# !pip install cloud-tpu-client==0.10 torch==2.0.0 torchvision==0.15.1 https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-2.0-cp310-cp310-linux_x86_64.whl
import torch
import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl
import torch_xla.distributed.xla_multiprocessing as xmp


def astra(mixed_precision=False, use_bfloat16=False, distributed=False):
    def decorator(func):
        if mixed_precision:
            # Use Apex or native amp for mixed precision
            pass  # placeholder

        def wrapper(*args, **kwargs):
            device = xm.xla_device()

            # Convert to bfloat16 if required
            if use_bfloat16:
                args = [
                    arg.to(dtype=torch.bfloat16) for arg in args if torch.is_tensor(arg)
                ]

            # Run the function with XLA tensors
            result = func(*args, **kwargs)

            # Mark step for XLA's lazy execution
            xm.mark_step()

            return result

        return wrapper

    return decorator


# Sample Usage
from torch import nn

data = torch.randn(2, 3)


@astra(use_bfloat16=True)
def forward(x):
    softmax = nn.Softmax(dim=1).to(xm.xla_device())
    result = softmax(x)
    return result


result = forward(data)
print(result)
