codingutf8 2022 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 for the postprocessfunctions this function computes the expected height and width when providing images to oneformerprocessor assuming doresize is set to true with a scalar size it also provides the expected sequence length for the taskinputs and textlistinput 1 for null class only for testfeatextracttioncommon testfeatextracttojsonstring initialize processor create random pil images test not batched input test batched initialize processor create random numpy tensors test not batched input test batched initialize processor create random pytorch tensors test not batched input test batched prepare image and target check the batchsize this ensure padding has happened load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes prepare the images and annotations verify the pixel values task inputs text inputs and pixel mask verify the class labels verify the task inputs verify the text inputs verify the mask labels load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes prepare the images and annotations verify the pixel values task inputs text inputs and pixel mask verify the class labels verify the task inputs verify the text inputs verify the mask labels load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes prepare the images and annotations verify the pixel values task inputs text inputs and pixel mask verify the class labels verify the task inputs verify the text inputs verify the mask labels coding utf 8 2022 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 for the post_process_functions this function computes the expected height and width when providing images to oneformerprocessor assuming do_resize is set to true with a scalar size it also provides the expected sequence length for the task_inputs and text_list_input 1 for null class only for test_feat_extracttion_common test_feat_extract_to_json_string initialize processor create random pil images test not batched input test batched initialize processor create random numpy tensors test not batched input test batched initialize processor create random pytorch tensors test not batched input test batched prepare image and target check the batch_size this ensure padding has happened load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes prepare the images and annotations verify the pixel values task inputs text inputs and pixel mask verify the class labels noqa e231 fmt skip noqa e231 fmt skip verify the task inputs verify the text inputs verify the mask labels load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes prepare the images and annotations verify the pixel values task inputs text inputs and pixel mask verify the class labels fmt skip fmt skip verify the task inputs verify the text inputs verify the mask labels load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes prepare the images and annotations verify the pixel values task inputs text inputs and pixel mask verify the class labels fmt skip fmt skip verify the task inputs verify the text inputs verify the mask labels
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset
from huggingface_hub import hf_hub_download

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

from ...test_image_processing_common import prepare_image_inputs


if is_torch_available():
    import torch

    if is_vision_available():
        from transformers import CLIPTokenizer, OneFormerImageProcessor, OneFormerProcessor
        from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
        from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
    from PIL import Image


def prepare_metadata(class_info_file, repo_path="shi-labs/oneformer_demo"):
    with open(hf_hub_download(repo_path, class_info_file, repo_type="dataset"), "r") as f:
        class_info = json.load(f)
    metadata = {}
    class_names = []
    thing_ids = []

    for key, info in class_info.items():
        metadata[key] = info["name"]
        class_names.append(info["name"])
        if info["isthing"]:
            thing_ids.append(int(key))

    metadata["thing_ids"] = thing_ids
    metadata["class_names"] = class_names
    return metadata


class OneFormerProcessorTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=30,
        max_resolution=400,
        size=None,
        do_resize=True,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        num_labels=10,
        reduce_labels=False,
        ignore_index=255,
        max_seq_length=77,
        task_seq_length=77,
        model_repo="shi-labs/oneformer_ade20k_swin_tiny",
        class_info_file="ade20k_panoptic.json",
        num_text=10,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.max_seq_length = max_seq_length
        self.task_seq_length = task_seq_length
        self.class_info_file = class_info_file
        self.metadata = prepare_metadata(class_info_file)
        self.num_text = num_text
        self.model_repo = model_repo

        
        self.batch_size = 2
        self.num_queries = 10
        self.num_classes = 10
        self.height = 3
        self.width = 4
        self.num_labels = num_labels
        self.reduce_labels = reduce_labels
        self.ignore_index = ignore_index

    def prepare_processor_dict(self):
        image_processor_dict = {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "num_labels": self.num_labels,
            "reduce_labels": self.reduce_labels,
            "ignore_index": self.ignore_index,
            "class_info_file": self.class_info_file,
            "metadata": self.metadata,
            "num_text": self.num_text,
        }

        image_processor = OneFormerImageProcessor(**image_processor_dict)
        tokenizer = CLIPTokenizer.from_pretrained(self.model_repo)

        return {
            "image_processor": image_processor,
            "tokenizer": tokenizer,
            "max_seq_length": self.max_seq_length,
            "task_seq_length": self.task_seq_length,
        }

    def get_expected_values(self, image_inputs, batched=False):
        
        if not batched:
            image = image_inputs[0]
            if isinstance(image, Image.Image):
                w, h = image.size
            else:
                h, w = image.shape[1], image.shape[2]
            if w < h:
                expected_height = int(self.size["shortest_edge"] * h / w)
                expected_width = self.size["shortest_edge"]
            elif w > h:
                expected_height = self.size["shortest_edge"]
                expected_width = int(self.size["shortest_edge"] * w / h)
            else:
                expected_height = self.size["shortest_edge"]
                expected_width = self.size["shortest_edge"]

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

        expected_sequence_length = self.max_seq_length

        return expected_height, expected_width, expected_sequence_length

    def get_fake_oneformer_outputs(self):
        return OneFormerForUniversalSegmentationOutput(
            
            class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)),
            masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.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 OneFormerProcessingTest(unittest.TestCase):
    processing_class = OneFormerProcessor if (is_vision_available() and is_torch_available()) else None
    
    feature_extraction_class = processing_class

    def setUp(self):
        self.processing_tester = OneFormerProcessorTester(self)

    @property
    def processor_dict(self):
        return self.processing_tester.prepare_processor_dict()

    def test_feat_extract_properties(self):
        processor = self.processing_class(**self.processor_dict)
        self.assertTrue(hasattr(processor, "image_processor"))
        self.assertTrue(hasattr(processor, "tokenizer"))
        self.assertTrue(hasattr(processor, "max_seq_length"))
        self.assertTrue(hasattr(processor, "task_seq_length"))

    def test_batch_feature(self):
        pass

    def test_call_pil(self):
        
        processor = self.processing_class(**self.processor_dict)
        
        image_inputs = self.processing_tester.prepare_image_inputs(equal_resolution=False)
        for image in image_inputs:
            self.assertIsInstance(image, Image.Image)

        
        encoded_images = processor(image_inputs[0], ["semantic"], return_tensors="pt").pixel_values

        expected_height, expected_width, expected_sequence_length = self.processing_tester.get_expected_values(
            image_inputs
        )

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

        tokenized_task_inputs = processor(image_inputs[0], ["semantic"], return_tensors="pt").task_inputs

        self.assertEqual(
            tokenized_task_inputs.shape,
            (1, expected_sequence_length),
        )

        
        expected_height, expected_width, expected_sequence_length = self.processing_tester.get_expected_values(
            image_inputs, batched=True
        )

        encoded_images = processor(image_inputs, ["semantic"] * len(image_inputs), return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.processing_tester.batch_size,
                self.processing_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        tokenized_task_inputs = processor(
            image_inputs, ["semantic"] * len(image_inputs), return_tensors="pt"
        ).task_inputs

        self.assertEqual(
            tokenized_task_inputs.shape,
            (self.processing_tester.batch_size, expected_sequence_length),
        )

    def test_call_numpy(self):
        
        processor = self.processing_class(**self.processor_dict)
        
        image_inputs = self.processing_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)

        
        encoded_images = processor(image_inputs[0], ["semantic"], return_tensors="pt").pixel_values

        expected_height, expected_width, expected_sequence_length = self.processing_tester.get_expected_values(
            image_inputs
        )

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

        tokenized_task_inputs = processor(image_inputs[0], ["semantic"], return_tensors="pt").task_inputs

        self.assertEqual(
            tokenized_task_inputs.shape,
            (1, expected_sequence_length),
        )

        
        expected_height, expected_width, expected_sequence_length = self.processing_tester.get_expected_values(
            image_inputs, batched=True
        )

        encoded_images = processor(image_inputs, ["semantic"] * len(image_inputs), return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.processing_tester.batch_size,
                self.processing_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        tokenized_task_inputs = processor(
            image_inputs, ["semantic"] * len(image_inputs), return_tensors="pt"
        ).task_inputs

        self.assertEqual(
            tokenized_task_inputs.shape,
            (self.processing_tester.batch_size, expected_sequence_length),
        )

    def test_call_pytorch(self):
        
        processor = self.processing_class(**self.processor_dict)
        
        image_inputs = self.processing_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
        for image in image_inputs:
            self.assertIsInstance(image, torch.Tensor)

        
        encoded_images = processor(image_inputs[0], ["semantic"], return_tensors="pt").pixel_values

        expected_height, expected_width, expected_sequence_length = self.processing_tester.get_expected_values(
            image_inputs
        )

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

        tokenized_task_inputs = processor(image_inputs[0], ["semantic"], return_tensors="pt").task_inputs

        self.assertEqual(
            tokenized_task_inputs.shape,
            (1, expected_sequence_length),
        )

        
        expected_height, expected_width, expected_sequence_length = self.processing_tester.get_expected_values(
            image_inputs, batched=True
        )

        encoded_images = processor(image_inputs, ["semantic"] * len(image_inputs), return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.processing_tester.batch_size,
                self.processing_tester.num_channels,
                expected_height,
                expected_width,
            ),
        )

        tokenized_task_inputs = processor(
            image_inputs, ["semantic"] * len(image_inputs), return_tensors="pt"
        ).task_inputs

        self.assertEqual(
            tokenized_task_inputs.shape,
            (self.processing_tester.batch_size, expected_sequence_length),
        )

    def comm_get_processor_inputs(self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"):
        processor = self.processing_class(**self.processor_dict)
        
        num_labels = self.processing_tester.num_labels
        annotations = None
        instance_id_to_semantic_id = None
        image_inputs = self.processing_tester.prepare_image_inputs(equal_resolution=False)
        if with_segmentation_maps:
            high = num_labels
            if is_instance_map:
                labels_expanded = list(range(num_labels)) * 2
                instance_id_to_semantic_id = dict(enumerate(labels_expanded))
            annotations = [
                np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
            ]
            if segmentation_type == "pil":
                annotations = [Image.fromarray(annotation) for annotation in annotations]

        inputs = processor(
            image_inputs,
            ["semantic"] * len(image_inputs),
            annotations,
            return_tensors="pt",
            instance_id_to_semantic_id=instance_id_to_semantic_id,
            pad_and_return_pixel_mask=True,
        )

        return inputs

    def test_init_without_params(self):
        pass

    def test_feat_extract_from_and_save_pretrained(self):
        feat_extract_first = self.feature_extraction_class(**self.processor_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            feat_extract_first.save_pretrained(tmpdirname)
            check_json_file_has_correct_format(os.path.join(tmpdirname, "preprocessor_config.json"))
            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)

        self.assertEqual(feat_extract_second.image_processor.to_dict(), feat_extract_first.image_processor.to_dict())
        self.assertIsInstance(feat_extract_first.image_processor, OneFormerImageProcessor)
        self.assertIsInstance(feat_extract_first.tokenizer, CLIPTokenizer)

    def test_call_with_segmentation_maps(self):
        def common(is_instance_map=False, segmentation_type=None):
            inputs = self.comm_get_processor_inputs(
                with_segmentation_maps=True, is_instance_map=is_instance_map, segmentation_type=segmentation_type
            )

            mask_labels = inputs["mask_labels"]
            class_labels = inputs["class_labels"]
            pixel_values = inputs["pixel_values"]
            text_inputs = inputs["text_inputs"]

            
            for mask_label, class_label, text_input in zip(mask_labels, class_labels, text_inputs):
                self.assertEqual(mask_label.shape[0], class_label.shape[0])
                
                self.assertEqual(mask_label.shape[1:], pixel_values.shape[2:])
                self.assertEqual(text_input.shape[0], self.processing_tester.num_text)

        common()
        common(is_instance_map=True)
        common(is_instance_map=False, segmentation_type="pil")
        common(is_instance_map=True, segmentation_type="pil")

    def test_integration_semantic_segmentation(self):
        
        dataset = load_dataset("nielsr/ade20k-panoptic-demo")
        image1 = dataset["train"][0]["image"]
        image2 = dataset["train"][1]["image"]
        segments_info1 = dataset["train"][0]["segments_info"]
        segments_info2 = dataset["train"][1]["segments_info"]
        annotation1 = dataset["train"][0]["label"]
        annotation2 = dataset["train"][1]["label"]

        def rgb_to_id(color):
            if isinstance(color, np.ndarray) and len(color.shape) == 3:
                if color.dtype == np.uint8:
                    color = color.astype(np.int32)
                return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
            return int(color[0] + 256 * color[1] + 256 * 256 * color[2])

        def create_panoptic_map(annotation, segments_info):
            annotation = np.array(annotation)
            
            
            panoptic_map = rgb_to_id(annotation)

            
            inst2class = {segment["id"]: segment["category_id"] for segment in segments_info}

            return panoptic_map, inst2class

        panoptic_map1, inst2class1 = create_panoptic_map(annotation1, segments_info1)
        panoptic_map2, inst2class2 = create_panoptic_map(annotation2, segments_info2)

        image_processor = OneFormerImageProcessor(
            reduce_labels=True,
            ignore_index=0,
            size=(512, 512),
            class_info_file="ade20k_panoptic.json",
            num_text=self.processing_tester.num_text,
        )

        tokenizer = CLIPTokenizer.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")

        processor = OneFormerProcessor(
            image_processor=image_processor,
            tokenizer=tokenizer,
            max_seq_length=77,
            task_seq_length=77,
        )

        
        pixel_values_list = [np.moveaxis(np.array(image1), -1, 0), np.moveaxis(np.array(image2), -1, 0)]
        inputs = processor.encode_inputs(
            pixel_values_list,
            ["semantic", "semantic"],
            [panoptic_map1, panoptic_map2],
            instance_id_to_semantic_id=[inst2class1, inst2class2],
            return_tensors="pt",
        )

        
        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 711))
        self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 711))
        self.assertEqual(inputs["task_inputs"].shape, (2, 77))
        self.assertEqual(inputs["text_inputs"].shape, (2, self.processing_tester.num_text, 77))

        
        self.assertEqual(len(inputs["class_labels"]), 2)
        expected_class_labels = torch.tensor([4, 17, 32, 42, 12, 3, 5, 0, 43, 96, 104, 31, 125, 138, 87, 149])  
        self.assertTrue(torch.allclose(inputs["class_labels"][0], expected_class_labels))
        expected_class_labels = torch.tensor([19, 67, 82, 17, 12, 42, 3, 14, 5, 0, 115, 43, 8, 138, 125, 143])  
        self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels))

        
        self.assertEqual(len(inputs["task_inputs"]), 2)
        self.assertEqual(inputs["task_inputs"][0].sum().item(), 141082)
        self.assertEqual(inputs["task_inputs"][0].sum().item(), inputs["task_inputs"][1].sum().item())

        
        self.assertEqual(len(inputs["text_inputs"]), 2)
        self.assertEqual(inputs["text_inputs"][0].sum().item(), 1095752)
        self.assertEqual(inputs["text_inputs"][1].sum().item(), 1062468)

        
        self.assertEqual(len(inputs["mask_labels"]), 2)
        self.assertEqual(inputs["mask_labels"][0].shape, (16, 512, 711))
        self.assertEqual(inputs["mask_labels"][1].shape, (16, 512, 711))
        self.assertEqual(inputs["mask_labels"][0].sum().item(), 315193.0)
        self.assertEqual(inputs["mask_labels"][1].sum().item(), 350747.0)

    def test_integration_instance_segmentation(self):
        
        dataset = load_dataset("nielsr/ade20k-panoptic-demo")
        image1 = dataset["train"][0]["image"]
        image2 = dataset["train"][1]["image"]
        segments_info1 = dataset["train"][0]["segments_info"]
        segments_info2 = dataset["train"][1]["segments_info"]
        annotation1 = dataset["train"][0]["label"]
        annotation2 = dataset["train"][1]["label"]

        def rgb_to_id(color):
            if isinstance(color, np.ndarray) and len(color.shape) == 3:
                if color.dtype == np.uint8:
                    color = color.astype(np.int32)
                return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
            return int(color[0] + 256 * color[1] + 256 * 256 * color[2])

        def create_panoptic_map(annotation, segments_info):
            annotation = np.array(annotation)
            
            
            panoptic_map = rgb_to_id(annotation)

            
            inst2class = {segment["id"]: segment["category_id"] for segment in segments_info}

            return panoptic_map, inst2class

        panoptic_map1, inst2class1 = create_panoptic_map(annotation1, segments_info1)
        panoptic_map2, inst2class2 = create_panoptic_map(annotation2, segments_info2)

        image_processor = OneFormerImageProcessor(
            reduce_labels=True,
            ignore_index=0,
            size=(512, 512),
            class_info_file="ade20k_panoptic.json",
            num_text=self.processing_tester.num_text,
        )

        tokenizer = CLIPTokenizer.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")

        processor = OneFormerProcessor(
            image_processor=image_processor,
            tokenizer=tokenizer,
            max_seq_length=77,
            task_seq_length=77,
        )

        
        pixel_values_list = [np.moveaxis(np.array(image1), -1, 0), np.moveaxis(np.array(image2), -1, 0)]
        inputs = processor.encode_inputs(
            pixel_values_list,
            ["instance", "instance"],
            [panoptic_map1, panoptic_map2],
            instance_id_to_semantic_id=[inst2class1, inst2class2],
            return_tensors="pt",
        )

        
        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 711))
        self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 711))
        self.assertEqual(inputs["task_inputs"].shape, (2, 77))
        self.assertEqual(inputs["text_inputs"].shape, (2, self.processing_tester.num_text, 77))

        
        self.assertEqual(len(inputs["class_labels"]), 2)
        expected_class_labels = torch.tensor([32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 43, 43, 43, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87])  
        self.assertTrue(torch.allclose(inputs["class_labels"][0], expected_class_labels))
        expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 12, 12, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143])  
        self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels))

        
        self.assertEqual(len(inputs["task_inputs"]), 2)
        self.assertEqual(inputs["task_inputs"][0].sum().item(), 144985)
        self.assertEqual(inputs["task_inputs"][0].sum().item(), inputs["task_inputs"][1].sum().item())

        
        self.assertEqual(len(inputs["text_inputs"]), 2)
        self.assertEqual(inputs["text_inputs"][0].sum().item(), 1037040)
        self.assertEqual(inputs["text_inputs"][1].sum().item(), 1044078)

        
        self.assertEqual(len(inputs["mask_labels"]), 2)
        self.assertEqual(inputs["mask_labels"][0].shape, (73, 512, 711))
        self.assertEqual(inputs["mask_labels"][1].shape, (57, 512, 711))
        self.assertEqual(inputs["mask_labels"][0].sum().item(), 35040.0)
        self.assertEqual(inputs["mask_labels"][1].sum().item(), 98228.0)

    def test_integration_panoptic_segmentation(self):
        
        dataset = load_dataset("nielsr/ade20k-panoptic-demo")
        image1 = dataset["train"][0]["image"]
        image2 = dataset["train"][1]["image"]
        segments_info1 = dataset["train"][0]["segments_info"]
        segments_info2 = dataset["train"][1]["segments_info"]
        annotation1 = dataset["train"][0]["label"]
        annotation2 = dataset["train"][1]["label"]

        def rgb_to_id(color):
            if isinstance(color, np.ndarray) and len(color.shape) == 3:
                if color.dtype == np.uint8:
                    color = color.astype(np.int32)
                return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
            return int(color[0] + 256 * color[1] + 256 * 256 * color[2])

        def create_panoptic_map(annotation, segments_info):
            annotation = np.array(annotation)
            
            
            panoptic_map = rgb_to_id(annotation)

            
            inst2class = {segment["id"]: segment["category_id"] for segment in segments_info}

            return panoptic_map, inst2class

        panoptic_map1, inst2class1 = create_panoptic_map(annotation1, segments_info1)
        panoptic_map2, inst2class2 = create_panoptic_map(annotation2, segments_info2)

        image_processor = OneFormerImageProcessor(
            reduce_labels=True,
            ignore_index=0,
            size=(512, 512),
            class_info_file="ade20k_panoptic.json",
            num_text=self.processing_tester.num_text,
        )

        tokenizer = CLIPTokenizer.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")

        processor = OneFormerProcessor(
            image_processor=image_processor,
            tokenizer=tokenizer,
            max_seq_length=77,
            task_seq_length=77,
        )

        
        pixel_values_list = [np.moveaxis(np.array(image1), -1, 0), np.moveaxis(np.array(image2), -1, 0)]
        inputs = processor.encode_inputs(
            pixel_values_list,
            ["panoptic", "panoptic"],
            [panoptic_map1, panoptic_map2],
            instance_id_to_semantic_id=[inst2class1, inst2class2],
            return_tensors="pt",
        )

        
        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 711))
        self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 711))
        self.assertEqual(inputs["task_inputs"].shape, (2, 77))
        self.assertEqual(inputs["text_inputs"].shape, (2, self.processing_tester.num_text, 77))

        
        self.assertEqual(len(inputs["class_labels"]), 2)
        expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87])  
        self.assertTrue(torch.allclose(inputs["class_labels"][0], expected_class_labels))
        expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143])  
        self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels))

        
        self.assertEqual(len(inputs["task_inputs"]), 2)
        self.assertEqual(inputs["task_inputs"][0].sum().item(), 136240)
        self.assertEqual(inputs["task_inputs"][0].sum().item(), inputs["task_inputs"][1].sum().item())

        
        self.assertEqual(len(inputs["text_inputs"]), 2)
        self.assertEqual(inputs["text_inputs"][0].sum().item(), 1048653)
        self.assertEqual(inputs["text_inputs"][1].sum().item(), 1067160)

        
        self.assertEqual(len(inputs["mask_labels"]), 2)
        self.assertEqual(inputs["mask_labels"][0].shape, (79, 512, 711))
        self.assertEqual(inputs["mask_labels"][1].shape, (61, 512, 711))
        self.assertEqual(inputs["mask_labels"][0].sum().item(), 315193.0)
        self.assertEqual(inputs["mask_labels"][1].sum().item(), 350747.0)

    def test_binary_mask_to_rle(self):
        fake_binary_mask = np.zeros((20, 50))
        fake_binary_mask[0, 20:] = 1
        fake_binary_mask[1, :15] = 1
        fake_binary_mask[5, :10] = 1

        rle = binary_mask_to_rle(fake_binary_mask)
        self.assertEqual(len(rle), 4)
        self.assertEqual(rle[0], 21)
        self.assertEqual(rle[1], 45)

    def test_post_process_semantic_segmentation(self):
        image_processor = OneFormerImageProcessor(
            reduce_labels=True,
            ignore_index=0,
            size=(512, 512),
            class_info_file="ade20k_panoptic.json",
            num_text=self.processing_tester.num_text,
        )
        tokenizer = CLIPTokenizer.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
        processor = OneFormerProcessor(
            image_processor=image_processor,
            tokenizer=tokenizer,
            max_seq_length=77,
            task_seq_length=77,
        )

        outputs = self.processing_tester.get_fake_oneformer_outputs()

        segmentation = processor.post_process_semantic_segmentation(outputs)

        self.assertEqual(len(segmentation), self.processing_tester.batch_size)
        self.assertEqual(
            segmentation[0].shape,
            (
                self.processing_tester.height,
                self.processing_tester.width,
            ),
        )

        target_sizes = [(1, 4) for i in range(self.processing_tester.batch_size)]
        segmentation = processor.post_process_semantic_segmentation(outputs, target_sizes=target_sizes)

        self.assertEqual(segmentation[0].shape, target_sizes[0])

    def test_post_process_instance_segmentation(self):
        image_processor = OneFormerImageProcessor(
            reduce_labels=True,
            ignore_index=0,
            size=(512, 512),
            class_info_file="ade20k_panoptic.json",
            num_text=self.processing_tester.num_text,
        )
        tokenizer = CLIPTokenizer.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
        processor = OneFormerProcessor(
            image_processor=image_processor,
            tokenizer=tokenizer,
            max_seq_length=77,
            task_seq_length=77,
        )

        outputs = self.processing_tester.get_fake_oneformer_outputs()
        segmentation = processor.post_process_instance_segmentation(outputs, threshold=0)

        self.assertTrue(len(segmentation) == self.processing_tester.batch_size)
        for el in segmentation:
            self.assertTrue("segmentation" in el)
            self.assertTrue("segments_info" in el)
            self.assertEqual(type(el["segments_info"]), list)
            self.assertEqual(el["segmentation"].shape, (self.processing_tester.height, self.processing_tester.width))

    def test_post_process_panoptic_segmentation(self):
        image_processor = OneFormerImageProcessor(
            reduce_labels=True,
            ignore_index=0,
            size=(512, 512),
            class_info_file="ade20k_panoptic.json",
            num_text=self.processing_tester.num_text,
        )
        tokenizer = CLIPTokenizer.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
        processor = OneFormerProcessor(
            image_processor=image_processor,
            tokenizer=tokenizer,
            max_seq_length=77,
            task_seq_length=77,
        )

        outputs = self.processing_tester.get_fake_oneformer_outputs()
        segmentation = processor.post_process_panoptic_segmentation(outputs, threshold=0)

        self.assertTrue(len(segmentation) == self.processing_tester.batch_size)
        for el in segmentation:
            self.assertTrue("segmentation" in el)
            self.assertTrue("segments_info" in el)
            self.assertEqual(type(el["segments_info"]), list)
            self.assertEqual(el["segmentation"].shape, (self.processing_tester.height, self.processing_tester.width))