pyproject.toml

[project]
name = "refiners"
version = "0.3.1"
description = "The simplest way to train and run adapters on top of foundation models"
authors = [{ name = "The Finegrain Team", email = "bonjour@lagon.tech" }]
license = "MIT"
dependencies = [
    "torch>=2.1.1",
    "safetensors>=0.4.0",
    "pillow>=10.1.0",
    "jaxtyping>=0.2.23",
    "packaging>=23.2",
]
readme = "README.md"
requires-python = ">= 3.10"
classifiers = [
    "Typing :: Typed",
    "Intended Audience :: Developers",
    "Intended Audience :: Science/Research",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Topic :: Software Development :: Libraries :: Python Modules",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "License :: OSI Approved :: MIT License",
]

[project.optional-dependencies]
training = [
    "bitsandbytes>=0.41.2.post2",
    "pydantic>=2.5.2",
    "prodigyopt>=1.0",
    "torchvision>=0.16.1",
    "loguru>=0.7.2",
    "wandb>=0.16.0",
    "datasets>=2.15.0",
    "tomli>=2.0.1",
]
test = [
    "diffusers>=0.26.1",
    "transformers>=4.35.2",
    "piq>=0.8.0",
    "invisible-watermark>=0.2.0",
    "torchvision>=0.16.1",
    # An unofficial Python package for Meta AI's Segment Anything Model:
    # https://github.com/opengeos/segment-anything
    "segment-anything-py>=1.0",
]
conversion = [
    "diffusers>=0.26.1",
    "transformers>=4.35.2",
    "segment-anything-py>=1.0",
    "requests>=2.26.0",
    "tqdm>=4.62.3",
]
doc = [
    # required by mkdocs to format the signatures
    "black>=24.1.1",
    "mkdocs-material>=9.5.6",
    "mkdocstrings[python]>=0.24.0",
    "mkdocs-literate-nav>=0.6.1",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"


[tool.rye]
managed = true
dev-dependencies = [
    "pyright==1.1.349",
    "ruff>=0.2.0",
    "docformatter>=1.7.5",
    "pytest>=8.0.0",
    "coverage>=7.4.1",
    "typos>=1.18.2",
]


[tool.hatch.metadata]
allow-direct-references = true

[tool.rye.scripts]
lint = { chain = ["ruff format .", "ruff --fix ."] }
serve-docs = "mkdocs serve"
test-cov = "coverage run -m pytest"
# Work around for "Couldn't parse" errors due to e.g. opencv-python:
# https://github.com/nedbat/coveragepy/issues/1653
build-html-cov = { cmd = "coverage html", env = { PYTHONWARNINGS = "ignore:Couldn't parse::coverage.report_core" } }
serve-cov-report = { chain = [
    "build-html-cov",
    "python -m http.server 8080 -b 127.0.0.1 -d htmlcov",
] }

[tool.black]
line-length = 120

[tool.ruff]
src = ["src"] # see https://docs.astral.sh/ruff/settings/#src
line-length = 120

[tool.ruff.lint]
select = [
    "I", # isort
]
ignore = [
    "F722", # forward-annotation-syntax-error, because of Jaxtyping
    "E731", # do-not-assign-lambda
]

[tool.ruff.lint.isort]
# Allow this kind of import on a single line:
#
#     from torch import device as Device, dtype as DType
#
combine-as-imports = true

[tool.docformatter]
black = true

[tool.pyright]
include = ["src/refiners", "tests", "scripts"]
strict = ["*"]
exclude = ["**/__pycache__", "tests/weights"]
reportMissingTypeStubs = "warning"

[tool.coverage.run]
branch = true
source = ["src/refiners"]

# Also apply to HTML output, where appropriate
[tool.coverage.report]
ignore_errors = true # see `build-html-cov` for details
exclude_also = [
    "def __repr__",
    "raise NotImplementedError",
    "if TYPE_CHECKING:",
    "class .*\\bProtocol\\):",
    "@(abc\\.)?abstractmethod",
]

[tool.typos.default]
extend-words = { adaptee = "adaptee" }
extend-ignore-identifiers-re = ["NDArray*"]