codingutf8 2021 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license coding utf 8 2021 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license
import unittest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_vision_available():
    from transformers import ViTImageProcessor


class EfficientFormerImageProcessorTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=13,
        num_channels=3,
        image_size=224,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
    ):
        size = size if size is not None else {"height": 18, "width": 18}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

    def prepare_image_processor_dict(self):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }

    def expected_output_image_shape(self, images):
        return self.num_channels, self.size["height"], self.size["width"]

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )


@require_torch
@require_vision
class EfficientFormerImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = ViTImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = EfficientFormerImageProcessorTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_proc_properties(self):
        image_processor = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processor, "image_mean"))
        self.assertTrue(hasattr(image_processor, "image_std"))
        self.assertTrue(hasattr(image_processor, "do_normalize"))
        self.assertTrue(hasattr(image_processor, "do_resize"))
        self.assertTrue(hasattr(image_processor, "size"))