# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0

import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect


class ReduceL2(Base):
    @staticmethod
    def export_do_not_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([2], dtype=np.int64)
        keepdims = 0

        node = onnx.helper.make_node(
            "ReduceL2",
            inputs=["data", "axes"],
            outputs=["reduced"],
            keepdims=keepdims,
        )

        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
        # print(data)
        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]

        reduced = np.sqrt(
            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
        )
        # print(reduced)
        # [[2.23606798, 5.],
        # [7.81024968, 10.63014581],
        # [13.45362405, 16.2788206]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_do_not_keepdims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.sqrt(
            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
        )

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_do_not_keepdims_random",
        )

    @staticmethod
    def export_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([2], dtype=np.int64)
        keepdims = 1

        node = onnx.helper.make_node(
            "ReduceL2",
            inputs=["data", "axes"],
            outputs=["reduced"],
            keepdims=keepdims,
        )

        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
        # print(data)
        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]

        reduced = np.sqrt(
            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
        )
        # print(reduced)
        # [[[2.23606798], [5.]]
        # [[7.81024968], [10.63014581]]
        # [[13.45362405], [16.2788206 ]]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_keep_dims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.sqrt(
            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
        )

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_keep_dims_random",
        )

    @staticmethod
    def export_default_axes_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([], dtype=np.int64)
        keepdims = 1

        node = onnx.helper.make_node(
            "ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
        )

        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
        # print(data)
        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]

        reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))
        # print(reduced)
        # [[[25.49509757]]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_default_axes_keepdims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_default_axes_keepdims_random",
        )

    @staticmethod
    def export_negative_axes_keepdims() -> None:
        shape = [3, 2, 2]
        axes = np.array([-1], dtype=np.int64)
        keepdims = 1

        node = onnx.helper.make_node(
            "ReduceL2",
            inputs=["data", "axes"],
            outputs=["reduced"],
            keepdims=keepdims,
        )

        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
        # print(data)
        # [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]

        reduced = np.sqrt(
            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
        )
        # print(reduced)
        # [[[2.23606798], [5.]]
        # [[7.81024968], [10.63014581]]
        # [[13.45362405], [16.2788206 ]]]

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_negative_axes_keep_dims_example",
        )

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.sqrt(
            np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
        )

        expect(
            node,
            inputs=[data, axes],
            outputs=[reduced],
            name="test_reduce_l2_negative_axes_keep_dims_random",
        )
