# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# 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 unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:

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


@is_pipeline_test
@require_vision
@require_torch
class ZeroShotObjectDetectionPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        object_detector = pipeline(
            "zero-shot-object-detection", model="hf-internal-testing/tiny-random-owlvit-object-detection"
        )

        examples = [
            {
                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "candidate_labels": ["cat", "remote", "couch"],
            }
        ]
        return object_detector, examples

    def run_pipeline_test(self, object_detector, examples):
        outputs = object_detector(examples[0], threshold=0.0)

        n = len(outputs)
        self.assertGreater(n, 0)
        self.assertEqual(
            outputs,
            [
                {
                    "score": ANY(float),
                    "label": ANY(str),
                    "box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
                }
                for i in range(n)
            ],
        )

    @require_tf
    @unittest.skip("Zero Shot Object Detection not implemented in TF")
    def test_small_model_tf(self):
        pass

    @require_torch
    def test_small_model_pt(self):
        object_detector = pipeline(
            "zero-shot-object-detection", model="hf-internal-testing/tiny-random-owlvit-object-detection"
        )

        outputs = object_detector(
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            candidate_labels=["cat", "remote", "couch"],
            threshold=0.64,
        )

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
            ],
        )

        outputs = object_detector(
            [
                {
                    "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                    "candidate_labels": ["cat", "remote", "couch"],
                }
            ],
            threshold=0.64,
        )

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                    {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                    {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                    {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                    {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                    {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                    {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                    {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                    {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                ]
            ],
        )

    @require_torch
    @slow
    def test_large_model_pt(self):
        object_detector = pipeline("zero-shot-object-detection")

        outputs = object_detector(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            candidate_labels=["cat", "remote", "couch"],
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
            ],
        )

        outputs = object_detector(
            [
                {
                    "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                    "candidate_labels": ["cat", "remote", "couch"],
                },
                {
                    "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                    "candidate_labels": ["cat", "remote", "couch"],
                },
            ],
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                    {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                    {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                    {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                    {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                ],
                [
                    {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                    {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                    {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                    {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                    {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                ],
            ],
        )

    @require_tf
    @unittest.skip("Zero Shot Object Detection not implemented in TF")
    def test_large_model_tf(self):
        pass

    @require_torch
    @slow
    def test_threshold(self):
        threshold = 0.2
        object_detector = pipeline("zero-shot-object-detection")

        outputs = object_detector(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            candidate_labels=["cat", "remote", "couch"],
            threshold=threshold,
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
            ],
        )

    @require_torch
    @slow
    def test_top_k(self):
        top_k = 2
        object_detector = pipeline("zero-shot-object-detection")

        outputs = object_detector(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            candidate_labels=["cat", "remote", "couch"],
            top_k=top_k,
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
            ],
        )

# 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 unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:

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


@is_pipeline_test
@require_vision
@require_torch
class ZeroShotObjectDetectionPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        object_detector = pipeline(
            "zero-shot-object-detection", model="hf-internal-testing/tiny-random-owlvit-object-detection"
        )

        examples = [
            {
                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "candidate_labels": ["cat", "remote", "couch"],
            }
        ]
        return object_detector, examples

    def run_pipeline_test(self, object_detector, examples):
        outputs = object_detector(examples[0], threshold=0.0)

        n = len(outputs)
        self.assertGreater(n, 0)
        self.assertEqual(
            outputs,
            [
                {
                    "score": ANY(float),
                    "label": ANY(str),
                    "box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
                }
                for i in range(n)
            ],
        )

    @require_tf
    @unittest.skip("Zero Shot Object Detection not implemented in TF")
    def test_small_model_tf(self):
        pass

    @require_torch
    def test_small_model_pt(self):
        object_detector = pipeline(
            "zero-shot-object-detection", model="hf-internal-testing/tiny-random-owlvit-object-detection"
        )

        outputs = object_detector(
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            candidate_labels=["cat", "remote", "couch"],
            threshold=0.64,
        )

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
            ],
        )

        outputs = object_detector(
            [
                {
                    "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                    "candidate_labels": ["cat", "remote", "couch"],
                }
            ],
            threshold=0.64,
        )

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                    {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                    {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                    {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                    {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                    {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                    {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                    {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                    {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                ]
            ],
        )

    @require_torch
    @slow
    def test_large_model_pt(self):
        object_detector = pipeline("zero-shot-object-detection")

        outputs = object_detector(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            candidate_labels=["cat", "remote", "couch"],
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
            ],
        )

        outputs = object_detector(
            [
                {
                    "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                    "candidate_labels": ["cat", "remote", "couch"],
                },
                {
                    "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                    "candidate_labels": ["cat", "remote", "couch"],
                },
            ],
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                    {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                    {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                    {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                    {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                ],
                [
                    {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                    {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                    {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                    {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                    {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                ],
            ],
        )

    @require_tf
    @unittest.skip("Zero Shot Object Detection not implemented in TF")
    def test_large_model_tf(self):
        pass

    @require_torch
    @slow
    def test_threshold(self):
        threshold = 0.2
        object_detector = pipeline("zero-shot-object-detection")

        outputs = object_detector(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            candidate_labels=["cat", "remote", "couch"],
            threshold=threshold,
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
            ],
        )

    @require_torch
    @slow
    def test_top_k(self):
        top_k = 2
        object_detector = pipeline("zero-shot-object-detection")

        outputs = object_detector(
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            candidate_labels=["cat", "remote", "couch"],
            top_k=top_k,
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
            ],
        )
