# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
import popart._internal.ir as _ir
from popxl.context import get_current_context, op_debug_context
from popxl.tensor import Tensor
from .utils import check_in_graph, check_tensor_ipu_and_tile_set


@op_debug_context
def mul(lhs: Tensor, rhs: Tensor) -> Tensor:
    """
    Multiply two tensors elementwise.

    Follows NumPy broadcasting rules.
    Arguments must have the same dtype.

    See also `PyTorch Tensor.mul <https://pytorch.org/docs/stable/generated/torch.Tensor.mul.html>`__, `ONNX Mul <https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mul>`__.

    Args:
        lhs, rhs (Tensor):
            Tensors to be multiplied.
    Returns:
        Tensor:
            The product of `lhs` and `rhs`.
    """
    ctx = get_current_context()
    g = ctx.graph
    pb_g = g._pb_graph

    check_in_graph(g, lhs=lhs, rhs=rhs)
    check_tensor_ipu_and_tile_set(lhs=lhs, rhs=rhs)

    settings = ctx._get_op_settings("mul")
    opid = _ir.OperatorIdentifier("ai.onnx", "Mul", 7, _ir.NumInputs(2, 2), 1)

    op = pb_g.createConnectedOp_MulOp(
        {0: lhs.id, 1: rhs.id},
        {
            0: g._create_tensor_id("mul_out"),
        },
        opid,
        settings,
    )

    return Tensor._from_pb_tensor(op.outTensor(0))