feat(mdk): mdk python

[michael-0acf4]

Mdk for python runtime

Migration notes

None

Summary by CodeRabbit

• New Features

  • Introduced new functionalities for generating Python code based on configurations, including handling of templates and required objects.
  • Added Python script templates for defining typed functions and structured objects with comprehensive data type handling.
  • Enhanced type management and priority handling in utility functions.

• Documentation

  • Provided detailed summaries and documentation for new functionalities and templates.

• Refactor

  • Implemented new structures and methods for efficient code generation and type handling.

• Tests

  • Added tests for defining typegraph structures and policies in Python.

• Chores

  • Updated URLs in the .ghjk/deno.lock file to reflect new changes in the codebase.

[linear]

MET-491 (mdk) python

[michael-0acf4]

I ended up using class approach

Example output
scripts/example.py (entry point)

from .example_types import Output, Object26, typed_duplicate, Input, typed_add, TypeAddResult


@typed_duplicate
def duplicate(inp: Input) -> Output:
    # TODO: write your logic here
    raise Exception("duplicate not implemented")

@typed_add
def add(inp: Object26) -> TypeAddResult:
    # TODO: write your logic here
    raise Exception("add not implemented")

scripts/example_types.py

from types import NoneType
from typing import Callable, List, Union, get_origin, ForwardRef
from dataclasses import dataclass, asdict, fields

FORWARD_REFS = {}

class Struct:
    def try_new(dt_class, val: any):
        # Object
        ftypes = {f.name: f.type for f in fields(dt_class)}
        attrs = {}
        for f in val:
            fval = val[f]
            ftype = ftypes[f]
            serialized = False
            # Union
            if get_origin(ftype) is Union:
                try:
                    attrs[f] = Struct.try_union(ftype.__args__, fval)
                    serialized = True
                except Exception as _e:
                    pass
            # List
            elif get_origin(ftype) is list:
                try:
                    attrs[f] = Struct.try_typed_list(ftype.__args__, fval)
                    serialized = True
                except Exception as _e:
                    pass
            # Any
            if not serialized:
                if type(ftype) is str and ftype in FORWARD_REFS:
                    klass = FORWARD_REFS[ftype]
                    attrs[f] = Struct.new(klass, fval)
                else:
                    attrs[f] = Struct.new(ftype, fval)
        return dt_class(**attrs)

    def try_typed_list(tpe: any, items: any):
        hint = tpe.__args__[0]
        klass = FORWARD_REFS[hint.__forward_arg__] if type(hint) is ForwardRef else hint
        return [Struct.new(klass, v) for v in items]

    def try_union(variants: List[any], val: any):
        errors = []
        for variant in variants:
            try:
                if variant is NoneType:
                    if val is None:
                        return None
                    else:
                        continue
                if get_origin(variant) is list:
                    if type(val) is list:
                        return Struct.try_typed_list(variant, val)
                    else:
                        continue
                klass = FORWARD_REFS[variant.__forward_arg__]
                return Struct.try_new(klass, val)
            except Exception as e:
                errors.append(str(e))
        raise Exception("\n".join(errors))


    def new(dt_class: any, val: any):
        try:
            return Struct.try_new(dt_class, val)
        except:
            return val

    def repr(self):
        return asdict(self)


@dataclass
class Input(Struct):
    string: str
    integer: int
    email: Union[str, None]
    list_integer: List[int]
    opt_union_flat: Union[Union[float, int], None]
    reference: Union[List['References'], None]
    self: Union[List['Example'], None]
    

FORWARD_REFS['Input'] = Input

@dataclass
class References(Struct):
    string: str
    example: Union['Example', None]
    

FORWARD_REFS['References'] = References

@dataclass
class Example(Struct):
    string: str
    integer: int
    email: Union[str, None]
    list_integer: List[int]
    opt_union_flat: Union[Union[int, float], None]
    reference: Union[List['References'], None]
    self: Union[List['Example'], None]
    

FORWARD_REFS['Example'] = Example

@dataclass
class Output(Struct):
    string: str
    integer: int
    email: Union[str, None]
    list_integer: List[int]
    opt_union_flat: Union[Union[float, int], None]
    reference: Union[List['References'], None]
    self: Union[List['Example'], None]
    

FORWARD_REFS['Output'] = Output

@dataclass
class Object26(Struct):
    a: int
    b: int
    

FORWARD_REFS['Object26'] = Object26

TypeAddResult = int


def __repr(value: any):
        if isinstance(value, Struct):
            return value.repr()
        return value


def typed_duplicate(user_fn: Callable[[Input], Output]):
    def exported_wrapper(raw_inp):
        inp: Input = Struct.new(Input, raw_inp)
        out: Output = user_fn(inp)
        if type(out) is list:
            return [__repr(v) for v in out]
        return __repr(out)
    return exported_wrapper

def typed_add(user_fn: Callable[[Object26], TypeAddResult]):
    def exported_wrapper(raw_inp):
        inp: Object26 = Struct.new(Object26, raw_inp)
        out: TypeAddResult = user_fn(inp)
        if type(out) is list:
            return [__repr(v) for v in out]
        return __repr(out)
    return exported_wrapper

typegraph.py

from typegraph import typegraph, Policy, t, Graph
from typegraph.runtimes.python import PythonRuntime


@typegraph()
def example(g: Graph):
    references = t.struct(
        {"string": t.string(), "example": g.ref("Example").optional()},
        name="References",
    )
    example = t.struct(
        {
            "string": t.string(),
            "integer": t.integer(),
            "email": t.email().optional(),
            "list_integer": t.list(t.integer()),
            "opt_union_flat": t.union([t.integer(), t.integer(), t.float()]).optional(),
            "reference": t.list(references).optional(),
            "self": t.list(g.ref("Example")).optional(),
        },
        name="Example",
    )
    python = PythonRuntime()
    pub = Policy.public()
    g.expose(
        pub,
        duplicate=python.import_(
            example.rename("Input"),
            example.rename("Output"),
            name="duplicate",
            module="scripts/example.py",
        ),
        add=python.import_(
            t.struct({"a": t.integer(), "b": t.integer() }),
            t.integer().rename("AddResult"),
            name="add",
            module="scripts/example.py",
        ),
    )

[zifeo]

Nice!

[zifeo]

@CodiumAI-Agent /review

[CodiumAI-Agent]

PR Review 🔍

⏱️ Estimated effort to review [1-5]	4, due to the complexity and size of the PR, which includes multiple new modules, complex logic, and integration with existing systems. The PR introduces a new Python generator and modifies the core generator handling logic, requiring careful review to ensure correctness and maintainability.
🧪 Relevant tests	No
⚡ Possible issues	Possible Bug: The GeneratorHandler struct and its method handle might not correctly handle errors from the generator functions. The current implementation directly calls the function and returns its result, but there is no error handling or logging in case of failures.
	Performance Concern: The generate_target function now iterates over all generators to find a match for each generation request. This could be inefficient if the list of generators grows large.
🔒 Security concerns	No

Code feedback:

---

relevant file	libs/metagen/src/lib.rs
suggestion	Consider implementing error handling or logging within the GeneratorHandler::handle method to improve reliability and debuggability. [important]
relevant line	(*self.f)(value)

---

relevant file	libs/metagen/src/lib.rs
suggestion	Optimize the generator lookup in generate_target by using a HashMap or another efficient data structure to map generator names to their handlers, instead of iterating over a list. [important]
relevant line	.find(|g| g.name.eq(gen_name))

---

relevant file	libs/metagen/src/mdk_python/mod.rs
suggestion	Add error handling for potential failures in the new method of PythonGenerator, especially considering the validation of configuration which might throw errors. [important]
relevant line	config.validate(&())?;

---

relevant file	libs/metagen/src/mdk_python/mod.rs
suggestion	Consider adding more specific error messages or custom error types to improve the debugging experience and error traceability in the visit_type function. [medium]
relevant line	_ => bail!("Unsupported type {:?}", tpe.type_name()),

---

[coderabbitai]

Walkthrough

The recent updates across various modules enhance Python code generation capabilities, introduce new type handling functionalities, and improve type prioritization mechanisms. These changes include the addition of new structures and methods across Rust and Python files, aimed at better configuration management, dynamic type generation, and efficient template rendering for Python code generation.

Changes

File Path	Change Summary
.../mdk_python/mod.rs	Added configurations, generator struct, and methods for Python code generation.
.../mdk_python/static/main.py.jinja	Introduced typed function templates in Python.
.../mdk_python/static/types.py.jinja	Enhanced Struct class for advanced type handling.
.../mdk_python/utils.rs	Developed utilities for managing type generation and prioritization.
typegate/tests/metagen/typegraphs/python.py	Added functionality for defining typegraphs with specified structures.
.ghjk/deno.lock	Updated URLs for various TypeScript modules.

---

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 73.85%. Comparing base (24963ce) to head (ad5fada).
Report is 1 commits behind head on main.

❗ Current head ad5fada differs from pull request most recent head 6fe428a

Please upload reports for the commit 6fe428a to get more accurate results.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #707      +/-   ##
==========================================
+ Coverage   73.52%   73.85%   +0.32%     
==========================================
  Files         120      120              
  Lines       14142    13777     -365     
  Branches     1416     1393      -23     
==========================================
- Hits        10398    10175     -223     
+ Misses       3717     3576     -141     
+ Partials       27       26       -1     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[coderabbitai]

Actionable comments posted: 5

[coderabbitai]

Actionable comments posted: 1

Out of diff range and nitpick comments (1)

typegate/tests/metagen/typegraphs/python.py (1)

5-35: Ensure consistent naming conventions and type definitions.

The type definitions and the structure of the typegraph are well-defined. However, consider using more descriptive names for the types and variables to enhance readability and maintainability.

[Yohe-Am]

Would love a demo for this but looking good. Curious how you'll tackle re-generation for the user modified files.

[coderabbitai]

Actionable comments posted: 3

[coderabbitai]

Actionable comments posted: 1

[Yohe-Am]

Wonderful. Some comments but not blocking and feel free to merge.

[Yohe-Am]

Oh man. Am I glad to see the test-full timeout happening on this PR as well. I was loosing hair over here thinking it was the deadlock bug.

[Yohe-Am]

Looks like something wrong with serialization of python modules looking at the logs of runs on both PRs.

[zifeo]

🚀