pydantic_bind_add_package(my_package)


*my_util.py*

    from pydantic_bind import get_pybind_value
    from my_package.my_class imnport MyClass

    orig = MyClass(my_int=123, my_string="hello")
    generated = get_pybind_value(orig)

    print(f"my_int: {orig.my_int}, {generated.my_int}")


## Why Not Protobufs?

I personally find protobufs to be a PITA to use: they have poor to no variant support, the generated code is ugly and
idiosyncratic, they're large and painful to copy around etc.

AVRO is more friendly but generates python classes dynamically, which confuses IDEs like Pycharm. I do think a good
solution is something like [pydantic_avro](https://github.com/godatadriven/pydantic-avro/tree/main/src/pydantic_avro)
where one can define the classes using pydantic, generate the AVRO schema and then the generateed C++ etc. I might
well try and converge this project with that approach.

I was inspired to some degree by this [blog](https://mikeloomisgg.github.io/2019-07-02-making-a-serialization-library/).


## No Copy

One annoyance of multi-language representations of data objects is that you often end up copying data around where
you'd prefer to share a single copy. This is the raison d'etre for Protobufs and its ilk. In this project I've created
implementations of `BaseModel` and `dataclass` which allow python to use the underlying C++ data representation, rather
than holding its own copy.

Deriving from this `BaseModel` will give you equivalent functionality of as pydantic's `BaseModel`. The
annotations are re-written using `computed_field`, with property getters and setters operating on the generated pybind
class, which is instantiated behind the scenes in `__init__`. Note that this will make some operations (especially those
that access __dict__) less efficient. I've also plumbed the computed fields into the JSON schema, so these objects can
be used with [FastAPI](https://fastapi.tiangolo.com).

`dataclass` works similarly, adding properties to the dataclass, so that the exisitng get and set functionality works
seamless in accessing the generated pybind11 class (also set via a shimmed `__init__`).

Using regular `dataclass` or `BaseModel` as members of classes defined with the pydantic_bind versions is very
inefficient and not recommended.


## Supported Types

The following python -> C++ mappings are supported (there are likely others I should consider):

- bool --> bool
- float --> double
- int --> int
- str --> std::string
- datetime.date --> std::chrono::system_clock::time_point
- datetime.datetime --> std::chrono::system_clock::time_point
- datetime.time --> std::chrono::system_clock::time_point
- datetime.timedelta --> std::chrono::duration
- pydantic.BaseModel --> struct
- pydantic_bind.BaseModel --> struct
- dataclass --> struct
- pydantic_bind.dataclass --> struct
- Enum --> enum class


## Inheritance

I have tested single inheritance (see [Generated Code](#Generated-code)). Multiple inheritance may work ... or it
may not. I'd generally advise against using it for data classes.


## Msgpack

A rather rudimentary msgpack implementation is added to the generated C++ structs, using a slightly modified version
of [cpppack](https://github.com/mikeloomisgg/cppack). It wasn't clear to me whether this package is maintained or
accepting submissions, so I copied and slightly modified `msgpack.h` (also, I couldn't work out how to add to my 
project with my rather rudimentary cmake skillz!) Changes include:

- Fixing includes
- Support for std::optional
- Support for std::variant
- Support for enums

A likely future enhancement will be to use [cereal](https://github.com/USCiLab/cereal) and add a mgspack adaptor.

The no-copy python objects add `to_msg_pack()` and `from_msg_pack()` (the latter being a class method), to access
this functionality.


## Namespaces

Directory structure and namespaces in the generated C++ match the python package and module names.

cmake requires unique target names and pybind11 requires that the filename (minus the OS-speicific qualifiers) matches
the module name. 


## Generated Code

Code is generated into a directory structure underneath `<top level>/generated`.

Headers are installed to `<top level>/include`.

Compiled pybind11 modules are installed into `<original module path>/__pybind__`.

For C++ usage, you need only the headers, the compiled code is for pybind/python usage only.

For the example below, `common_object_model/common_object_model/v1/common/__pybind__/foo.cpython-311-darwin.so` will
be installed (obviously with corresponding qualifiers for Linux/Windows). `get_pybind_value()` searches this
directory.

Imports/includes should work seamlessly (the python import scheme will be copied). I have tested this but not
completely rigorously.

*common_object_model/common_object_model/v1/common/foo.py:*

    from dataclasses import dataclass
    import datetime as dt
    from enum import Enum, auto
    from typing import Union

    from pydantic_bind import BaseModel


    class Weekday(Enum):
        MONDAY = auto()
        TUESDAY = auto()
        WEDNESDAY = auto()
        THURSDAY = auto()
        FRIDAY = auto()
        SATURDAY = auto()
        SUNDAY = auto()
    
    
    @dataclass
    class DCFoo:
        my_int: int
        my_string: str | None
    
    
    class Foo(BaseModel):
        my_bool: bool = True
        my_day: Weekday = Weekday.SUNDAY
    
    
    class Bar(Foo):
        my_int: int = 123
        my_string: str
        my_optional_string: str | None = None
    
    
    class Baz(BaseModel):
        my_variant: Union[str, float] = 123.
        my_date: dt.date
        my_foo: Foo
        my_dc_foo: DCFoo

will generate the following files:

*common_object_model/generated/common_object_model/v1/common/foo.h:*

    #ifndef COMMON_OBJECT_MODEL_FOO_H
    #define COMMON_OBJECT_MODEL_FOO_H
    
    #include <string>
    #include <optional>
    #include <variant>
    #include <msgpack/msgpack.h>
    #include <chrono>
    
    namespace common_object_model::v1::common
    {
        enum Weekday { MONDAY = 1, TUESDAY = 2, WEDNESDAY = 3, THURSDAY = 4, FRIDAY = 5, SATURDAY = 6, SUNDAY = 7
        };
    
        struct DCFoo
        {
            DCFoo() :
                my_string(), my_int()
            {
            }
        
            DCFoo(std::optional<std::string> my_string, int my_int) :
                my_string(my_string), my_int(my_int)
            {
            }
    
            std::optional<std::string> my_string;
            int my_int;
        
            MSGPACK_DEFINE(my_string, my_int);
        };
    
        struct Foo
        {
            Foo(bool my_bool=true, Weekday my_day=SUNDAY) :
                my_bool(my_bool), my_day(my_day)
            {
            }
    
            bool my_bool;
            Weekday my_day;
        
            MSGPACK_DEFINE(my_bool, my_day);
        };
    
        struct Bar : public Foo
        {
            Bar() :
                Foo(),
                my_string(), my_int(123), my_optional_string(std::nullopt)
            {
            }
        
            Bar(std::string my_string, bool my_bool=true, Weekday my_day=SUNDAY, int my_int=123, std::optional<std::string>
                my_optional_string=std::nullopt) :
                Foo(my_bool, my_day),
                my_string(std::move(my_string)), my_int(my_int), my_optional_string(my_optional_string)
            {
            }
    
            std::string my_string;
            int my_int;
            std::optional<std::string> my_optional_string;
        
            MSGPACK_DEFINE(my_string, my_bool, my_day, my_int, my_optional_string);
        };
    
        struct Baz
        {
            Baz() :
                my_dc_foo(), my_foo(), my_date(), my_variant(123.0)
            {
            }
        
            Baz(DCFoo my_dc_foo, Foo my_foo, std::chrono::system_clock::time_point my_date, std::variant<std::string, double>
                my_variant=123.0) :
                my_dc_foo(std::move(my_dc_foo)), my_foo(std::move(my_foo)), my_date(my_date),
                my_variant(my_variant)
            {
            }
    
            DCFoo my_dc_foo;
            Foo my_foo;
            std::chrono::system_clock::time_point my_date;
            std::variant<std::string, double> my_variant;
        
            MSGPACK_DEFINE(my_dc_foo, my_foo, my_date, my_variant);
        };
    } // common_object_model
    
    #endif // COMMON_OBJECT_MODEL_FOO_H


*common_object_model/generated/common_object_model/v1/common/foo.cpp:*

    #include <pybind11/pybind11.h>
    #include <pybind11/stl.h>
    #include <pybind11/chrono.h>
    
    #include "foo.h"
    
    namespace py = pybind11;
    using namespace common_object_model::v1::common;
    
    
    PYBIND11_MODULE(common_object_model_v1_common_foo, m)
    {
        py::enum_<Weekday>(m, "Weekday").value("MONDAY", Weekday::MONDAY)
            .value("TUESDAY", Weekday::TUESDAY)
            .value("WEDNESDAY", Weekday::WEDNESDAY)
            .value("THURSDAY", Weekday::THURSDAY)
            .value("FRIDAY", Weekday::FRIDAY)
            .value("SATURDAY", Weekday::SATURDAY)
            .value("SUNDAY", Weekday::SUNDAY);

        py::class_<DCFoo>(m, "DCFoo")
            .def(py::init<>())
            .def(py::init<std::optional<std::string>, int>(), py::arg("my_string"), py::arg("my_int"))
            .def("to_msg_pack", &DCFoo::to_msg_pack)
            .def_static("from_msg_pack", &DCFoo::from_msg_pack<Baz>)
            .def_readwrite("my_string", &DCFoo::my_string)
            .def_readwrite("my_int", &DCFoo::my_int);
    
        py::class_<Foo>(m, "Foo")
            .def(py::init<bool, Weekday>(), py::arg("my_bool")=true, py::arg("my_day")=SUNDAY)
            .def("to_msg_pack", &Foo::to_msg_pack)
            .def_static("from_msg_pack", &Foo::from_msg_pack<Baz>)
            .def_readwrite("my_bool", &Foo::my_bool)
            .def_readwrite("my_day", &Foo::my_day);
    
        py::class_<Bar>(m, "Bar")
            .def(py::init<>())
            .def(py::init<std::string, bool, Weekday, int, std::optional<std::string>>(), py::arg("my_string"), py::arg("my_bool")=true,
                py::arg("my_day")=SUNDAY, py::arg("my_int")=123, py::arg("my_optional_string")=std::nullopt)
            .def("to_msg_pack", &Bazr:to_msg_pack)
            .def_static("from_msg_pack", &Bar::from_msg_pack<Baz>)
            .def_readwrite("my_string", &Bar::my_string)
            .def_readwrite("my_int", &Bar::my_int)
            .def_readwrite("my_optional_string", &Bar::my_optional_string);
    
        py::class_<Baz>(m, "Baz")
            .def(py::init<>())
            .def(py::init<DCFoo, Foo, std::chrono::system_clock::time_point, std::variant<std::string, double>>(), py::arg("my_dc_foo"),
                py::arg("my_foo"), py::arg("my_date"), py::arg("my_variant")=123.0)
            .def("to_msg_pack", &Baz::to_msg_pack)
            .def_static("from_msg_pack", &Baz::from_msg_pack<Baz>)
            .def_readwrite("my_dc_foo", &Baz::my_dc_foo)
            .def_readwrite("my_foo", &Baz::my_foo)
            .def_readwrite("my_date", &Baz::my_date)
            .def_readwrite("my_variant", &Baz::my_variant);
    }


## Other languages

When time allows, I will look at adding support for Rust. There is limited value in generating Java or C# classes;
calling those VM-based lanagues in-process from python has never worked well, in my experience.