# 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 nested_simplify, require_tf, 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


@require_vision
@require_torch
class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):

    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}},
            ],
        )
