70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# We still provide a setup.py for backwards compatability.
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# But the pyproject.toml should be prefered.
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import itertools
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from pathlib import Path
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from typing import List
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from setuptools import setup, find_packages
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# Environment-specific dependencies for dmc and metaworld
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extras = {
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'dmc': ['shimmy[dm-control]', 'Shimmy==1.0.0'],
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'box2d': ['gymnasium[box2d]>=0.26.0'],
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'mujoco-legacy': ['mujoco-py >=2.1,<2.2', 'cython<3'],
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'jax': ["jax >=0.4.0", "jaxlib >=0.4.0"],
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}
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# All dependencies
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all_groups = set(extras.keys())
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extras["all"] = list(set(itertools.chain.from_iterable(
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map(lambda group: extras[group], all_groups))))
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extras['testing'] = extras["all"] + ['pytest']
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def find_package_data(extensions_to_include: List[str]) -> List[str]:
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envs_dir = Path("fancy_gym/envs/mujoco")
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package_data_paths = []
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for extension in extensions_to_include:
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package_data_paths.extend([str(path)[10:]
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for path in envs_dir.rglob(extension)])
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return package_data_paths
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setup(
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author='Fabian Otto, Onur Celik, Dominik Roth, Hongyi Zhou',
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name='fancy_gym',
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version='1.0',
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classifiers=[
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'Development Status :: 4 - Beta',
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'Intended Audience :: Science/Research',
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'License :: OSI Approved :: MIT License',
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'Natural Language :: English',
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'Operating System :: OS Independent',
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'Topic :: Scientific/Engineering :: Artificial Intelligence',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.9',
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'Programming Language :: Python :: 3.10',
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'Programming Language :: Python :: 3.11',
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],
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extras_require=extras,
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install_requires=[
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'mp_pytorch<=0.1.3',
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'mujoco==2.3.3',
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'gymnasium[mujoco]>=0.26.0'
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],
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packages=[package for package in find_packages(
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) if package.startswith("fancy_gym")],
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package_data={
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"fancy_gym": find_package_data(extensions_to_include=["*.stl", "*.xml"])
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},
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python_requires=">=3.7",
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url='https://github.com/ALRhub/fancy_gym/',
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license='MIT license',
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author_email='',
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description='Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches.'
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)
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