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


@op_debug_context
def logical_and(lhs: Tensor, rhs: Tensor) -> Tensor:
    """
    Compute the elementwise logical `and` of two tensors.

    Follows NumPy broadcasting rules.
    Inputs will be cast to bool if necessary.

    See also `PyTorch Tensor.logical_and <https://pytorch.org/docs/stable/generated/torch.Tensor.logical_and.html>`__, `NumPy logical_and <https://numpy.org/doc/stable/reference/generated/numpy.logical_and.html>`__.

    Args:
        lhs, rhs:
            Tensor to be combined.
    Returns:
        Tensor:
            A new tensor with the result of elementwise `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)

    lhs = cast_if_needed(lhs, dtypes.bool)
    rhs = cast_if_needed(rhs, dtypes.bool)

    settings = ctx._get_op_settings("and")
    opid = _ir.OperatorIdentifier("ai.onnx", "And", 7, _ir.NumInputs(2, 2), 1)
    op = pb_g.createConnectedOp_AndOp(
        {0: lhs.id, 1: rhs.id},
        {
            0: g._create_tensor_id("and_out"),
        },
        opid,
        settings,
    )

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