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"""Wrapping functions to bridge frameworks with DLPack support to TVM"""
from tvm.runtime import ndarray


def convert_func(tvm_func, tensor_type, to_dlpack_func):
    """Convert a tvm function into one that accepts a tensor from another
       framework, provided the other framework supports DLPACK

    Parameters
    ----------
    tvm_func: Function
        Built tvm function operating on arrays

    tensor_type: Type
        Type of the tensors of the target framework

    to_dlpack_func: Function
        Function to convert the source tensors to DLPACK
    """
    assert callable(tvm_func)

    def _wrapper(*args):
        args = tuple(
            ndarray.from_dlpack(to_dlpack_func(arg)) if isinstance(arg, tensor_type) else arg
            for arg in args
        )
        return tvm_func(*args)

    return _wrapper


def to_pytorch_func(tvm_func):
    """Convert a tvm function into one that accepts PyTorch tensors

    Parameters
    ----------
    tvm_func: Function
        Built tvm function operating on arrays

    Returns
    -------
    wrapped_func: Function
        Wrapped tvm function that operates on PyTorch tensors
    """
    # pylint: disable=import-outside-toplevel
    import torch
    import torch.utils.dlpack

    return convert_func(tvm_func, torch.Tensor, torch.utils.dlpack.to_dlpack)
