# SPDX-License-Identifier: Apache-2.0

import numpy as np  # type: ignore

import onnx
from ..base import Base
from . import expect


class ReduceMin(Base):

    @staticmethod
    def export_do_not_keepdims() -> None:
        shape = [3, 2, 2]
        axes = [1]
        keepdims = 0

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

        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
        #print(reduced)
        #[[5., 1.]
        # [30., 1.]
        # [55., 1.]]

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_example')

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_do_not_keepdims_random')

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

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

        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
        #print(reduced)
        #[[[5., 1.]]
        # [[30., 1.]]
        # [[55., 1.]]]

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_example')

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_keepdims_random')

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

        node = onnx.helper.make_node(
            'ReduceMin',
            inputs=['data'],
            outputs=['reduced'],
            keepdims=keepdims)

        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
        reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
        #print(reduced)
        #[[[1.]]]

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_example')

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_default_axes_keepdims_random')

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

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

        data = np.array([[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.float32)
        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
        # print(reduced)
        #[[[5., 1.]]
        # [[30., 1.]]
        # [[55., 1.]]]

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_example')

        np.random.seed(0)
        data = np.random.uniform(-10, 10, shape).astype(np.float32)
        reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)

        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_min_negative_axes_keepdims_random')