codingutf8 2023 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 2023 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

import numpy as np
from huggingface_hub import hf_hub_download

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

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import NougatImageProcessor


class NougatImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        do_crop_margin=True,
        do_resize=True,
        size=None,
        do_thumbnail=True,
        do_align_long_axis: bool = False,
        do_pad=True,
        do_normalize: bool = 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": 20, "width": 20}
        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_crop_margin = do_crop_margin
        self.do_resize = do_resize
        self.size = size
        self.do_thumbnail = do_thumbnail
        self.do_align_long_axis = do_align_long_axis
        self.do_pad = do_pad
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

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

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

    def prepare_dummy_image(self):
        filepath = hf_hub_download(
            repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
        )
        image = Image.open(filepath).convert("RGB")
        return image

    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 NougatImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = NougatImageProcessor if is_vision_available() else None

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

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

    @cached_property
    def image_processor(self):
        return self.image_processing_class(**self.image_processor_dict)

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

    def test_image_processor_from_dict_with_kwargs(self):
        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size, {"height": 20, "width": 20})

        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
        self.assertEqual(image_processor.size, {"height": 42, "width": 42})

    def test_expected_output(self):
        dummy_image = self.image_processor_tester.prepare_dummy_image()
        image_processor = self.image_processor
        inputs = image_processor(dummy_image, return_tensors="pt")
        self.assertTrue(torch.allclose(inputs["pixel_values"].mean(), torch.tensor(0.4906), atol=1e-3, rtol=1e-3))

    def test_crop_margin_all_white(self):
        image = np.uint8(np.ones((100, 100, 3)) * 255)
        image_processor = self.image_processor
        cropped_image = image_processor.crop_margin(image)
        self.assertTrue(np.array_equal(image, cropped_image))

    def test_crop_margin_centered_black_square(self):
        image = np.ones((100, 100, 3), dtype=np.uint8) * 255
        image[45:55, 45:55, :] = 0
        image_processor = self.image_processor
        cropped_image = image_processor.crop_margin(image)
        expected_cropped = image[45:55, 45:55, :]
        self.assertTrue(np.array_equal(expected_cropped, cropped_image))

    def test_align_long_axis_no_rotation(self):
        image = np.uint8(np.ones((100, 200, 3)) * 255)
        image_processor = self.image_processor
        size = {"height": 200, "width": 300}
        aligned_image = image_processor.align_long_axis(image, size)
        self.assertEqual(image.shape, aligned_image.shape)

    def test_align_long_axis_with_rotation(self):
        image = np.uint8(np.ones((200, 100, 3)) * 255)
        image_processor = self.image_processor
        size = {"height": 300, "width": 200}
        aligned_image = image_processor.align_long_axis(image, size)
        self.assertEqual((200, 100, 3), aligned_image.shape)

    def test_align_long_axis_data_format(self):
        image = np.uint8(np.ones((100, 200, 3)) * 255)
        data_format = "channels_first"
        size = {"height": 200, "width": 300}
        image_processor = self.image_processor
        aligned_image = image_processor.align_long_axis(image, size, data_format=data_format)
        self.assertEqual((3, 100, 200), aligned_image.shape)

    def prepare_dummy_np_image(self):
        filepath = hf_hub_download(
            repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
        )
        image = Image.open(filepath).convert("RGB")
        return np.array(image)

    def test_crop_margin_equality_cv2_python(self):
        image = self.prepare_dummy_np_image()
        image_processor = self.image_processor
        image_cropped_python = image_processor.crop_margin(image)

        self.assertEqual(image_cropped_python.shape, (850, 685, 3))
        self.assertEqual(image_cropped_python.mean(), 237.43881150708458)