import os.path

import pytest

from image_prediction.predictor import Predictor


@pytest.fixture
def predictions():
    return [
        {
            "class": "signature",
            "probabilities": {
                "signature": 1.0,
                "logo": 9.150285377746546e-19,
                "other": 4.374506412383356e-19,
                "formula": 3.582569597002796e-24,
            },
        }
    ]


@pytest.fixture
def metadata():
    return [
        {
            "page_height": 612.0,
            "page_width": 792.0,
            "height": 61.049999999999955,
            "width": 139.35000000000002,
            "page_idx": 8,
            "x1": 63.5,
            "x2": 202.85000000000002,
            "y1": 472.0,
            "y2": 533.05,
        }
    ]


@pytest.fixture
def response():
    return [
        {
            "classification": {
                "label": "signature",
                "probabilities": {"formula": 0.0, "logo": 0.0, "other": 0.0, "signature": 1.0},
            },
            "filters": {
                "allPassed": True,
                "geometry": {
                    "imageFormat": {"quotient": 2.282555282555285, "tooTall": False, "tooWide": False},
                    "imageSize": {"quotient": 0.13248234868245012, "tooLarge": False, "tooSmall": False},
                },
                "probability": {"unconfident": False},
            },
            "geometry": {"height": 61.049999999999955, "width": 139.35000000000002},
            "position": {"pageNumber": 9, "x1": 63.5, "x2": 202.85000000000002, "y1": 472.0, "y2": 533.05},
        }
    ]


@pytest.fixture
def predictor():
    return Predictor()


@pytest.fixture
def test_pdf():
    with open("./test/test_data/f2dc689ca794fccb8cd38b95f2bf6ba9.pdf", "rb") as f:
        return f.read()