# !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
# !git clone https://github.com/NVIDIA/apex
# %cd apex
# # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key...
# !pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./

import torch
import numpy as np

# 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

try:
    from apex import amp

    _APEX_AVAILABLE = True
except ImportError:
    _APEX_AVAILABLE = False


def astra(mixed_precision=False, backend="auto"):
    def decorator(original_func):
        # Backend-specific Compilation
        if backend == "jax" and _JAX_AVAILABLE:
            compiled_func = jit(original_func)
        elif backend == "triton" and _TRITON_AVAILABLE:
            # Placeholder for Triton's JIT compilation if and when it's available
            compiled_func = original_func  # Modify once Triton provides JIT
        else:
            compiled_func = original_func

        def wrapper(*args, **kwargs):
            # Check device availability and set device based on backend
            if not _XLA_AVAILABLE and not torch.cuda.is_available():
                raise RuntimeError(
                    "Neither XLA (TPU support) nor CUDA (GPU support) is available. Ensure one of them is installed."
                )

            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

            # Auto backend determination
            if backend == "auto":
                if _XLA_AVAILABLE and xm.xla_device().type == "xla":
                    device = xm.xla_device()

            # Adaptive Precision based on device and workload
            dtype = torch.float32
            if device.type == "cuda" and mixed_precision:
                dtype = torch.float16 if _APEX_AVAILABLE else torch.float32
            elif device.type == "xla":
                # Check if the data size benefits from bfloat16
                total_elements = sum(
                    [
                        torch.prod(torch.tensor(arg.shape))
                        for arg in args
                        if torch.is_tensor(arg)
                    ]
                )
                dtype = torch.bfloat16 if total_elements > 1e5 else torch.float32

            args = [
                arg.to(device=device, dtype=dtype) if torch.is_tensor(arg) else arg
                for arg in args
            ]

            # Convert PyTorch tensors to numpy for JAX
            if backend == "jax" and _JAX_AVAILABLE:
                args = [
                    arg.detach().cpu().numpy() if isinstance(arg, torch.Tensor) else arg
                    for arg in args
                ]
                kwargs = {
                    k: v.detach().cpu().numpy() if isinstance(v, torch.Tensor) else v
                    for k, v in kwargs.items()
                }

            # Error Handling and Reporting
            try:
                result = compiled_func(*args, **kwargs)
            except Exception as e:
                raise RuntimeError(
                    f"Error during function execution on {backend} backend: {str(e)}"
                )

            # If the result is a numpy array (from JAX), convert it back to a PyTorch tensor
            if backend == "jax" and _JAX_AVAILABLE and isinstance(result, np.ndarray):
                result = torch.tensor(result, device=device, dtype=dtype)

            # Tensor Fusion for XLA and Memory Management
            if device.type == "xla" and _XLA_AVAILABLE:
                xm.mark_step()

            return result

        return wrapper

    return decorator


# Sample Usage
data = torch.randn(2, 3)


@astra(mixed_precision=True, backend="triton")
def forward(x):
    return x * 2  # Just doubling the data for demonstration


result = forward(data)
print(result)
