# SPDX-License-Identifier: Apache-2.0

import numpy as np  # type: ignore

import onnx
from ..base import Base
from . import expect
from .pool_op_common import get_pad_shape, get_output_shape, pool


class AveragePool(Base):

    @staticmethod
    def export_averagepool_2d_precomputed_pads() -> None:
        """
        input_shape: [1, 1, 5, 5]
        output_shape: [1, 1, 5, 5]
        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[5, 5],
            pads=[2, 2, 2, 2]

        )
        x = np.array([[[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20],
            [21, 22, 23, 24, 25],
        ]]]).astype(np.float32)
        y = np.array([[[[7, 7.5, 8, 8.5, 9],
                        [9.5, 10, 10.5, 11, 11.5],
                        [12, 12.5, 13, 13.5, 14],
                        [14.5, 15, 15.5, 16, 16.5],
                        [17, 17.5, 18, 18.5, 19]]]]).astype(np.float32)

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads')

    @staticmethod
    def export_averagepool_2d_precomputed_pads_count_include_pad() -> None:
        """
        input_shape: [1, 1, 5, 5]
        output_shape: [1, 1, 5, 5]
        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[5, 5],
            pads=[2, 2, 2, 2],
            count_include_pad=1
        )
        x = np.array([[[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20],
            [21, 22, 23, 24, 25],
        ]]]).astype(np.float32)
        y = np.array([[[[2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
                        [4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
                        [7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
                        [6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
                        [6.1200, 8.4000, 10.8000, 8.8800, 6.8400]]]]).astype(np.float32)

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_pads_count_include_pad')

    @staticmethod
    def export_averagepool_2d_precomputed_strides() -> None:
        """
        input_shape: [1, 1, 5, 5]
        output_shape: [1, 1, 2, 2]
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[2, 2],
            strides=[2, 2]
        )
        x = np.array([[[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20],
            [21, 22, 23, 24, 25],
        ]]]).astype(np.float32)
        y = np.array([[[[4, 6],
                        [14, 16]]]]).astype(np.float32)

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_strides')

    @staticmethod
    def export_averagepool_2d_precomputed_same_upper() -> None:
        """
        input_shape: [1, 1, 5, 5]
        output_shape: [1, 1, 3, 3]
        pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[3, 3],
            strides=[2, 2],
            auto_pad='SAME_UPPER'
        )
        x = np.array([[[
            [1, 2, 3, 4, 5],
            [6, 7, 8, 9, 10],
            [11, 12, 13, 14, 15],
            [16, 17, 18, 19, 20],
            [21, 22, 23, 24, 25],
        ]]]).astype(np.float32)
        y = np.array([[[[4, 5.5, 7],
                        [11.5, 13, 14.5],
                        [19, 20.5, 22]]]]).astype(np.float32)

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_precomputed_same_upper')

    @staticmethod
    def export_averagepool_1d_default() -> None:
        """
        input_shape: [1, 3, 32]
        output_shape: [1, 3, 31]
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[2],
        )
        x = np.random.randn(1, 3, 32).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = [2]
        strides = [1]
        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
        padded = x
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'AVG')

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_1d_default')

    @staticmethod
    def export_averagepool_2d_default() -> None:
        """
        input_shape: [1, 3, 32, 32]
        output_shape: [1, 3, 31, 31]
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[2, 2],
        )
        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = (2, 2)
        strides = (1, 1)
        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
        padded = x
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_default')

    @staticmethod
    def export_averagepool_3d_default() -> None:
        """
        input_shape: [1, 3, 32, 32, 32]
        output_shape: [1, 3, 31, 31, 31]
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[2, 2, 2],
        )
        x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = [2, 2, 2]
        strides = [1, 1, 1]
        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
        padded = x
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], 'AVG')

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_3d_default')

    @staticmethod
    def export_averagepool_2d_same_upper() -> None:
        """
        input_shape: [1, 3, 32, 32]
        output_shape: [1, 3, 32, 32]
        pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[2, 2],
            auto_pad='SAME_UPPER'
        )
        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = (2, 2)
        strides = (1, 1)
        out_shape = get_output_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides)
        pad_shape = get_pad_shape('SAME_UPPER', x_shape[2:], kernel_shape, strides, out_shape)
        pad_top = pad_shape[0] // 2
        pad_bottom = pad_shape[0] - pad_top
        pad_left = pad_shape[1] // 2
        pad_right = pad_shape[1] - pad_left
        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
                        constant_values=np.nan)
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_upper')

    @staticmethod
    def export_averagepool_2d_same_lower() -> None:
        """
        input_shape: [1, 3, 32, 32]
        output_shape: [1, 3, 32, 32]
        pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[2, 2],
            auto_pad='SAME_LOWER'
        )
        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = (2, 2)
        strides = (1, 1)
        out_shape = get_output_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides)
        pad_shape = get_pad_shape('SAME_LOWER', x_shape[2:], kernel_shape, strides, out_shape)
        pad_bottom = pad_shape[0] // 2
        pad_top = pad_shape[0] - pad_bottom
        pad_right = pad_shape[1] // 2
        pad_left = pad_shape[1] - pad_right
        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
                        constant_values=np.nan)
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_same_lower')

    @staticmethod
    def export_averagepool_2d_pads() -> None:
        """
        input_shape: [1, 3, 28, 28]
        output_shape: [1, 3, 30, 30]
        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[3, 3],
            pads=[2, 2, 2, 2]
        )
        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = (3, 3)
        strides = (1, 1)
        pad_bottom = 2
        pad_top = 2
        pad_right = 2
        pad_left = 2
        pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
        out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
                        constant_values=np.nan)
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG')

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads')

    @staticmethod
    def export_averagepool_2d_pads_count_include_pad() -> None:
        """
        input_shape: [1, 3, 28, 28]
        output_shape: [1, 3, 30, 30]
        pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[3, 3],
            pads=[2, 2, 2, 2],
            count_include_pad=1,
        )
        x = np.random.randn(1, 3, 28, 28).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = (3, 3)
        strides = (1, 1)
        pad_bottom = 2
        pad_top = 2
        pad_right = 2
        pad_left = 2
        pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
        out_shape = get_output_shape('VALID', np.add(x_shape[2:], pad_shape), kernel_shape, strides)
        padded = np.pad(x, ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)), mode='constant',
                        constant_values=0)
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, 'AVG', count_include_pad=1)

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_pads_count_include_pad')

    @staticmethod
    def export_averagepool_2d_strides() -> None:
        """
        input_shape: [1, 3, 32, 32]
        output_shape: [1, 3, 10, 10]
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[5, 5],
            strides=[3, 3]
        )
        x = np.random.randn(1, 3, 32, 32).astype(np.float32)
        x_shape = np.shape(x)
        kernel_shape = (5, 5)
        strides = (3, 3)
        out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
        padded = x
        y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), 'AVG')

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_strides')

    @staticmethod
    def export_averagepool_2d_ceil() -> None:
        """
        input_shape: [1, 1, 4, 4]
        output_shape: [1, 1, 2, 2]
        """
        node = onnx.helper.make_node(
            'AveragePool',
            inputs=['x'],
            outputs=['y'],
            kernel_shape=[3, 3],
            strides=[2, 2],
            ceil_mode=True
        )
        x = np.array([[[
            [1, 2, 3, 4],
            [5, 6, 7, 8],
            [9, 10, 11, 12],
            [13, 14, 15, 16],
        ]]]).astype(np.float32)
        y = np.array([[[
            [6, 7.5],
            [12, 13.5]]]]).astype(np.float32)

        expect(node, inputs=[x], outputs=[y], name='test_averagepool_2d_ceil')