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 self size size this function computes the expected height and width when providing images to ideficsimageprocessor assuming doresize is set to true with a scalar size and sizedivisor as we had to reimplement the torchvision transforms using transformers utils we must check they both do the same image convertrgb would only work for jpg images as it creates a wrong background for transparent images the call to alphacomposite handles this case 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 self size size this function computes the expected height and width when providing images to ideficsimageprocessor assuming do_resize is set to true with a scalar size and size_divisor as we had to reimplement the torchvision transforms using transformers utils we must check they both do the same image convert rgb would only work for jpg images as it creates a wrong background for transparent images the call to alpha_composite handles this case
import unittest

from transformers.testing_utils import require_torch, require_torchvision, require_vision
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_torchvision_available():
    import torchvision.transforms as transforms

if is_vision_available():
    from PIL import Image

    from transformers import IdeficsImageProcessor


class IdeficsImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        size=None,
        image_mean=[0.48145466, 0.4578275, 0.40821073],
        image_std=[0.26862954, 0.26130258, 0.27577711],
    ):
        size = size if size is not None else {"shortest_edge": 30}
        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.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,
            "image_size": self.image_size,
        }

    def get_expected_values(self, image_inputs, batched=False):
        
        if not batched:
            size = self.image_size
            image = image_inputs[0]
            if isinstance(image, Image.Image):
                w, h = image.size
            else:
                h, w = image.shape[1], image.shape[2]
            scale = size / min(w, h)
            if h < w:
                newh, neww = size, scale * w
            else:
                newh, neww = scale * h, size

            max_size = int((1333 / 800) * size)
            if max(newh, neww) > max_size:
                scale = max_size / max(newh, neww)
                newh = newh * scale
                neww = neww * scale

            newh, neww = int(newh + 0.5), int(neww + 0.5)
            expected_height, expected_width = (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            expected_values = []
            for image in image_inputs:
                expected_height, expected_width = self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            expected_height = max(expected_values, key=lambda item: item[0])[0]
            expected_width = max(expected_values, key=lambda item: item[1])[1]

        return expected_height, expected_width

    def expected_output_image_shape(self, images):
        height, width = self.get_expected_values(images, batched=True)
        return (self.num_channels, height, 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 IdeficsImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = IdeficsImageProcessor if is_vision_available() else None

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

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

    def test_image_processor_properties(self):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processing, "image_mean"))
        self.assertTrue(hasattr(image_processing, "image_std"))
        self.assertTrue(hasattr(image_processing, "image_size"))

    def test_image_processor_from_dict_with_kwargs(self):
        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertNotEqual(image_processor.image_size, 30)

        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, image_size=42)
        self.assertEqual(image_processor.image_size, 42)

    @require_torchvision
    def test_torchvision_numpy_transforms_equivalency(self):
        
        

        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
        image_processor = self.image_processing_class(**self.image_processor_dict)

        print(image_inputs)

        def convert_to_rgb(image):
            
            
            if image.mode == "RGB":
                return image

            image_rgba = image.convert("RGBA")
            background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
            alpha_composite = Image.alpha_composite(background, image_rgba)
            alpha_composite = alpha_composite.convert("RGB")
            return alpha_composite

        image_size = image_processor.image_size
        image_mean = image_processor.image_mean
        image_std = image_processor.image_std

        transform = transforms.Compose(
            [
                convert_to_rgb,
                transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
                transforms.ToTensor(),
                transforms.Normalize(mean=image_mean, std=image_std),
            ]
        )

        pixel_values_transform_implied = image_processor(image_inputs, transform=None)
        pixel_values_transform_supplied = image_processor(image_inputs, transform=transform)

        torch.testing.assert_close(pixel_values_transform_implied, pixel_values_transform_supplied, rtol=0.0, atol=0.0)

    @unittest.skip("not supported")
    def test_call_numpy(self):
        pass

    @unittest.skip("not supported")
    def test_call_numpy_4_channels(self):
        pass

    @unittest.skip("not supported")
    def test_call_pil(self):
        pass

    @unittest.skip("not supported")
    def test_call_pytorch(self):
        pass