# Copyright (c) Qualcomm Innovation Center, Inc.
# 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.

import warnings
from typing import Dict

import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper

import numpy as np
import torch
from executorch.backends.qualcomm.utils.constants import QCOM_DATA

from .node_visitor import NodeVisitor, register_node_visitor
from .qnn_constants import OpLayerNorm, QNN_OP_PACKAGE_NAME_QTI_AISW
from .utils import get_parameter


@register_node_visitor
class LayerNormVisitor(NodeVisitor):
    target = ["aten.native_layer_norm.default"]

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

    def define_node(
        self,
        node: torch.fx.Node,
        nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper],
    ) -> PyQnnWrapper.PyQnnOpWrapper:
        input_node = self.get_node(node.args[0])
        input_tensor = self.get_tensor(input_node, node)
        input_tensor_wrapper = self.define_tensor(
            input_node,
            node,
            input_tensor,
            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
            nodes_to_wrappers,
        )

        normalized_shapes = node.args[1]
        if (
            len(normalized_shapes) != 1
            and normalized_shapes[0] != input_tensor.shape[-1]
        ):
            warnings.warn(
                "[QNN Delegate Op Builder]: Only supports normalization with last input dimension.",
                stacklevel=1,
            )
            return
        axis = [len(input_tensor.shape) - 1]
        axis_shape = [len(axis)]

        weight_node = self.get_node(node.args[2])
        weight_tensor = get_parameter(weight_node, self.edge_program)
        weight_tensor_wrapper = self.define_tensor(
            weight_node,
            node,
            weight_tensor,
            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_STATIC,
            nodes_to_wrappers,
        )

        layer_norm_input_tensors = [input_tensor_wrapper, weight_tensor_wrapper]

        bias_node = self.get_node(node.args[3])
        if bias_node is not None:
            bias_tensor = get_parameter(bias_node, self.edge_program)
            bias_tensor_wrapper = self.define_tensor(
                bias_node,
                node,
                bias_tensor,
                PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_STATIC,
                nodes_to_wrappers,
            )
            layer_norm_input_tensors.append(bias_tensor_wrapper)

        epsilon = node.args[4]

        output_tensor = self.get_tensor(node, node, 0)
        output_tensor_wrapper = self.define_tensor(
            node,
            node,
            output_tensor,
            PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
            nodes_to_wrappers,
        )

        layer_norm_op = PyQnnWrapper.PyQnnOpWrapper(
            node.name,
            QNN_OP_PACKAGE_NAME_QTI_AISW,
            OpLayerNorm.op_name,
        )
        layer_norm_op.AddInputTensors(layer_norm_input_tensors)
        layer_norm_op.AddOutputTensors([output_tensor_wrapper])
        layer_norm_op.AddScalarParam(
            OpLayerNorm.param_epsilon,
            PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_FLOAT_32,
            {QCOM_DATA: np.float32(epsilon)},
        )
        layer_norm_op.AddTensorParam(
            OpLayerNorm.param_axes,
            PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
            len(axis_shape),
            axis_shape,
            np.array(axis, dtype=np.uint32),
            True,
        )

        return layer_norm_op