add basic scaffold

setup.py

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+from setuptools import setup, find_packages
+
+setup(
+  name = 'x-transformers',
+  packages = find_packages(),
+  version = '0.0.1',
+  license='MIT',
+  description = 'X-Transformers - Pytorch',
+  author = 'Phil Wang',
+  author_email = '[email protected]',
+  url = 'https://github.com/lucidrains/x-transformers',
+  keywords = [
+    'artificial intelligence',
+    'attention mechanism',
+    'transformers'
+  ],
+  install_requires=[
+    'torch>=1.6',
+    'einops>=0.3'
+  ],
+  classifiers=[
+    'Development Status :: 4 - Beta',
+    'Intended Audience :: Developers',
+    'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    'License :: OSI Approved :: MIT License',
+    'Programming Language :: Python :: 3.6',
+  ],
+)

x_transformers/__init__.py

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+from x_transformers.x_transformers import XTransformer

x_transformers/x_transformers.py

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+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+class XTransformer(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x