fancy_gym/setup.py

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import itertools
from pathlib import Path
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
extras = {
'dmc': ['shimmy[dm-control]', 'Shimmy==1.0.0'],
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'metaworld': ['metaworld @ git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld'],
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'box2d': ['gymnasium[box2d]>=0.26.0'],
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'mujoco': ['mujoco==2.3.3', 'gymnasium[mujoco]>0.26.0'],
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'mujoco-legacy': ['mujoco-py >=2.1,<2.2', 'cython<3'],
'jax': ["jax >=0.4.0", "jaxlib >=0.4.0"],
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}
# All dependencies
all_groups = set(extras.keys())
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extras["all"] = list(set(itertools.chain.from_iterable(
map(lambda group: extras[group], all_groups))))
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extras['testing'] = extras["all"] + ['pytest']
def find_package_data(extensions_to_include: List[str]) -> List[str]:
envs_dir = Path("fancy_gym/envs/mujoco")
package_data_paths = []
for extension in extensions_to_include:
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package_data_paths.extend([str(path)[10:]
for path in envs_dir.rglob(extension)])
return package_data_paths
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setup(
author='Fabian Otto, Onur Celik, Dominik Roth, Hongyi Zhou',
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name='fancy_gym',
version='1.0',
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classifiers=[
'Development Status :: 4 - Beta',
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'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
'Natural Language :: English',
'Operating System :: OS Independent',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
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],
extras_require=extras,
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install_requires=[
'gymnasium>=0.26.0',
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'mp_pytorch<=0.1.3'
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],
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packages=[package for package in find_packages(
) if package.startswith("fancy_gym")],
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package_data={
"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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)