from PIL import Image
import numpy
from torch import device, cuda, no_grad
from lavis.models import load_model_and_preprocess
import misinformation.multimodal_search as ms

TEST_IMAGE_1 = "./test/data/d755771b-225e-432f-802e-fb8dc850fff7.png"
TEST_IMAGE_2 = "./test/data/IMG_2746.png"
TEST_IMAGE_3 = "./test/data/IMG_2750.png"
TEST_IMAGE_4 = "./test/data/IMG_2805.png"
TEST_IMAGE_5 = "./test/data/IMG_2806.png"
TEST_IMAGE_6 = "./test/data/IMG_2807.png"
TEST_IMAGE_7 = "./test/data/IMG_2808.png"
TEST_IMAGE_8 = "./test/data/IMG_2809.png"
TEST_IMAGE_9 = "./test/data/IMG_3755.jpg"
TEST_IMAGE_10 = "./test/data/IMG_3756.jpg"
TEST_IMAGE_11 = "./test/data/IMG_3757.jpg"
TEST_IMAGE_12 = "./test/data/pic1.png"


def test_read_img():
    my_dict = {}
    test_img = ms.MultimodalSearch.read_img(my_dict, TEST_IMAGE_2)
    assert list(numpy.array(test_img)[257][34]) == [70, 66, 63]


def test_load_feature_extractor_model_blip2():
    my_dict = {}
    multimodal_device = device("cuda" if cuda.is_available() else "cpu")
    (
        model,
        vis_processor,
        txt_processor,
    ) = ms.MultimodalSearch.load_feature_extractor_model_blip2(
        my_dict, multimodal_device
    )
    test_pic = Image.open(TEST_IMAGE_2).convert("RGB")
    test_querry = (
        "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )
    processed_pic = vis_processor["eval"](test_pic).unsqueeze(0).to(multimodal_device)
    processed_text = txt_processor["eval"](test_querry)

    with no_grad():
        with cuda.amp.autocast(enabled=(device != device("cpu"))):
            extracted_feature_img = model.extract_features(
                {"image": processed_pic, "text_input": ""}, mode="image"
            )
            extracted_feature_text = model.extract_features(
                {"image": "", "text_input": processed_text}, mode="text"
            )

    assert processed_pic[0, 0, 0, 25:35].tolist() == [
        -1.0039474964141846,
        -1.0039474964141846,
        -0.8433647751808167,
        -0.6097899675369263,
        -0.5951915383338928,
        -0.6243883967399597,
        -0.6827820539474487,
        -0.6097899675369263,
        -0.7119789123535156,
        -1.0623412132263184,
    ]

    assert (
        processed_text
        == "the bird sat on a tree located at the intersection of 23rd and 43rd streets"
    )

    assert extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist() == [
        0.04566730558872223,
        -0.042554520070552826,
        -0.06970272958278656,
        -0.009771779179573059,
        0.01446065679192543,
        0.10173682868480682,
        0.007092420011758804,
        -0.020045937970280647,
        0.12923966348171234,
        0.006452132016420364,
    ]

    assert extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist() == [
        -0.1384519338607788,
        -0.008663734421133995,
        0.006240826100111008,
        0.031466349959373474,
        0.060625165700912476,
        -0.03230545297265053,
        0.01585903950035572,
        -0.11856520175933838,
        -0.05823372304439545,
        0.036941494792699814,
    ]

    del model, vis_processor, txt_processor
    cuda.empty_cache()


def test_load_feature_extractor_model_blip():
    my_dict = {}
    multimodal_device = device("cuda" if cuda.is_available() else "cpu")
    (
        model,
        vis_processor,
        txt_processor,
    ) = ms.MultimodalSearch.load_feature_extractor_model_blip(
        my_dict, multimodal_device
    )
    test_pic = Image.open(TEST_IMAGE_2).convert("RGB")
    test_querry = (
        "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )
    processed_pic = vis_processor["eval"](test_pic).unsqueeze(0).to(multimodal_device)
    processed_text = txt_processor["eval"](test_querry)

    with no_grad():
        extracted_feature_img = model.extract_features(
            {"image": processed_pic, "text_input": ""}, mode="image"
        )
        extracted_feature_text = model.extract_features(
            {"image": "", "text_input": processed_text}, mode="text"
        )

    assert processed_pic[0, 0, 0, 25:35].tolist() == [
        -1.0039474964141846,
        -1.0039474964141846,
        -0.8433647751808167,
        -0.6097899675369263,
        -0.5951915383338928,
        -0.6243883967399597,
        -0.6827820539474487,
        -0.6097899675369263,
        -0.7119789123535156,
        -1.0623412132263184,
    ]

    assert (
        processed_text
        == "the bird sat on a tree located at the intersection of 23rd and 43rd streets"
    )

    assert extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist() == [
        -0.02480311505496502,
        0.05037587881088257,
        0.039517853409051895,
        -0.06994109600782394,
        -0.12886561453342438,
        0.047039758414030075,
        -0.11620642244815826,
        -0.003398326924070716,
        -0.07324369996786118,
        0.06994668394327164,
    ]

    assert extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist() == [
        0.0118643119931221,
        -0.01291718054562807,
        -0.0009687161073088646,
        0.01428765058517456,
        -0.05591396614909172,
        0.07386433333158493,
        -0.11475936323404312,
        0.01620068959891796,
        0.0062415082938969135,
        0.0034833091776818037,
    ]

    del model, vis_processor, txt_processor
    cuda.empty_cache()


def test_load_feature_extractor_model_albef():
    my_dict = {}
    multimodal_device = device("cuda" if cuda.is_available() else "cpu")
    (
        model,
        vis_processor,
        txt_processor,
    ) = ms.MultimodalSearch.load_feature_extractor_model_albef(
        my_dict, multimodal_device
    )
    test_pic = Image.open(TEST_IMAGE_2).convert("RGB")
    test_querry = (
        "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )
    processed_pic = vis_processor["eval"](test_pic).unsqueeze(0).to(multimodal_device)
    processed_text = txt_processor["eval"](test_querry)

    with no_grad():
        extracted_feature_img = model.extract_features(
            {"image": processed_pic, "text_input": ""}, mode="image"
        )
        extracted_feature_text = model.extract_features(
            {"image": "", "text_input": processed_text}, mode="text"
        )

    assert processed_pic[0, 0, 0, 25:35].tolist() == [
        -1.0039474964141846,
        -1.0039474964141846,
        -0.8433647751808167,
        -0.6097899675369263,
        -0.5951915383338928,
        -0.6243883967399597,
        -0.6827820539474487,
        -0.6097899675369263,
        -0.7119789123535156,
        -1.0623412132263184,
    ]

    assert (
        processed_text
        == "the bird sat on a tree located at the intersection of 23rd and 43rd streets"
    )

    assert extracted_feature_img["image_embeds_proj"][0, 0, 10:20].tolist() == [
        0.08971136063337326,
        -0.10915573686361313,
        -0.020636577159166336,
        0.048121627420186996,
        -0.05943416804075241,
        -0.129856139421463,
        -0.0034469354432076216,
        0.017888527363538742,
        -0.03284582123160362,
        -0.1037328764796257,
    ]

    assert extracted_feature_text["text_embeds_proj"][0, 0, 10:20].tolist() == [
        -0.06229640915989876,
        0.11278597265481949,
        0.06628583371639252,
        0.1649140566587448,
        0.068987175822258,
        0.006291372701525688,
        0.03244050219655037,
        -0.049556829035282135,
        0.050752390176057816,
        -0.0421440489590168,
    ]

    del model, vis_processor, txt_processor
    cuda.empty_cache()


def test_load_feature_extractor_model_clip_base():
    my_dict = {}
    multimodal_device = device("cuda" if cuda.is_available() else "cpu")
    (
        model,
        vis_processor,
        txt_processor,
    ) = ms.MultimodalSearch.load_feature_extractor_model_clip_base(
        my_dict, multimodal_device
    )
    test_pic = Image.open(TEST_IMAGE_2).convert("RGB")
    test_querry = (
        "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )
    processed_pic = vis_processor["eval"](test_pic).unsqueeze(0).to(multimodal_device)
    processed_text = txt_processor["eval"](test_querry)

    with no_grad():
        extracted_feature_img = model.extract_features({"image": processed_pic})
        extracted_feature_text = model.extract_features({"text_input": processed_text})

    assert processed_pic[0, 0, 0, 25:35].tolist() == [
        -0.7995694875717163,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7703726291656494,
        -0.7703726291656494,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7703726291656494,
        -0.7703726291656494,
        -0.7703726291656494,
    ]

    assert (
        processed_text
        == "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )

    assert extracted_feature_img[0, 10:20].tolist() == [
        0.15101124346256256,
        -0.03759124130010605,
        -0.40093156695365906,
        -0.32228705286979675,
        0.1576370894908905,
        -0.23340347409248352,
        -0.3892208933830261,
        0.20170584321022034,
        -0.030034437775611877,
        0.19082790613174438,
    ]

    assert extracted_feature_text[0, 10:20].tolist() == [
        0.15391531586647034,
        0.3078577518463135,
        0.21737979352474213,
        0.0775114893913269,
        -0.3013279139995575,
        0.2806251049041748,
        -0.0407320111989975,
        -0.02664487063884735,
        -0.1858849972486496,
        0.20347601175308228,
    ]

    del model, vis_processor, txt_processor
    cuda.empty_cache()


def test_load_feature_extractor_model_clip_vitl14():
    my_dict = {}
    multimodal_device = device("cuda" if cuda.is_available() else "cpu")
    (
        model,
        vis_processor,
        txt_processor,
    ) = ms.MultimodalSearch.load_feature_extractor_model_clip_vitl14(
        my_dict, multimodal_device
    )
    test_pic = Image.open(TEST_IMAGE_2).convert("RGB")
    test_querry = (
        "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )
    processed_pic = vis_processor["eval"](test_pic).unsqueeze(0).to(multimodal_device)
    processed_text = txt_processor["eval"](test_querry)

    with no_grad():
        extracted_feature_img = model.extract_features({"image": processed_pic})
        extracted_feature_text = model.extract_features({"text_input": processed_text})

    assert processed_pic[0, 0, 0, 25:35].tolist() == [
        -0.7995694875717163,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7703726291656494,
        -0.7703726291656494,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7703726291656494,
        -0.7703726291656494,
        -0.7703726291656494,
    ]

    assert (
        processed_text
        == "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )

    assert extracted_feature_img[0, 10:20].tolist() == [
        -0.3911527395248413,
        -0.35456305742263794,
        0.5724918842315674,
        0.3184954524040222,
        0.23444902896881104,
        -0.14105141162872314,
        0.26309096813201904,
        -0.0559774711728096,
        0.19491413235664368,
        0.01419895887374878,
    ]

    assert extracted_feature_text[0, 10:20].tolist() == [
        -0.07539052516222,
        0.0939129889011383,
        -0.2643853425979614,
        -0.2459949105978012,
        0.2387947291135788,
        -0.5204038023948669,
        -0.514020562171936,
        -0.32557412981987,
        0.18563221395015717,
        -0.3183072805404663,
    ]

    del model, vis_processor, txt_processor
    cuda.empty_cache()


def test_load_feature_extractor_model_clip_vitl14_336():
    my_dict = {}
    multimodal_device = device("cuda" if cuda.is_available() else "cpu")
    (
        model,
        vis_processor,
        txt_processor,
    ) = ms.MultimodalSearch.load_feature_extractor_model_clip_vitl14_336(
        my_dict, multimodal_device
    )
    test_pic = Image.open(TEST_IMAGE_2).convert("RGB")
    test_querry = (
        "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )
    processed_pic = vis_processor["eval"](test_pic).unsqueeze(0).to(multimodal_device)
    processed_text = txt_processor["eval"](test_querry)

    with no_grad():
        extracted_feature_img = model.extract_features({"image": processed_pic})
        extracted_feature_text = model.extract_features({"text_input": processed_text})

    assert processed_pic[0, 0, 0, 25:35].tolist() == [
        -0.7995694875717163,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.7849710583686829,
        -0.9163569211959839,
        -1.149931788444519,
        -1.0039474964141846,
    ]

    assert (
        processed_text
        == "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
    )

    assert extracted_feature_img[0, 10:20].tolist() == [
        -0.15060146152973175,
        -0.1998099535703659,
        0.5503129363059998,
        0.2589969336986542,
        -0.0182882659137249,
        -0.12753525376319885,
        0.018985718488693237,
        -0.17110440135002136,
        0.02220013737678528,
        0.01086437702178955,
    ]

    assert extracted_feature_text[0, 10:20].tolist() == [
        -0.1172553077340126,
        0.07105237245559692,
        -0.283934086561203,
        -0.24353823065757751,
        0.22662702202796936,
        -0.472959041595459,
        -0.5191791653633118,
        -0.29402273893356323,
        0.22669515013694763,
        -0.32044747471809387,
    ]

    del model, vis_processor, txt_processor
    cuda.empty_cache()