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