# Copyright 2022 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_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import 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_torch
@require_vision
class VisualQuestionAnsweringPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
    model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

    def get_test_pipeline(self, model, tokenizer, feature_extractor):
        vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
        examples = [
            {
                "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
                "question": "How many cats are there?",
            },
            {
                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "question": "How many cats are there?",
            },
        ]
        return vqa_pipeline, examples

    def run_pipeline_test(self, vqa_pipeline, examples):
        outputs = vqa_pipeline(examples, top_k=1)
        self.assertEqual(
            outputs,
            [
                [{"score": ANY(float), "answer": ANY(str)}],
                [{"score": ANY(float), "answer": ANY(str)}],
            ],
        )

    @require_torch
    def test_small_model_pt(self):
        vqa_pipeline = pipeline("visual-question-answering", model="hf-internal-testing/tiny-vilt-random-vqa")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
        question = "How many cats are there?"

        outputs = vqa_pipeline(image=image, question="How many cats are there?", top_k=2)
        self.assertEqual(
            outputs, [{"score": ANY(float), "answer": ANY(str)}, {"score": ANY(float), "answer": ANY(str)}]
        )

        outputs = vqa_pipeline({"image": image, "question": question}, top_k=2)
        self.assertEqual(
            outputs, [{"score": ANY(float), "answer": ANY(str)}, {"score": ANY(float), "answer": ANY(str)}]
        )

    @slow
    @require_torch
    def test_large_model_pt(self):
        vqa_pipeline = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
        question = "How many cats are there?"

        outputs = vqa_pipeline(image=image, question=question, top_k=2)
        self.assertEqual(
            nested_simplify(outputs, decimals=4), [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]
        )

        outputs = vqa_pipeline({"image": image, "question": question}, top_k=2)
        self.assertEqual(
            nested_simplify(outputs, decimals=4), [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]
        )

        outputs = vqa_pipeline(
            [{"image": image, "question": question}, {"image": image, "question": question}], top_k=2
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2,
        )

    @require_tf
    @unittest.skip("Visual question answering not implemented in TF")
    def test_small_model_tf(self):
        pass
