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import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_accelerator,
    require_vision,
    slow,
    torch_device,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image
else:

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


@is_pipeline_test
@require_torch
@require_vision
class VisualQuestionAnsweringPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        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)}]
        )

    @require_torch
    @require_torch_accelerator
    def test_small_model_pt_blip2(self):
        vqa_pipeline = pipeline(
            "visual-question-answering", model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration"
        )
        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
        question = "How many cats are there?"

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

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

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

        vqa_pipeline = pipeline(
            "visual-question-answering",
            model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration",
            model_kwargs={"torch_dtype": torch.float16},
            device=torch_device,
        )
        self.assertEqual(vqa_pipeline.model.device, torch.device("{}:0".format(torch_device)))
        self.assertEqual(vqa_pipeline.model.language_model.dtype, torch.float16)
        self.assertEqual(vqa_pipeline.model.vision_model.dtype, torch.float16)

        outputs = vqa_pipeline(image=image, question=question)
        self.assertEqual(outputs, [{"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,
        )

    @slow
    @require_torch
    @require_torch_accelerator
    def test_large_model_pt_blip2(self):
        vqa_pipeline = pipeline(
            "visual-question-answering",
            model="Salesforce/blip2-opt-2.7b",
            model_kwargs={"torch_dtype": torch.float16},
            device=torch_device,
        )
        self.assertEqual(vqa_pipeline.model.device, torch.device("{}:0".format(torch_device)))
        self.assertEqual(vqa_pipeline.model.language_model.dtype, torch.float16)

        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
        question = "Question: how many cats are there? Answer:"

        outputs = vqa_pipeline(image=image, question=question)
        self.assertEqual(outputs, [{"answer": "two"}])

        outputs = vqa_pipeline({"image": image, "question": question})
        self.assertEqual(outputs, [{"answer": "two"}])

        outputs = vqa_pipeline([{"image": image, "question": question}, {"image": image, "question": question}])
        self.assertEqual(outputs, [[{"answer": "two"}]] * 2)

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