2023 the huggingface team 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 todo implement me arthur shortening by hashing fmt off fmt on shortening by hashing 2023 the huggingface team 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 todo implement me arthur shortening by hashing fmt off fmt on shortening by hashing
import unittest
from typing import Dict

import numpy as np
from huggingface_hub.utils import insecure_hashlib

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image
else:

    class Image:
        @staticmethod
        def open(*args, **kwargs):
            pass


def hashimage(image: Image) -> str:
    m = insecure_hashlib.md5(image.tobytes())
    return m.hexdigest()[:10]


def mask_to_test_readable(mask: Image) -> Dict:
    npimg = np.array(mask)
    shape = npimg.shape
    return {"hash": hashimage(mask), "shape": shape}


@is_pipeline_test
@require_vision
@require_torch
class MaskGenerationPipelineTests(unittest.TestCase):
    model_mapping = dict(
        (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()) if MODEL_FOR_MASK_GENERATION_MAPPING else [])
    )
    tf_model_mapping = dict(
        (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])
    )

    def get_test_pipeline(self, model, tokenizer, processor):
        image_segmenter = MaskGenerationPipeline(model=model, image_processor=processor)
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]

    
    def run_pipeline_test(self, mask_generator, examples):
        pass

    @require_tf
    @unittest.skip("Image segmentation not implemented in TF")
    def test_small_model_tf(self):
        pass

    @slow
    @require_torch
    def test_small_model_pt(self):
        image_segmenter = pipeline("mask-generation", model="facebook/sam-vit-huge")

        outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", points_per_batch=256)

        
        new_outupt = []
        for i, o in enumerate(outputs["masks"]):
            new_outupt += [{"mask": mask_to_test_readable(o), "scores": outputs["scores"][i]}]

        
        self.assertEqual(
            nested_simplify(new_outupt, decimals=4),
            [
                {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
                {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
                {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
                {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
                {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
                {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
                {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
                {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
                {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
                {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
                {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
                {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
                {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
                {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
                {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
                {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
                {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
                {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
                {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
                {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
                {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
                {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
                {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
                {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
                {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
                {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
                {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
                {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
            ],
        )
        

    @require_torch
    @slow
    def test_threshold(self):
        model_id = "facebook/sam-vit-huge"
        image_segmenter = pipeline("mask-generation", model=model_id)

        outputs = image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg", pred_iou_thresh=1, points_per_batch=256
        )

        
        new_outupt = []
        for i, o in enumerate(outputs["masks"]):
            new_outupt += [{"mask": mask_to_test_readable(o), "scores": outputs["scores"][i]}]

        self.assertEqual(
            nested_simplify(new_outupt, decimals=4),
            [
                {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
                {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0210},
                {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
                {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
                {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
            ],
        )