# CST / QCST Dual License # Non-commercial research use only. # Commercial use requires explicit permission. # Copyright (c) 2025 Mohamed Mohamed Elhelbawi # All rights reserved. # See LICENSE file in the project root for full license information. SETUP_PY = ''' """ Setup script for Contextual Spectrum Tokenization (CST) """ from setuptools import setup, find_packages import os # Read README file with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() # Read requirements with open("requirements.txt", "r", encoding="utf-8") as fh: requirements = [line.strip() for line in fh if line.strip() and not line.startswith("#")] setup( name="contextual-spectrum-tokenization", version="0.1.0", author="Your Name", author_email="your.email@example.com", description="A production-ready dynamic tokenization architecture for transformer models", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/yourusername/cst-implementation", packages=find_packages(), classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Topic :: Scientific/Engineering :: Artificial Intelligence", ], python_requires=">=3.8", install_requires=requirements, extras_require={ "dev": [ "pytest>=7.3.0", "black>=23.3.0", "flake8>=6.0.0", "mypy>=1.3.0", "pre-commit>=3.3.0", ], "gpu": [ "faiss-gpu>=1.7.4", ], "vision": [ "opencv-python>=4.7.0", "timm>=0.9.0", ], "audio": [ "librosa>=0.10.0", "torchaudio>=2.0.0", ], }, entry_points={ "console_scripts": [ "cst-train=cst.training.train:main", "cst-evaluate=cst.evaluation.evaluate:main", "cst-demo=cst.demo.demo:main", ], }, include_package_data=True, package_data={ "cst": [ "configs/*.yaml", "data/*.json", ], }, ) '''