test 3d input shape 123 test 4d input shape 2123 test 3d input shape 213 test 4d input shape 2213 test 3d input shape 2 1 3 test 4d input shape 2 2 1 3 test 3d input shape 1 2 3 test 4d input shape 2 1 2 3 test 3d input shape 2 1 3 test 4d input shape 2 2 1 3 test 3d input shape 2 1 3 test 4d input shape 2 2 1 3
import unittest.mock

import numpy as np
from absl.testing import parameterized
from tensorflow import data as tf_data

from keras import backend
from keras import layers
from keras import testing
from keras import utils


class MockedRandomFlip(layers.RandomFlip):
    def call(self, inputs, training=True):
        unbatched = len(inputs.shape) == 3
        batch_size = 1 if unbatched else self.backend.shape(inputs)[0]
        mocked_value = self.backend.numpy.full(
            (batch_size, 1, 1, 1), 0.1, dtype="float32"
        )
        with unittest.mock.patch.object(
            self.backend.random,
            "uniform",
            return_value=mocked_value,
        ):
            out = super().call(inputs, training=training)
        return out


class RandomFlipTest(testing.TestCase, parameterized.TestCase):
    @parameterized.named_parameters(
        ("random_flip_horizontal", "horizontal"),
        ("random_flip_vertical", "vertical"),
        ("random_flip_both", "horizontal_and_vertical"),
    )
    def test_random_flip(self, mode):
        run_training_check = False if backend.backend() == "numpy" else True
        self.run_layer_test(
            layers.RandomFlip,
            init_kwargs={
                "mode": mode,
            },
            input_shape=(2, 3, 4),
            expected_output_shape=(2, 3, 4),
            supports_masking=False,
            run_training_check=run_training_check,
        )

    def test_random_flip_horizontal(self):
        run_training_check = False if backend.backend() == "numpy" else True
        utils.set_random_seed(0)
        
        self.run_layer_test(
            MockedRandomFlip,
            init_kwargs={
                "mode": "horizontal",
                "seed": 42,
            },
            input_data=np.asarray([[[2, 3, 4], [5, 6, 7]]]),
            expected_output=backend.convert_to_tensor([[[5, 6, 7], [2, 3, 4]]]),
            supports_masking=False,
            run_training_check=run_training_check,
        )
        
        self.run_layer_test(
            MockedRandomFlip,
            init_kwargs={
                "mode": "horizontal",
                "seed": 42,
            },
            input_data=np.asarray(
                [
                    [[[2, 3, 4], [5, 6, 7]]],
                    [[[2, 3, 4], [5, 6, 7]]],
                ]
            ),
            expected_output=backend.convert_to_tensor(
                [
                    [[[5, 6, 7], [2, 3, 4]]],
                    [[[5, 6, 7], [2, 3, 4]]],
                ]
            ),
            supports_masking=False,
            run_training_check=run_training_check,
        )

    def test_random_flip_vertical(self):
        run_training_check = False if backend.backend() == "numpy" else True
        utils.set_random_seed(0)
        
        self.run_layer_test(
            MockedRandomFlip,
            init_kwargs={
                "mode": "vertical",
                "seed": 42,
            },
            input_data=np.asarray([[[2, 3, 4]], [[5, 6, 7]]]),
            expected_output=backend.convert_to_tensor(
                [[[5, 6, 7]], [[2, 3, 4]]]
            ),
            supports_masking=False,
            run_training_check=run_training_check,
        )
        
        self.run_layer_test(
            MockedRandomFlip,
            init_kwargs={
                "mode": "vertical",
                "seed": 42,
            },
            input_data=np.asarray(
                [
                    [
                        [[2, 3, 4]],
                        [[5, 6, 7]],
                    ],
                    [
                        [[2, 3, 4]],
                        [[5, 6, 7]],
                    ],
                ]
            ),
            expected_output=backend.convert_to_tensor(
                [
                    [[[5, 6, 7]], [[2, 3, 4]]],
                    [[[5, 6, 7]], [[2, 3, 4]]],
                ]
            ),
            supports_masking=False,
            run_training_check=run_training_check,
        )

    def test_tf_data_compatibility(self):
        
        layer = layers.RandomFlip("vertical", seed=42)
        input_data = np.array([[[2, 3, 4]], [[5, 6, 7]]])
        expected_output = np.array([[[5, 6, 7]], [[2, 3, 4]]])
        ds = tf_data.Dataset.from_tensor_slices(input_data).batch(2).map(layer)
        for output in ds.take(1):
            output = output.numpy()
        self.assertAllClose(output, expected_output)
        
        layer = layers.RandomFlip("vertical", seed=42)
        input_data = np.array(
            [
                [
                    [[2, 3, 4]],
                    [[5, 6, 7]],
                ],
                [
                    [[2, 3, 4]],
                    [[5, 6, 7]],
                ],
            ]
        )
        expected_output = np.array(
            [
                [[[5, 6, 7]], [[2, 3, 4]]],
                [[[5, 6, 7]], [[2, 3, 4]]],
            ]
        )
        ds = tf_data.Dataset.from_tensor_slices(input_data).batch(2).map(layer)
        for output in ds.take(1):
            output = output.numpy()
        self.assertAllClose(output, expected_output)