codingutf8 2022 meta platforms s and 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 initialize imageprocessing create random pil images test not batched input test no bool masked pos test batched test no bool masked pos initialize imageprocessing create random tensors test not batched input test batched test masking initialize imageprocessing test not batched input initialize imageprocessing create random pil images test not batched input test batched coding utf 8 2022 meta platforms s and 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 initialize image_processing create random pil images test not batched input test no bool masked pos test batched test no bool masked pos initialize image_processing create random tensors test not batched input test batched test masking initialize image_processing test not batched input initialize image_processing create random pil images test not batched input test batched
import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import 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():
    import PIL

    from transformers import FlavaImageProcessor
    from transformers.image_utils import PILImageResampling
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )
else:
    FLAVA_IMAGE_MEAN = FLAVA_IMAGE_STD = FLAVA_CODEBOOK_MEAN = FLAVA_CODEBOOK_STD = None


class FlavaImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        do_center_crop=True,
        crop_size=None,
        resample=None,
        do_rescale=True,
        rescale_factor=1 / 255,
        do_normalize=True,
        image_mean=FLAVA_IMAGE_MEAN,
        image_std=FLAVA_IMAGE_STD,
        input_size_patches=14,
        total_mask_patches=75,
        mask_group_max_patches=None,
        mask_group_min_patches=16,
        mask_group_min_aspect_ratio=0.3,
        mask_group_max_aspect_ratio=None,
        codebook_do_resize=True,
        codebook_size=None,
        codebook_resample=None,
        codebook_do_center_crop=True,
        codebook_crop_size=None,
        codebook_do_map_pixels=True,
        codebook_do_normalize=True,
        codebook_image_mean=FLAVA_CODEBOOK_MEAN,
        codebook_image_std=FLAVA_CODEBOOK_STD,
    ):
        size = size if size is not None else {"height": 224, "width": 224}
        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
        codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112}
        codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.do_resize = do_resize
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.size = size
        self.resample = resample if resample is not None else PILImageResampling.BICUBIC
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size

        self.input_size_patches = input_size_patches
        self.total_mask_patches = total_mask_patches
        self.mask_group_max_patches = mask_group_max_patches
        self.mask_group_min_patches = mask_group_min_patches
        self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio
        self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio

        self.codebook_do_resize = codebook_do_resize
        self.codebook_size = codebook_size
        self.codebook_resample = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
        self.codebook_do_center_crop = codebook_do_center_crop
        self.codebook_crop_size = codebook_crop_size
        self.codebook_do_map_pixels = codebook_do_map_pixels
        self.codebook_do_normalize = codebook_do_normalize
        self.codebook_image_mean = codebook_image_mean
        self.codebook_image_std = codebook_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,
            "resample": self.resample,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "input_size_patches": self.input_size_patches,
            "total_mask_patches": self.total_mask_patches,
            "mask_group_max_patches": self.mask_group_max_patches,
            "mask_group_min_patches": self.mask_group_min_patches,
            "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
            "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
            "codebook_do_resize": self.codebook_do_resize,
            "codebook_size": self.codebook_size,
            "codebook_resample": self.codebook_resample,
            "codebook_do_center_crop": self.codebook_do_center_crop,
            "codebook_crop_size": self.codebook_crop_size,
            "codebook_do_map_pixels": self.codebook_do_map_pixels,
            "codebook_do_normalize": self.codebook_do_normalize,
            "codebook_image_mean": self.codebook_image_mean,
            "codebook_image_std": self.codebook_image_std,
        }

    def get_expected_image_size(self):
        return (self.size["height"], self.size["width"])

    def get_expected_mask_size(self):
        return (
            (self.input_size_patches, self.input_size_patches)
            if not isinstance(self.input_size_patches, tuple)
            else self.input_size_patches
        )

    def get_expected_codebook_image_size(self):
        return (self.codebook_size["height"], self.codebook_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 FlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = FlavaImageProcessor if is_vision_available() else None
    maxDiff = None

    def setUp(self):
        self.image_processor_tester = FlavaImageProcessingTester(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, "do_normalize"))
        self.assertTrue(hasattr(image_processing, "do_resize"))
        self.assertTrue(hasattr(image_processing, "resample"))
        self.assertTrue(hasattr(image_processing, "crop_size"))
        self.assertTrue(hasattr(image_processing, "do_center_crop"))
        self.assertTrue(hasattr(image_processing, "do_rescale"))
        self.assertTrue(hasattr(image_processing, "rescale_factor"))
        self.assertTrue(hasattr(image_processing, "masking_generator"))
        self.assertTrue(hasattr(image_processing, "codebook_do_resize"))
        self.assertTrue(hasattr(image_processing, "codebook_size"))
        self.assertTrue(hasattr(image_processing, "codebook_resample"))
        self.assertTrue(hasattr(image_processing, "codebook_do_center_crop"))
        self.assertTrue(hasattr(image_processing, "codebook_crop_size"))
        self.assertTrue(hasattr(image_processing, "codebook_do_map_pixels"))
        self.assertTrue(hasattr(image_processing, "codebook_do_normalize"))
        self.assertTrue(hasattr(image_processing, "codebook_image_mean"))
        self.assertTrue(hasattr(image_processing, "codebook_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": 224, "width": 224})
        self.assertEqual(image_processor.crop_size, {"height": 224, "width": 224})
        self.assertEqual(image_processor.codebook_size, {"height": 112, "width": 112})
        self.assertEqual(image_processor.codebook_crop_size, {"height": 112, "width": 112})

        image_processor = self.image_processing_class.from_dict(
            self.image_processor_dict, size=42, crop_size=84, codebook_size=33, codebook_crop_size=66
        )
        self.assertEqual(image_processor.size, {"height": 42, "width": 42})
        self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
        self.assertEqual(image_processor.codebook_size, {"height": 33, "width": 33})
        self.assertEqual(image_processor.codebook_crop_size, {"height": 66, "width": 66})

    def test_call_pil(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
        for image in image_inputs:
            self.assertIsInstance(image, PIL.Image.Image)

        
        encoded_images = image_processing(image_inputs[0], return_tensors="pt")

        
        self.assertFalse("bool_masked_pos" in encoded_images)

        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()

        self.assertEqual(
            encoded_images.pixel_values.shape,
            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
        )

        
        encoded_images = image_processing(image_inputs, return_tensors="pt")
        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()

        
        self.assertFalse("bool_masked_pos" in encoded_images)

        self.assertEqual(
            encoded_images.pixel_values.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

    def _test_call_framework(self, instance_class, prepare_kwargs):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, **prepare_kwargs)
        for image in image_inputs:
            self.assertIsInstance(image, instance_class)

        
        encoded_images = image_processing(image_inputs[0], return_tensors="pt")

        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape,
            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
        )

        encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")

        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape,
            (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            ),
        )

        
        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values

        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        
        encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")

        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
        self.assertEqual(
            encoded_images.pixel_values.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
        self.assertEqual(
            encoded_images.bool_masked_pos.shape,
            (
                self.image_processor_tester.batch_size,
                expected_height,
                expected_width,
            ),
        )

    def test_call_numpy(self):
        self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})

    def test_call_numpy_4_channels(self):
        self.image_processing_class.num_channels = 4
        self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
        self.image_processing_class.num_channels = 3

    def test_call_pytorch(self):
        self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True})

    def test_masking(self):
        
        random.seed(1234)
        image_processing = self.image_processing_class(**self.image_processor_dict)
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)

        
        encoded_images = image_processing(image_inputs[0], return_image_mask=True, return_tensors="pt")
        self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)

    def test_codebook_pixels(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
        for image in image_inputs:
            self.assertIsInstance(image, PIL.Image.Image)

        
        encoded_images = image_processing(image_inputs[0], return_codebook_pixels=True, return_tensors="pt")
        expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape,
            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
        )

        
        encoded_images = image_processing(image_inputs, return_codebook_pixels=True, return_tensors="pt")
        expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
        self.assertEqual(
            encoded_images.codebook_pixel_values.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )