# import setuptools

# version={}
# with open("./jVMC/version.py") as fp:
#     exec(fp.read(), version)

# with open("README.md", "r") as fh:
#     long_description = fh.read()

# DEFAULT_DEPENDENCIES = ["setuptools", "wheel", "numpy>=2", "openfermion", "jax>=0.4.12,<=0.4.31", "jaxlib>=0.4.12,<=0.4.31", "flax>=0.7.0", "mpi4py", "h5py", "PyYAML", "matplotlib", "scipy"] # "scipy<1.13"] # Scipy version restricted, because jax is currently incompatible with new function namespace scipy.sparse.tril
# #CUDA_DEPENDENCIES = ["setuptools", "wheel", "numpy", "jax[cuda]>=0.2.11,<=0.2.25", "flax>=0.3.6,<=0.3.6", "mpi4py", "h5py"]
# DEV_DEPENDENCIES = DEFAULT_DEPENDENCIES + ["sphinx", "mock", "sphinx_rtd_theme", "pytest", "pytest-mpi"]

# setuptools.setup(
#     name='jVMC',
#     version=version['__version__'],
#     author="Markus Schmitt, Moritz Reh",
#     author_email="markus.schmitt@uni-koeln.de",
#     description="jVMC: Versatile and performant variational Monte Carlo",
#     long_description=long_description,
#     long_description_content_type="text/markdown",
#     url="https://jvmc.readthedocs.io/en/latest/#",
#     packages=setuptools.find_packages(),
#     install_requires=DEFAULT_DEPENDENCIES,
#     extras_require={
# #        "cuda": CUDA_DEPENDENCIES
#         "dev": DEV_DEPENDENCIES
#     },
#     classifiers=[
#         "Programming Language :: Python :: 3",
#          "License :: OSI Approved :: MIT License",
#          "Operating System :: OS Independent",
#     ],
# )

from setuptools import setup

setup()