setup.py

import shutil
import os
from pathlib import Path
from typing import List

from setuptools import find_packages, setup


# Remove stale transformers.egg-info directory to avoid https://github.com/pypa/pip/issues/5466
stale_egg_info = Path(__file__).parent / "glitter.egg-info"
if stale_egg_info.exists():
    print(
        (
            "Warning: {} exists.\n\n"
            "If you recently updated transformers to 3.0 or later, this is expected,\n"
            "but it may prevent transformers from installing in editable mode.\n\n"
            "This directory is automatically generated by Python's packaging tools.\n"
            "I will remove it now.\n\n"
            "See https://github.com/pypa/pip/issues/5466 for details.\n"
        ).format(stale_egg_info)
    )
    shutil.rmtree(stale_egg_info)

extras = {}

VERSION = {}  # type: ignore
with open("src/glitter/version.py", "r") as version_file:
    exec(version_file.read(), VERSION)

_PATH_ROOT = os.path.dirname(__file__)


def _load_requirements(path_dir: str, file_name: str = 'requirements.txt', comment_char: str = '#') -> List[str]:
    """Load requirements from a file
    >>> _load_requirements(_PATH_ROOT)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    ['pytorch-lightning...', 'torch...', ...]
    """
    with open(os.path.join(path_dir, file_name), 'r') as file:
        lines = [ln.strip() for ln in file.readlines()]
    reqs = []
    for ln in lines:
        # filer all comments
        if comment_char in ln:
            ln = ln[:ln.index(comment_char)].strip()
        # skip directly installed dependencies
        if ln.startswith('http'):
            continue
        if ln:  # if requirement is not empty
            reqs.append(ln)
    return reqs


setup(
    name="glitter",
    version=VERSION["VERSION"],
    description="Data Augmentation on NLU downstream tasks, written with PyTorch",
    long_description=open("README.md", "r", encoding="utf-8").read(),
    long_description_content_type="text/markdown",
    keywords="NLP deep learning transformer data augmentation",
    package_dir={"": "src"},
    packages=find_packages("src"),
    install_requires=_load_requirements(_PATH_ROOT),
    extras_require=extras,
    python_requires=">=3.6.0",
    classifiers=[
        "Intended Audience :: Science/Research",
        "Operating System :: OS Independent",
        "Programming Language :: Python :: 3",
        "Programming Language :: Python :: 3.6",
        "Programming Language :: Python :: 3.7",
        "Programming Language :: Python :: 3.8",
        "Topic :: Scientific/Engineering :: Data Augmentation",
    ],
)