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import torch

import cutlass.cute as cute
from cutlass.cute.runtime import from_dlpack


"""Example demonstrating how to use CuTe with PyTorch's FakeTensor mode.

This example shows how to:
1. Use PyTorch's FakeTensor mode to compile a CuTe function without real data
2. Execute the compiled function on real data later

FakeTensor mode allows compiling code without allocating real memory, which is useful
for ahead-of-time compilation scenarios. The compiled function can then be executed
on real tensors that match the expected shapes and dtypes.

Primary goals of this example are to demonstrate: How to use PyTorch's FakeTensor mode with CuTe
to enable ahead-of-time compilation without real data allocation.

The example:
1. Creates a fake tensor in PyTorch using FakeTensor mode
2. Compiles a CuTe function using the fake tensor without allocating real memory
3. Creates a real tensor with matching shape and dtype
4. Executes the compiled function on the real tensor

To run this example:

.. code-block:: bash

    python examples/cute/torch_fake_tensor.py
"""


@cute.jit
def print_tensor(t: cute.Tensor):
    cute.print_tensor(t)


def run():
    from torch._subclasses.fake_tensor import FakeTensorMode

    shape = (3, 4)
    with FakeTensorMode():
        fake_tensor = torch.zeros(shape, dtype=torch.float32)
        compiled_fn = cute.compile(print_tensor, from_dlpack(fake_tensor))

    real_tensor = torch.randn(shape, dtype=torch.float32)
    compiled_fn(from_dlpack(real_tensor))


if __name__ == "__main__":
    run()