import hashlib

import numpy as np
import pytest

from v1.pose import ModelValidationError, PoseAdapter


class _FakeKeypoints:
    def __init__(self, data):
        self.data = data


class _FakeBoxes:
    def __init__(self):
        self.xyxy = np.array([[10.0, 20.0, 50.0, 90.0], [1.0, 2.0, 3.0, 4.0]])
        self.conf = np.array([0.9, 0.8])
        self.cls = np.array([0, 1])


class _FakeResult:
    names = {0: "person", 1: "chair"}

    def __init__(self):
        person = [[float(index), float(index + 1), 0.9] for index in range(17)]
        chair = [[float(index), float(index + 1), 0.8] for index in range(17)]
        self.boxes = _FakeBoxes()
        self.keypoints = _FakeKeypoints(np.array([person, chair]))


def test_pose_adapter_rejects_hash_mismatch_before_loading_model(tmp_path):
    model_path = tmp_path / "pose.pt"
    model_path.write_bytes(b"not a real model")

    with pytest.raises(ModelValidationError, match="SHA-256"):
        PoseAdapter(model_path, expected_sha256="0" * 64)


def test_from_results_extracts_person_box_confidence_and_seventeen_keypoints():
    poses = PoseAdapter.from_results(_FakeResult())

    assert len(poses) == 1
    assert poses[0].box_xyxy == (10.0, 20.0, 50.0, 90.0)
    assert poses[0].box_confidence == 0.9
    assert len(poses[0].keypoints) == 17
    assert poses[0].keypoints[5].confidence == 0.9


def test_pose_adapter_rejects_non_pose_model_after_hash_validation(tmp_path):
    model_path = tmp_path / "model.pt"
    model_path.write_bytes(b"model bytes")
    expected_sha256 = hashlib.sha256(model_path.read_bytes()).hexdigest()

    class _WrongTaskModel:
        task = "detect"
        names = {0: "person"}

        class model:
            kpt_shape = (17, 3)

    with pytest.raises(ModelValidationError, match="task"):
        PoseAdapter(
            model_path,
            expected_sha256=expected_sha256,
            model_factory=lambda _path: _WrongTaskModel(),
        )