text: """ $lic$
  Copyright (C) 2016-2017 by The Board of Trustees of Stanford University
  
  This program is free software: you can redistribute it and/or modify it under
  the terms of the Modified BSD-3 License as published by the Open Source
  Initiative.
  
  If you use this program in your research, we request that you reference the
  TETRIS paper ("TETRIS: Scalable and Efficient Neural Network Acceleration with
  3D Memory", in ASPLOS'17. April, 2017), and that you send us a citation of your
  work.
  
  This program is distributed in the hope that it will be useful, but WITHOUT ANY
  WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
  PARTICULAR PURPOSE. See the BSD-3 License for more details.
  
  You should have received a copy of the Modified BSD-3 License along with this
  program. If not, see <https://opensource.org/licenses/BSD-3-Clause>.
  """
  
  import os
  import re
  import setuptools
  
  PACKAGE = 'nn_dataflow'
  DESC = 'Explore the energy-efficient dataflow scheduling for neural networks.'
  
  def _get_version():
      here = os.path.abspath(os.path.dirname(__file__))
      with open(os.path.join(here, PACKAGE, '__init__.py'), 'r') as fh:
          matches = re.findall(r'^\s*__version__\s*=\s*[\'"]([^\'"]+)[\'"]',
                               fh.read(), re.M)
          if matches:
              return matches[-1]
      return '0.0.0'
  
  def _readme():
      here = os.path.abspath(os.path.dirname(__file__))
      with open(os.path.join(here, 'README.rst'), 'r') as fh:
          return fh.read()
  
  setuptools.setup(
      name=PACKAGE,
      version=_get_version(),
      description=DESC,
  
      author='Mingyu Gao',
      author_email='mgao12@stanford.edu',
      long_description=_readme(),
      url='https://github.com/stanford-mast/nn_dataflow',
      license='BSD 3-clause',
  
      packages=setuptools.find_packages(),
  
      install_requires=[
          'argparse',
          'coverage>=4',
          'pytest>=3',
          'pytest-cov>=2',
          'pytest-xdist>=1',
      ],
  
      entry_points={
          'console_scripts': [
              'nn_dataflow_search=nn_dataflow.tools.nn_dataflow_search:main',
          ]
      },
  
      keywords='neural-network scheduling dataflow optimizer',
      classifiers=[
          'Development Status :: 3 - Alpha',
          'Intended Audience :: Developers',
          'Intended Audience :: Science/Research',
          'License :: OSI Approved :: BSD License',
          'Programming Language :: Python :: 2.7',
          'Topic :: Scientific/Engineering :: Artificial Intelligence',
          'Topic :: System :: Hardware',
      ],
  )
  
  