# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

from typing import Dict

import torch
from executorch.backends.xnnpack.operators.node_visitor import (
    NodeVisitor,
    register_node_visitor,
)
from executorch.backends.xnnpack.operators.quant_params import QuantParams
from executorch.backends.xnnpack.serialization.xnnpack_graph_schema import (
    OutputMinMax,
    XNNGraph,
    XNNMultiply,
    XNode,
)

from executorch.backends.xnnpack.utils.utils import get_input_node, get_relu_fused_node
from executorch.exir.dialects._ops import ops as exir_ops


@register_node_visitor
class MultiplyVisitor(NodeVisitor):
    target = "aten.mul.Tensor"

    def __init__(self, *args) -> None:
        super().__init__(*args)

    def define_node(
        self,
        node: torch.fx.Node,
        xnn_graph: XNNGraph,
        vals_to_ids: Dict[torch.fx.Node, int],
        debug_handle: int,
    ) -> None:
        # input1
        input1 = get_input_node(node, 0)
        self.define_tensor(
            input1,
            xnn_graph,
            vals_to_ids,
            quant_params=QuantParams.from_inputs(input1, self._exported_program),
        )
        input1_id = vals_to_ids[input1]

        # input2
        input2 = get_input_node(node, 1)
        self.define_tensor(
            input2,
            xnn_graph,
            vals_to_ids,
            quant_params=QuantParams.from_inputs(input2, self._exported_program),
        )
        input2_id = vals_to_ids[input2]

        # output
        output_node = get_relu_fused_node(node) or node
        output_min_max = None
        # if fused with relu
        if output_node.target == exir_ops.edge.aten.relu.default:
            output_node.meta["XNNPACK_FUSED"] = True
            output_min_max = OutputMinMax(output_min=0, output_max="+inf")

        self.define_tensor(
            output_node,
            xnn_graph,
            vals_to_ids,
            quant_params=QuantParams.from_outputs(output_node),
        )

        output_id = vals_to_ids[output_node]

        ser_node = XNode(
            xnode_union=XNNMultiply(
                input1_id=input1_id, input2_id=input2_id, output_id=output_id, flags=0
            ),
            debug_handle=debug_handle,
            output_min_max=output_min_max,
        )
        xnn_graph.xnodes.append(ser_node)