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import inspect
import textwrap
import re
import itertools
import numbers
import importlib
import sys
import functools
from pathlib import Path
from utils3d._helpers import suppress_traceback
def _contains_tensor(obj):
if isinstance(obj, (list, tuple)):
return any(_contains_tensor(item) for item in obj)
elif isinstance(obj, dict):
return any(_contains_tensor(value) for value in obj.values())
else:
import torch
return isinstance(obj, torch.Tensor)
@suppress_traceback
def _call_based_on_args(fname, args, kwargs):
if 'torch' in sys.modules:
if any(_contains_tensor(arg) for arg in args) or any(_contains_tensor(v) for v in kwargs.values()):
fn = getattr(utils3d.torch, fname, None)
if fn is None:
raise NotImplementedError(f"Function {fname} has no torch implementation.")
return fn(*args, **kwargs)
fn = getattr(utils3d.numpy, fname, None)
if fn is None:
raise NotImplementedError(f"Function {fname} has no numpy implementation.")
return fn(*args, **kwargs)
def extract_signature(fn):
signature = inspect.signature(fn)
signature_str = str(signature)
signature_str = re.sub(r"<class '.*'>", lambda m: m.group(0).split('\'')[1], signature_str)
signature_str = re.sub(r"(?<!\.)numpy\.", "numpy_.", signature_str)
signature_str = re.sub(r"(?<!\.)torch\.", "torch_.", signature_str)
return signature_str
if __name__ == "__main__":
import utils3d.numpy, utils3d.torch
numpy_impl = utils3d.numpy
torch_impl = utils3d.torch
numpy_funcs = {name: getattr(numpy_impl, name) for name in numpy_impl.__all__}
torch_funcs = {name: getattr(torch_impl, name) for name in torch_impl.__all__}
all = {**numpy_funcs, **torch_funcs}
Path("utils3d/_unified").mkdir(exist_ok=True)
with open("utils3d/_unified/__init__.pyi", "w", encoding="utf-8") as f:
f.write(inspect.cleandoc(
f"""
# Auto-generated interface file
from typing import List, Tuple, Dict, Union, Optional, Any, overload, Literal, Callable
import numpy as numpy_
import torch as torch_
import nvdiffrast.torch
import numbers
from . import numpy, torch
import utils3d.numpy, utils3d.torch
"""
))
f.write("\n\n")
f.write(f"__all__ = [{', \n'.join('\"' + s + '\"' for s in all.keys())}]\n\n")
for fname, fn in itertools.chain(numpy_funcs.items(), torch_funcs.items()):
sig, doc = extract_signature(fn), inspect.getdoc(fn)
f.write(f"@overload\n")
f.write(f"def {fname}{sig}:\n")
f.write(f" \"\"\"{doc}\"\"\"\n" if doc else "")
f.write(f" {fn.__module__}.{fn.__qualname__}\n\n")
with open("utils3d/_unified/__init__.py", "w", encoding="utf-8") as f:
f.write(inspect.cleandoc(
f"""
# Auto-generated implementation redirecting to numpy/torch implementations
import sys
from typing import TYPE_CHECKING
import utils3d
from .._helpers import suppress_traceback
"""
))
f.write("\n\n")
f.write(f"__all__ = [{', \n'.join('\"' + s + '\"' for s in all.keys())}]\n\n")
f.write(inspect.getsource(_contains_tensor) + "\n\n")
f.write(inspect.getsource(_call_based_on_args) + "\n\n")
for fname in {**numpy_funcs, **torch_funcs}:
f.write(f'@suppress_traceback\n')
f.write(f"def {fname}(*args, **kwargs):\n")
f.write(f" if TYPE_CHECKING: # redirected to:\n")
f.write(f" {'utils3d.numpy.' + fname if fname in numpy_funcs else 'None'}, {'utils3d.torch.'+ fname if fname in torch_funcs else 'None'}\n")
f.write(f" return _call_based_on_args('{fname}', args, kwargs)\n\n")
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