"""The interface of expr function exposed from C++."""
from __future__ import absolute_import

import logging
from ... import build_module as _build
from ... import container as _container
from ..._ffi.function import _init_api, register_func


@register_func("relay.backend.lower")
def lower(sch, inputs, func_name, source_func):
    """Backend function for lowering.

    Parameters
    ----------
    sch : tvm.Schedule
        The schedule.

    inputs : List[tvm.Tensor]
        The inputs to the function.

    func_name : str
        The name of the function.

    source-func : tvm.relay.Function
        The source function to be lowered.

    Returns
    -------
    lowered_funcs : List[tvm.LoweredFunc]
        The result of lowering.
    """
    import traceback
    # pylint: disable=broad-except
    try:
        f = _build.lower(sch, inputs, name=func_name)
        logging.debug("lower function %s", func_name)
        logging.debug("%s", _build.lower(sch, inputs, simple_mode=True))
    except Exception:
        msg = traceback.format_exc()
        msg += "Error during compile function\n"
        msg += "-----------------------------\n"
        msg += source_func.astext()
        raise RuntimeError(msg)
    return f if isinstance(
        f, (_container.Array, tuple, list)) else [f]


@register_func("relay.backend.build")
def build(funcs, target, target_host=None):
    """Backend build function.

    Parameters
    ----------
    funcs : List[tvm.LoweredFunc] or Dict[str, List[tvm.LoweredFunc]]
        A list of lowered functions or dictionary mapping from targets to
        lowered functions.


    target : tvm.Target
        The target to run the code on.

    target_host : tvm.Target
        The host target.

    Returns
    -------
    module : tvm.Module
        The runtime module.
    """
    if target_host == "":
        target_host = None
    return _build.build(funcs, target=target, target_host=target_host)


@register_func("relay._tensor_value_repr")
def _tensor_value_repr(tvalue):
    return str(tvalue.data.asnumpy())


@register_func("relay._constant_repr")
def _tensor_constant_repr(tvalue):
    return str(tvalue.data.asnumpy())


_init_api("relay.backend", __name__)