pyproject.toml

[build-system]
requires = ["setuptools>=68.0", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "arke"
version = "0.1.0"
description = "AI-First Operator Description Language & Compiler Toolchain"
readme = "README.md"
license = "Apache-2.0"
requires-python = ">=3.10"
authors = [
    {name = "Arke Contributors"},
]
keywords = ["ai", "compiler", "dsl", "ir", "operator", "optimization", "gpu"]
classifiers = [
    "Development Status :: 2 - Pre-Alpha",
    "Intended Audience :: Developers",
    "Intended Audience :: Science/Research",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Software Development :: Compilers",
]

dependencies = [
    "numpy>=1.24",
    "jinja2>=3.1",
    "httpx>=0.24",
    "lark>=1.1",
    "click>=8.0",
    "rich>=13.0",
]

[project.optional-dependencies]
gpu = [
    "torch>=2.0",
    "triton>=2.0",
]
mlir-gpu = [
    # Phase 3 P3-S1 GPU path: launch MLIR-generated PTX via the CUDA driver
    # API. The Ubuntu mlir-18-tools build has NVPTX (emits PTX) but ships no
    # libmlir_cuda_runtime, so we JIT-load the PTX with cuda-python instead.
    "cuda-python>=12.0",
]
agent = [
    # Live-LLM optimization driver (arke.agent.runner.LLMRunner). Both SDKs
    # are supported transports; the OpenAI-protocol client also drives the
    # yunwu.ai /v1 relay (the clean endpoint — see llm_config.py).
    "anthropic>=0.40",
    "openai>=1.40",
]
dev = [
    "pytest>=7.0",
    "pytest-cov>=4.0",
    "pytest-xdist>=3.0",  # --dist loadfile for FlagGems per-file isolation (C5)
    "ruff>=0.4",
    "mypy>=1.0",
    "jsonschema>=4.20",
    "numpy>=1.24",
]

# [project.scripts]

[project.urls]
Repository = "https://github.com/arke-lang/arke"
Documentation = "https://github.com/arke-lang/arke/tree/main/docs"

[tool.setuptools.packages.find]
where = ["."]
include = ["arke*", "arkec*", "benchmarks*"]

[project.scripts]
arke = "arke.cli:main"

[tool.ruff]
target-version = "py310"
line-length = 100
extend-exclude = ["benchmarks/results"]

[tool.ruff.lint]
select = ["E", "F", "W", "I", "N", "UP"]
ignore = [
    "N803",   # argument name should be lowercase — we use A, B, X for tensors (math convention)
    "N806",   # variable in function should be lowercase — same reason
    "N812",   # lowercase imported as non-lowercase
    "E741",   # ambiguous variable name (l, O, I) — we use these in math context
]

[tool.ruff.lint.per-file-ignores]
"tests/*" = ["E501"]  # Allow longer lines in tests for readability
"arke/agent/prompts.py" = ["E501"]  # System prompt strings are naturally long
"arke/agent/tools_schema.py" = ["E501"]  # JSON schema descriptions are naturally long
"arke/parser/parser.py" = ["N802"]  # Lark transformer methods must match UPPERCASE terminal names

[tool.pytest.ini_options]
testpaths = ["tests"]
markers = [
    "cuda: tests that require CUDA availability",
]
# NOTE: The canonical full-suite command is:
#     pytest tests/ --dist loadfile -n 4
# The `--dist loadfile` flag (pytest-xdist) runs each test FILE in its own
# worker process. This is REQUIRED to get a clean full-suite run: the FlagGems
# baseline (benchmarks/baselines/flaggems.py) permanently hijacks the torch
# `aten` dispatch table at import time via flag_gems.enable(), which cannot be
# reversed in-process (see docs/phase4/audit-2026-07-13.md C5). Per-file worker
# isolation confines that hijack to the workers that actually import FlagGems.
# Without it, ~50 triton-codegen/probe tests fail from cross-file pollution.
# We deliberately do NOT put `-n` in addopts so single-test debugging and IDE
# runs stay in-process; use the Makefile `test` target for the full green run.

[tool.mypy]
python_version = "3.10"
warn_return_any = false
warn_unused_configs = true
disallow_untyped_defs = false