import torch

# Check for available backends
try:
    import torch_xla.core.xla_model as xm

    _XLA_AVAILABLE = True
except ImportError:
    _XLA_AVAILABLE = False

try:
    import jax
    from jax import jit, grad

    _JAX_AVAILABLE = True
except ImportError:
    _JAX_AVAILABLE = False

try:
    import triton

    _TRITON_AVAILABLE = True
except ImportError:
    _TRITON_AVAILABLE = False


def astra(mixed_precision=False, backend="auto"):
    def decorator(original_func):
        def wrapper(*args, **kwargs):
            if backend == "jax" and not _JAX_AVAILABLE:
                raise ImportError("JAX backend selected but JAX is not installed.")
            if backend == "triton" and not _TRITON_AVAILABLE:
                raise ImportError(
                    "Triton backend selected but Triton is not installed."
                )

            # Assuming XLA and CUDA are available
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

            # Check for TPU
            if _XLA_AVAILABLE and xm.xla_device().type == "xla":
                device = xm.xla_device()

            # For simplicity, just demonstrating JAX's JIT compilation
            func_to_run = original_func
            if backend == "jax" and _JAX_AVAILABLE:
                func_to_run = jit(original_func)

            result = func_to_run(*args, **kwargs)

            if device.type == "xla" and _XLA_AVAILABLE:
                xm.mark_step()

            return result

        return wrapper

    return decorator


# Sample Usage
from torch import nn

data = torch.randn(2, 3)


@astra(mixed_precision=True, backend="jax")
def forward(x):
    softmax = nn.Softmax(dim=1).to(x.device)
    result = softmax(x)
    return result


result = forward(data)
print(result)
