# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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 hashlib
import unittest

import datasets
from datasets import load_dataset

from transformers import (
    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForImageSegmentation,
    AutoModelForInstanceSegmentation,
    DetrForSegmentation,
    ImageSegmentationPipeline,
    MaskFormerForInstanceSegmentation,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY, PipelineTestCaseMeta


if is_vision_available():
    from PIL import Image
else:

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


def hashimage(image: Image) -> str:
    m = hashlib.md5(image.tobytes())
    return m.hexdigest()


@require_vision
@require_timm
@require_torch
@is_pipeline_test
class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
    model_mapping = {
        k: v
        for k, v in (
            list(MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()) if MODEL_FOR_IMAGE_SEGMENTATION_MAPPING else []
        )
        + (MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() if MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING else [])
        + (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else [])
    }

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

    def run_pipeline_test(self, image_segmenter, examples):
        outputs = image_segmenter("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
        self.assertIsInstance(outputs, list)
        n = len(outputs)
        if isinstance(image_segmenter.model, (MaskFormerForInstanceSegmentation)):
            # Instance segmentation (maskformer) have a slot for null class
            # and can output nothing even with a low threshold
            self.assertGreaterEqual(n, 0)
        else:
            self.assertGreaterEqual(n, 1)
        # XXX: PIL.Image implements __eq__ which bypasses ANY, so we inverse the comparison
        # to make it work
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n, outputs)

        dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")

        # RGBA
        outputs = image_segmenter(dataset[0]["file"])
        m = len(outputs)
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
        # LA
        outputs = image_segmenter(dataset[1]["file"])
        m = len(outputs)
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
        # L
        outputs = image_segmenter(dataset[2]["file"])
        m = len(outputs)
        self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)

        if isinstance(image_segmenter.model, DetrForSegmentation):
            # We need to test batch_size with images with the same size.
            # Detr doesn't normalize the size of the images, meaning we can have
            # 800x800 or 800x1200, meaning we cannot batch simply.
            # We simply bail on this
            batch_size = 1
        else:
            batch_size = 2

        # 5 times the same image so the output shape is predictable
        batch = [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]
        outputs = image_segmenter(batch, threshold=0.0, batch_size=batch_size)
        self.assertEqual(len(batch), len(outputs))
        self.assertEqual(len(outputs[0]), n)
        self.assertEqual(
            [
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
                [{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
            ],
            outputs,
            f"Expected [{n}, {n}, {n}, {n}, {n}], got {[len(item) for item in outputs]}",
        )

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

    @require_torch
    def test_small_model_pt(self):
        model_id = "mishig/tiny-detr-mobilenetsv3-panoptic"

        model = AutoModelForImageSegmentation.from_pretrained(model_id)
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
        image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)

        outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)
        for o in outputs:
            # shortening by hashing
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {
                    "score": 0.004,
                    "label": "LABEL_0",
                    "mask": "34eecd16bbfb0f476083ef947d81bf66",
                },
                {
                    "score": 0.004,
                    "label": "LABEL_0",
                    "mask": "34eecd16bbfb0f476083ef947d81bf66",
                },
            ],
        )

        outputs = image_segmenter(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            threshold=0.0,
        )
        for output in outputs:
            for o in output:
                o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {
                        "score": 0.004,
                        "label": "LABEL_0",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                    {
                        "score": 0.004,
                        "label": "LABEL_0",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                ],
                [
                    {
                        "score": 0.004,
                        "label": "LABEL_0",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                    {
                        "score": 0.004,
                        "label": "LABEL_0",
                        "mask": "34eecd16bbfb0f476083ef947d81bf66",
                    },
                ],
            ],
        )

    @require_torch
    def test_small_model_pt_semantic(self):
        model_id = "hf-internal-testing/tiny-random-beit-pipeline"
        image_segmenter = pipeline(model=model_id)
        outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg")
        for o in outputs:
            # shortening by hashing
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {
                    "score": None,
                    "label": "LABEL_0",
                    "mask": "6225140faf502d272af076222776d7e4",
                },
                {
                    "score": None,
                    "label": "LABEL_1",
                    "mask": "8297c9f8eb43ddd3f32a6dae21e015a1",
                },
            ],
        )

    @require_torch
    @slow
    def test_integration_torch_image_segmentation(self):
        model_id = "facebook/detr-resnet-50-panoptic"

        image_segmenter = pipeline("image-segmentation", model=model_id)

        outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg")
        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9094, "label": "blanket", "mask": "85144e4bf8d624c2c6175f7faf57eb30"},
                {"score": 0.9941, "label": "cat", "mask": "f3a7f80220788acc0245ebc084df6afc"},
                {"score": 0.9987, "label": "remote", "mask": "7703408f54da1d0ebda47841da875e48"},
                {"score": 0.9995, "label": "remote", "mask": "bd726918f10fed3efaef0091e11f923b"},
                {"score": 0.9722, "label": "couch", "mask": "226d6dcb98bebc3fbc208abdc0c83196"},
                {"score": 0.9994, "label": "cat", "mask": "fa5d8d5c329546ba5339f3095641ef56"},
            ],
        )

        outputs = image_segmenter(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            threshold=0.0,
        )
        for output in outputs:
            for o in output:
                o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.9094, "label": "blanket", "mask": "85144e4bf8d624c2c6175f7faf57eb30"},
                    {"score": 0.9941, "label": "cat", "mask": "f3a7f80220788acc0245ebc084df6afc"},
                    {"score": 0.9987, "label": "remote", "mask": "7703408f54da1d0ebda47841da875e48"},
                    {"score": 0.9995, "label": "remote", "mask": "bd726918f10fed3efaef0091e11f923b"},
                    {"score": 0.9722, "label": "couch", "mask": "226d6dcb98bebc3fbc208abdc0c83196"},
                    {"score": 0.9994, "label": "cat", "mask": "fa5d8d5c329546ba5339f3095641ef56"},
                ],
                [
                    {"score": 0.9094, "label": "blanket", "mask": "85144e4bf8d624c2c6175f7faf57eb30"},
                    {"score": 0.9941, "label": "cat", "mask": "f3a7f80220788acc0245ebc084df6afc"},
                    {"score": 0.9987, "label": "remote", "mask": "7703408f54da1d0ebda47841da875e48"},
                    {"score": 0.9995, "label": "remote", "mask": "bd726918f10fed3efaef0091e11f923b"},
                    {"score": 0.9722, "label": "couch", "mask": "226d6dcb98bebc3fbc208abdc0c83196"},
                    {"score": 0.9994, "label": "cat", "mask": "fa5d8d5c329546ba5339f3095641ef56"},
                ],
            ],
        )

    @require_torch
    @slow
    def test_threshold(self):
        threshold = 0.999
        model_id = "facebook/detr-resnet-50-panoptic"

        image_segmenter = pipeline("image-segmentation", model=model_id)

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

        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9995, "label": "remote", "mask": "bd726918f10fed3efaef0091e11f923b"},
                {"score": 0.9994, "label": "cat", "mask": "fa5d8d5c329546ba5339f3095641ef56"},
            ],
        )

    @require_torch
    @slow
    def test_maskformer(self):
        threshold = 0.8
        model_id = "facebook/maskformer-swin-base-ade"

        model = AutoModelForInstanceSegmentation.from_pretrained(model_id)
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)

        image_segmenter = pipeline("image-segmentation", model=model, feature_extractor=feature_extractor)

        image = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
        file = image[0]["file"]
        outputs = image_segmenter(file, threshold=threshold)

        for o in outputs:
            o["mask"] = hashimage(o["mask"])

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"mask": "20d1b9480d1dc1501dbdcfdff483e370", "label": "wall", "score": None},
                {"mask": "0f902fbc66a0ff711ea455b0e4943adf", "label": "house", "score": None},
                {"mask": "4537bdc07d47d84b3f8634b7ada37bd4", "label": "grass", "score": None},
                {"mask": "b7ac77dfae44a904b479a0926a2acaf7", "label": "tree", "score": None},
                {"mask": "e9bedd56bd40650fb263ce03eb621079", "label": "plant", "score": None},
                {"mask": "37a609f8c9c1b8db91fbff269f428b20", "label": "road, route", "score": None},
                {"mask": "0d8cdfd63bae8bf6e4344d460a2fa711", "label": "sky", "score": None},
            ],
        )
