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 initialize imageprocessor create random pil images test not batched input test not batched input test batched initialize imageprocessor create random pil images test not batched input test not batched input test batched initialize imageprocessor create random numpy tensors test not batched input test batched initialize imageprocessor create random numpy tensors test not batched input test batched initialize imageprocessor create random pytorch tensors test not batched input test not batched input test batched initialize imageprocessor create random pil images test not batched input test not batched input test batched 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 initialize image_processor create random pil images test not batched input test not batched input test batched initialize image_processor create random pil images test not batched input test not batched input test batched initialize image_processor create random numpy tensors test not batched input test batched initialize image_processor create random numpy tensors test not batched input test batched initialize image_processor create random pytorch tensors test not batched input test not batched input test batched initialize image_processor create random pil images test not batched input test not batched input test batched fixme amy fixme amy fixme amy
import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    is_torch_greater_or_equal_than_1_11 = False

if is_vision_available():
    from PIL import Image

    from transformers import Pix2StructImageProcessor


class Pix2StructImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        size=None,
        do_normalize=True,
        do_convert_rgb=True,
        patch_size=None,
    ):
        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.size = size
        self.do_normalize = do_normalize
        self.do_convert_rgb = do_convert_rgb
        self.max_patches = [512, 1024, 2048, 4096]
        self.patch_size = patch_size if patch_size is not None else {"height": 16, "width": 16}

    def prepare_image_processor_dict(self):
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}

    def prepare_dummy_image(self):
        img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
        return raw_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,
        )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,
    reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`.",
)
@require_torch
@require_vision
class Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = Pix2StructImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = Pix2StructImageProcessingTester(self)

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

    def test_image_processor_properties(self):
        image_processor = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processor, "do_normalize"))
        self.assertTrue(hasattr(image_processor, "do_convert_rgb"))

    def test_expected_patches(self):
        dummy_image = self.image_processor_tester.prepare_dummy_image()

        image_processor = self.image_processing_class(**self.image_processor_dict)
        max_patch = 2048

        inputs = image_processor(dummy_image, return_tensors="pt", max_patches=max_patch)
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean(), torch.tensor(0.0606), atol=1e-3, rtol=1e-3))

    def test_call_pil(self):
        
        image_processor = 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, Image.Image)

        
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    def test_call_vqa(self):
        
        image_processor = 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, Image.Image)

        
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        image_processor.is_vqa = True

        for max_patch in self.image_processor_tester.max_patches:
            
            with self.assertRaises(ValueError):
                encoded_images = image_processor(
                    image_inputs[0], return_tensors="pt", max_patches=max_patch
                ).flattened_patches

            dummy_text = "Hello"

            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch, header_text=dummy_text
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch, header_text=dummy_text
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    def test_call_numpy(self):
        
        image_processor = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)

        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    def test_call_numpy_4_channels(self):
        
        image_processor = self.image_processing_class(**self.image_processor_dict)
        
        self.image_processor_tester.num_channels = 4
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)

        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )
        self.image_processor_tester.num_channels = 3

    def test_call_pytorch(self):
        
        image_processor = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
        for image in image_inputs:
            self.assertIsInstance(image, torch.Tensor)

        
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,
    reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`.",
)
@require_torch
@require_vision
class Pix2StructImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = Pix2StructImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = Pix2StructImageProcessingTester(self, num_channels=4)
        self.expected_encoded_image_num_channels = 3

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

    def test_image_processor_properties(self):
        image_processor = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processor, "do_normalize"))
        self.assertTrue(hasattr(image_processor, "do_convert_rgb"))

    def test_call_pil(self):
        
        image_processor = 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, Image.Image)

        
        expected_hidden_dim = (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * (self.image_processor_tester.num_channels - 1)
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
            
            encoded_images = image_processor(
                image_inputs[0], return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (1, max_patch, expected_hidden_dim),
            )

            
            encoded_images = image_processor(
                image_inputs, return_tensors="pt", max_patches=max_patch
            ).flattened_patches
            self.assertEqual(
                encoded_images.shape,
                (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
            )

    @unittest.skip("Pix2StructImageProcessor does not support 4 channels yet")  
    def test_call_numpy(self):
        return super().test_call_numpy()

    @unittest.skip("Pix2StructImageProcessor does not support 4 channels yet")  
    def test_call_pytorch(self):
        return super().test_call_torch()

    @unittest.skip("Pix2StructImageProcessor does treat numpy and PIL 4 channel images consistently")  
    def test_call_numpy_4_channels(self):
        return super().test_call_torch()