setup.py

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import os
import subprocess
import sys
from setuptools import setup, find_packages, Extension

from setuptools import Extension, find_packages, setup


if sys.version_info < (3, 6):
    sys.exit("Sorry, Python >= 3.6 is required for fairseq.")


def write_version_py():
    with open(os.path.join("fairseq_signals", "version.txt")) as f:
        version = f.read().strip()

    # append latest commit hash to version string
    try:
        sha = (
            subprocess.check_output(["git", "rev-parse", "HEAD"])
            .decode("ascii")
            .strip()
        )
        version += "+" + sha[:7]
    except Exception:
        pass

    # write version info to fairseq/version.py
    with open(os.path.join("fairseq_signals", "version.py"), "w") as f:
        f.write('__version__ = "{}"\n'.format(version))
    return version


version = write_version_py()


with open("README.md") as f:
    readme = f.read()


if sys.platform == "darwin":
    extra_compile_args = ["-stdlib=libc++", "-O3"]
else:
    extra_compile_args = ["-std=c++11", "-O3"]


class NumpyExtension(Extension):
    """Source: https://stackoverflow.com/a/54128391"""

    def __init__(self, *args, **kwargs):
        self.__include_dirs = []
        super().__init__(*args, **kwargs)

    @property
    def include_dirs(self):
        import numpy

        return self.__include_dirs + [numpy.get_include()]

    @include_dirs.setter
    def include_dirs(self, dirs):
        self.__include_dirs = dirs


extensions = [
    NumpyExtension(
        "fairseq_signals.data.data_utils_fast",
        sources=["fairseq_signals/data/data_utils_fast.pyx"],
        language="c++",
        extra_compile_args=extra_compile_args,
    ),
]


cmdclass = {}


try:
    # torch is not available when generating docs
    from torch.utils import cpp_extension

    extensions.extend(
        [
            cpp_extension.CppExtension(
                "fairseq_signals.libbase",
                sources=[
                    "fairseq_signals/clib/libbase/balanced_assignment.cpp",
                ],
            )
        ]
    )

    extensions.extend(
        [
            cpp_extension.CppExtension(
                "fairseq_signals.libnat",
                sources=[
                    "fairseq_signals/clib/libnat/edit_dist.cpp",
                ],
            )
        ]
    )
    if "CUDA_HOME" in os.environ:
        extensions.extend(
            [
                cpp_extension.CppExtension(
                    "fairseq_signals.libnat_cuda",
                    sources=[
                        "fairseq_signals/clib/libnat_cuda/edit_dist.cu",
                        "fairseq_signals/clib/libnat_cuda/binding.cpp",
                    ],
                ),
                cpp_extension.CppExtension(
                    "fairseq_signals.ngram_repeat_block_cuda",
                    sources=[
                        "fairseq_signals/clib/cuda/ngram_repeat_block_cuda.cpp",
                        "fairseq_signals/clib/cuda/ngram_repeat_block_cuda_kernel.cu",
                    ],
                ),
            ]
        )
    cmdclass["build_ext"] = cpp_extension.BuildExtension

except ImportError:
    pass


if "READTHEDOCS" in os.environ:
    # don't build extensions when generating docs
    extensions = []
    if "build_ext" in cmdclass:
        del cmdclass["build_ext"]

    # use CPU build of PyTorch
    dependency_links = [
        "https://download.pytorch.org/whl/cpu/torch-1.7.0%2Bcpu-cp36-cp36m-linux_x86_64.whl"
    ]
else:
    dependency_links = []


if "clean" in sys.argv[1:]:
    # Source: https://bit.ly/2NLVsgE
    print("deleting Cython files...")
    import subprocess

    subprocess.run(
        ["rm -f fairseq_signals/*.so fairseq_signals/**/*.so fairseq_signals/*.pyd fairseq_signals/**/*.pyd"],
        shell=True,
    )


extra_packages = []
# if os.path.exists(os.path.join("fairseq", "model_parallel", "megatron", "mpu")):
#     extra_packages.append("fairseq.model_parallel.megatron.mpu")


def do_setup(package_data):
    setup(
        name="fairseq_signals",
        version=version,
        description="Facebook AI Research Sequence-to-Sequence Toolkit modifed for ecg signals",
        # url="https://github.com/pytorch/fairseq",
        classifiers=[
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
        ],
        long_description=readme,
        long_description_content_type="text/markdown",
        setup_requires=[
            "cython",
            'numpy<1.20.0; python_version<"3.7"',
            'numpy; python_version>="3.7"',
            "setuptools>=18.0",
        ],
        install_requires=[
            "cffi",
            "cython",
            'dataclasses; python_version<"3.7"',
            "hydra-core>=1.0.7,<1.1",
            "omegaconf<2.1",
            'numpy<1.20.0; python_version<"3.7"',
            'numpy; python_version>="3.7"',
            "regex",
            "sacrebleu>=1.4.12",
            "torch",
            "tqdm",
            "scikit-learn",
            "wfdb",
            "transformers",
        ],
        dependency_links=dependency_links,
        packages=find_packages(
            exclude=[
                "examples",
                "examples.*",
                "scripts",
                "scripts.*",
                "tests",
                "tests.*",
            ]
        )
        + extra_packages,
        package_data=package_data,
        ext_modules=extensions,
        test_suite="tests",
        entry_points={
            "console_scripts": [
                # "fairseq-eval-lm = fairseq_cli.eval_lm:cli_main",
                # "fairseq-generate = fairseq_cli.generate:cli_main",
                "fairseq-hydra-train = fairseq_cli.hydra_train:cli_main",
                # "fairseq-interactive = fairseq_cli.interactive:cli_main",
                # "fairseq-preprocess = fairseq_cli.preprocess:cli_main",
                # "fairseq-score = fairseq_cli.score:cli_main",
                "fairseq-train = fairseq_cli.train:cli_main",
                "fairseq-validate = fairseq_cli.validate:cli_main",
                "fairseq-hydra-validate = fairseq_cli.hydra_validate:cli_main",
                "fairseq-inference = fairseq_cli.inference:cli_main",
                "fairseq-hydra-inference = fairseq_cli.hydra_inference:cli_main"
            ],
        },
        cmdclass=cmdclass,
        zip_safe=False,
    )


def get_files(path, relative_to="fairseq_signals"):
    all_files = []
    for root, _dirs, files in os.walk(path, followlinks=True):
        root = os.path.relpath(root, relative_to)
        for file in files:
            if file.endswith(".pyc"):
                continue
            all_files.append(os.path.join(root, file))
    return all_files


if __name__ == "__main__":
    try:
        # symlink examples into fairseq package so package_data accepts them
        fairseq_examples = os.path.join("fairseq_signals", "examples")
        if "build_ext" not in sys.argv[1:] and not os.path.exists(fairseq_examples):
            os.symlink(os.path.join("..", "examples"), fairseq_examples)

        package_data = {
            "fairseq_signals": (
                get_files(fairseq_examples) + get_files(os.path.join("fairseq_signals", "config"))
            )
        }
        do_setup(package_data)
    finally:
        if "build_ext" not in sys.argv[1:] and os.path.islink(fairseq_examples):
            os.unlink(fairseq_examples)