# 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 cast, Dict, List

import executorch.backends.qualcomm.python.PyQnnManagerAdaptor as PyQnnManager

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

from .node_visitor import NodeVisitor
from .node_visitor_manager import register_node_visitor
from .qnn_constants import OpPoolAvg2d, QNN_OP_PACKAGE_NAME_QTI_AISW


@register_node_visitor
class AvgPool2d(NodeVisitor):
    target = ["aten.avg_pool2d.default"]

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

    def _get_filter_size(self, node):
        filter_size = cast(List[int], node.args[1])
        if len(filter_size) == 1:
            filter_size = filter_size + filter_size
        return filter_size

    def define_node(
        self,
        node: torch.fx.Node,
        nodes_to_wrappers: Dict[torch.fx.Node, PyQnnManager.TensorWrapper],
    ) -> PyQnnManager.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,
            PyQnnManager.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
            nodes_to_wrappers,
        )

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

        pt_ceil_mode = node.args[4] if len(node.args) > 4 else False

        # kernel info
        input_shape = input_node.meta["val"].shape
        input_h, input_w = input_shape[2], input_shape[3]
        filter_size = self._get_filter_size(node)
        if pt_ceil_mode:
            # filter_size might larger than input_h, input_w, use min of them
            filter_size = [min(filter_size[0], input_h), min(filter_size[1], input_w)]
        filter_size_shape = [len(filter_size)]

        padding = [0, 0]
        if len(node.args) > 3:
            padding = cast(List[int], node.args[3])
            if len(padding) == 1:
                padding = padding + padding
            if pt_ceil_mode:
                ori_filter_h, ori_filter_w = self._get_filter_size(node)
                padding = [
                    0 if ori_filter_h > input_h else padding[0],
                    0 if ori_filter_w > input_w else padding[1],
                ]

        padding_shape = [len(padding), len(padding)]

        # if ceil mode is True, use ceil instead of floor to compute the output shape
        mode = (
            OpPoolAvg2d.RoundingMode.CEIL
            if pt_ceil_mode
            else OpPoolAvg2d.RoundingMode.FLOOR
        )

        # stride info - default to kernel_size if not given
        stride = cast(List[int], node.args[2]) if len(node.args) > 2 else filter_size
        if len(stride) == 1:
            stride = stride + stride
        stride_shape = [len(stride)]

        count_include_pad = True
        if len(node.args) > 5:
            count_include_pad = cast(bool, node.args[5])
        # TODO: If count_include_pad = False, it seems not to compute average with padding in Qnn.
        # But it still compute average with padding value, and change divisor in torch
        # if not count_include_pad:
        #     print("Not support count_include_pad = False.")
        #     return

        pooling_region = filter_size[0] * filter_size[1]
        divisor_override = pooling_region  # Default divisor is pooling_region
        if len(node.args) > 6:
            divisor_override = cast(int, node.args[6])
        if divisor_override != pooling_region:
            warnings.warn(
                "[QNN Delegate Op Builder]: Not support divisor_override which is not equal to pooling region.",
                stacklevel=1,
            )
            return

        avg_pool2d_op = PyQnnManager.PyQnnOpWrapper(
            node.name,
            QNN_OP_PACKAGE_NAME_QTI_AISW,
            OpPoolAvg2d.op_name,
        )
        avg_pool2d_op.AddInputTensors([input_tensor_wrapper])
        avg_pool2d_op.AddOutputTensors([output_tensor_wrapper])

        avg_pool2d_op.AddTensorParam(
            OpPoolAvg2d.param_filter_size,
            PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
            len(filter_size_shape),
            filter_size_shape,
            np.array(
                filter_size,
                dtype=np.uint32,
            ),
            True,
        )
        avg_pool2d_op.AddTensorParam(
            OpPoolAvg2d.param_stride,
            PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
            len(stride_shape),
            stride_shape,
            np.array(
                stride,
                dtype=np.uint32,
            ),
            True,
        )
        avg_pool2d_op.AddTensorParam(
            OpPoolAvg2d.param_pad_amount,
            PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
            len(padding_shape),
            padding_shape,
            np.array(
                [[padding[0], padding[0]], [padding[1], padding[1]]],
                dtype=np.uint32,
            ),
            True,
        )

        avg_pool2d_op.AddScalarParam(
            OpPoolAvg2d.param_rounding_mode,
            PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
            {QCOM_DATA: np.uint32(mode)},
        )
        avg_pool2d_op.AddScalarParam(
            OpPoolAvg2d.param_count_pad_for_edges,
            PyQnnManager.Qnn_DataType_t.QNN_DATATYPE_BOOL_8,
            {QCOM_DATA: count_include_pad},
        )

        return avg_pool2d_op