"""学習機能付き品質チェックドメインのリポジトリインターフェーステスト

TDD準拠テスト:
- QualityLearningRepository (ABC)
- EpisodeAnalysisRepository (ABC)
- ReaderFeedbackRepository (ABC)
- WritingStyleProfileRepository (ABC)
- LearningEvaluatorRepository (ABC)


仕様書: SPEC-UNIT-TEST
"""

from abc import ABC
from datetime import datetime, timedelta
from unittest.mock import Mock

import pytest

from noveler.domain.learning.entities import LearningQualityEvaluator, QualityLearningModel
from noveler.domain.learning.repositories import (
    EpisodeAnalysisRepository,
    LearningEvaluatorRepository,
    QualityLearningRepository,
    ReaderFeedbackRepository,
    WritingStyleProfileRepository,
)
from noveler.domain.learning.value_objects import (
    QualityMetric,
    WritingStyleProfile,
)
from noveler.domain.value_objects.project_time import ProjectTimezone, project_now

# JSTタイムゾーン
JST = ProjectTimezone.jst().timezone


class TestQualityLearningRepository:
    """QualityLearningRepository抽象インターフェーステスト"""

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-QUALITY_LEARNING_REP")
    def test_quality_learning_repository_is_abstract(self) -> None:
        """QualityLearningRepositoryが抽象クラスであることを確認"""
        assert issubclass(QualityLearningRepository, ABC)

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-QUALITY_LEARNING_REP")
    def test_quality_learning_repository_abstract_methods(self) -> None:
        """QualityLearningRepositoryの抽象メソッド確認"""
        abstract_methods = QualityLearningRepository.__abstractmethods__
        expected_methods = {
            "save_model",
            "find_model_by_id",
            "find_models_by_project",
            "save_learning_history",
            "get_learning_history",
        }
        assert abstract_methods == expected_methods

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-QUALITY_LEARNING_REP")
    def test_quality_learning_repository_save_model_signature(self) -> None:
        """save_modelメソッドのシグネチャ確認"""

        class MockLearningRepo(QualityLearningRepository):
            def __init__(self) -> None:
                self.models = {}

            def save_model(self, model: QualityLearningModel) -> None:
                self.models[model.model_id] = model

            def find_model_by_id(self, _model_id: str) -> QualityLearningModel | None:
                return self.models.get(_model_id)

            def find_models_by_project(self, _project_id: str) -> list[QualityLearningModel]:
                return []

            def save_learning_history(self, _model_id: str, training_data: list[dict], accuracy: float) -> None:
                pass

            def get_learning_history(self, _model_id: str) -> list[dict]:
                return []

        repo = MockLearningRepo()
        model = Mock(spec=QualityLearningModel)
        model._model_id = "model-001"

        # 例外が発生しないことを確認
        repo.save_model(model)
        assert repo.find_model_by_id("model-001") == model

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-QUALITY_LEARNING_REP")
    def test_quality_learning_repository_find_models_by_project_signature(self) -> None:
        """find_models_by_projectメソッドのシグネチャ確認"""

        class MockLearningRepo(QualityLearningRepository):
            def __init__(self) -> None:
                self.models = [
                    Mock(spec=QualityLearningModel, model_id="model-001", _project_id="project-1"),
                    Mock(spec=QualityLearningModel, model_id="model-002", _project_id="project-1"),
                    Mock(spec=QualityLearningModel, model_id="model-003", _project_id="project-2"),
                ]

            def save_model(self, model: QualityLearningModel) -> None:
                pass

            def find_model_by_id(self, _model_id: str) -> QualityLearningModel | None:
                return None

            def find_models_by_project(self, _project_id: str) -> list[QualityLearningModel]:
                return [m for m in self.models if m._project_id == _project_id]

            def save_learning_history(self, model_id: str, training_data: list[dict], accuracy: float) -> None:
                pass

            def get_learning_history(self, _model_id: str) -> list[dict]:
                return []

        repo = MockLearningRepo()

        # プロジェクト1のモデル
        models_p1 = repo.find_models_by_project("project-1")
        assert isinstance(models_p1, list)
        assert len(models_p1) == 2

        # プロジェクト2のモデル
        models_p2 = repo.find_models_by_project("project-2")
        assert len(models_p2) == 1

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-QUALITY_LEARNING_REP")
    def test_quality_learning_repository_learning_history_signature(self) -> None:
        """学習履歴関連メソッドのシグネチャ確認"""

        class MockLearningRepo(QualityLearningRepository):
            def __init__(self) -> None:
                self.history = {}

            def save_model(self, model: QualityLearningModel) -> None:
                pass

            def find_model_by_id(self, _model_id: str) -> QualityLearningModel | None:
                return None

            def find_models_by_project(self, _project_id: str) -> list[QualityLearningModel]:
                return []

            def save_learning_history(self, model_id: str, training_data: list[dict], accuracy: float) -> None:
                if model_id not in self.history:
                    self.history[model_id] = []
                self.history[model_id].append(
                    {"training_data": training_data, "accuracy": accuracy, "timestamp": project_now().datetime}
                )

            def get_learning_history(self, model_id: str) -> list[dict]:
                return self.history.get(model_id, [])

        repo = MockLearningRepo()

        # 学習履歴の保存
        training_data = [{"episode_id": "ep1", "score": 85.0}, {"episode_id": "ep2", "score": 90.0}]
        repo.save_learning_history("model-001", training_data, 0.95)

        # 学習履歴の取得
        history = repo.get_learning_history("model-001")
        assert len(history) == 1
        assert history[0]["accuracy"] == 0.95
        assert len(history[0]["training_data"]) == 2


class TestEpisodeAnalysisRepository:
    """EpisodeAnalysisRepository抽象インターフェーステスト"""

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-EPISODE_ANALYSIS_REP")
    def test_episode_analysis_repository_is_abstract(self) -> None:
        """EpisodeAnalysisRepositoryが抽象クラスであることを確認"""
        assert issubclass(EpisodeAnalysisRepository, ABC)

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-EPISODE_ANALYSIS_REP")
    def test_episode_analysis_repository_abstract_methods(self) -> None:
        """EpisodeAnalysisRepositoryの抽象メソッド確認"""
        abstract_methods = EpisodeAnalysisRepository.__abstractmethods__
        expected_methods = {
            "save_episode_analysis",
            "find_episode_analysis",
            "find_project_episodes_analysis",
            "find_episodes_by_quality_range",
            "find_episodes_with_high_reader_rating",
        }
        assert abstract_methods == expected_methods

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-EPISODE_ANALYSIS_REP")
    def test_episode_analysis_repository_save_and_find_signature(self) -> None:
        """save/find分析データメソッドのシグネチャ確認"""

        class MockAnalysisRepo(EpisodeAnalysisRepository):
            def __init__(self) -> None:
                self.analysis_data = {}

            def save_episode_analysis(self, _episode_id: str, analysis_data: dict[str, object]) -> None:
                self.analysis_data[_episode_id] = analysis_data

            def find_episode_analysis(self, _episode_id: str) -> dict[str, object] | None:
                return self.analysis_data.get(_episode_id)

            def find_project_episodes_analysis(self, _project_id: str) -> list[dict[str, object]]:
                return []

            def find_episodes_by_quality_range(
                self,
                _project_id: str,
                _metric: QualityMetric,
                _min_score: float,
                _max_score: float,
            ) -> list[dict[str, object]]:
                return []

            def find_episodes_with_high_reader_rating(
                self, _project_id: str, _min_rating: float = 4.0
            ) -> list[dict[str, object]]:
                return []

        repo = MockAnalysisRepo()

        # 分析データの保存
        analysis = {
            "episode_id": "ep-001",
            "project_id": "project-1",
            "quality_scores": {"readability": 85.0, "engagement": 90.0, "character_development": 88.0},
            "analyzed_at": project_now().datetime,
        }
        repo.save_episode_analysis("ep-001", analysis)

        # 分析データの取得
        saved = repo.find_episode_analysis("ep-001")
        assert saved is not None
        assert saved["quality_scores"]["readability"] == 85.0

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-EPISODE_ANALYSIS_REP")
    def test_episode_analysis_repository_quality_range_signature(self) -> None:
        """品質範囲検索メソッドのシグネチャ確認"""

        class MockAnalysisRepo(EpisodeAnalysisRepository):
            def __init__(self) -> None:
                self.analysis_data = {
                    "ep-001": {"project_id": "p1", "readability": 85.0, "engagement": 90.0},
                    "ep-002": {"project_id": "p1", "readability": 75.0, "engagement": 80.0},
                    "ep-003": {"project_id": "p1", "readability": 95.0, "engagement": 88.0},
                    "ep-004": {"project_id": "p2", "readability": 82.0, "engagement": 85.0},
                }

            def save_episode_analysis(self, _episode_id: str, analysis_data: dict[str, object]) -> None:
                pass

            def find_episode_analysis(self, _episode_id: str) -> dict[str, object] | None:
                return None

            def find_project_episodes_analysis(self, _project_id: str) -> list[dict[str, object]]:
                return []

            def find_episodes_by_quality_range(
                self,
                _project_id: str,
                _metric: QualityMetric,
                _min_score: float,
                _max_score: float,
            ) -> list[dict[str, object]]:
                result = []
                for ep_id, data in self.analysis_data.items():
                    if data["project_id"] == _project_id:
                        score = data.get(_metric.value.lower(), 0)
                        if _min_score <= score <= _max_score:
                            result.append({"episode_id": ep_id, **data})
                return result

            def find_episodes_with_high_reader_rating(
                self, _project_id: str, _min_rating: float = 4.0
            ) -> list[dict[str, object]]:
                return []

        repo = MockAnalysisRepo()

        # 読みやすさ80-90の範囲で検索
        results = repo.find_episodes_by_quality_range("p1", QualityMetric.READABILITY, 80.0, 90.0)
        assert len(results) == 1
        assert results[0]["episode_id"] == "ep-001"

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-EPISODE_ANALYSIS_REP")
    def test_episode_analysis_repository_high_rating_signature(self) -> None:
        """高評価エピソード検索メソッドのシグネチャ確認"""

        class MockAnalysisRepo(EpisodeAnalysisRepository):
            def __init__(self) -> None:
                self.episodes_with_ratings = [
                    {"episode_id": "ep-001", "project_id": "p1", "reader_rating": 4.5},
                    {"episode_id": "ep-002", "project_id": "p1", "reader_rating": 3.8},
                    {"episode_id": "ep-003", "project_id": "p1", "reader_rating": 4.2},
                    {"episode_id": "ep-004", "project_id": "p2", "reader_rating": 4.7},
                ]

            def save_episode_analysis(self, episode_id: str, analysis_data: dict[str, object]) -> None:
                pass

            def find_episode_analysis(self, _episode_id: str) -> dict[str, object] | None:
                return None

            def find_project_episodes_analysis(self, _project_id: str) -> list[dict[str, object]]:
                return []

            def find_episodes_by_quality_range(
                self,
                _project_id: str,
                _metric: QualityMetric,
                _min_score: float,
                _max_score: float,
            ) -> list[dict[str, object]]:
                return []

            def find_episodes_with_high_reader_rating(
                self, _project_id: str, _min_rating: float = 4.0
            ) -> list[dict[str, object]]:
                return [
                    ep
                    for ep in self.episodes_with_ratings
                    if ep["project_id"] == _project_id and ep["reader_rating"] >= _min_rating
                ]

        repo = MockAnalysisRepo()

        # デフォルト閾値(4.0以上)
        high_rated = repo.find_episodes_with_high_reader_rating("p1")
        assert len(high_rated) == 2
        assert all(ep["reader_rating"] >= 4.0 for ep in high_rated)

        # カスタム閾値(4.3以上)
        very_high_rated = repo.find_episodes_with_high_reader_rating("p1", min_rating=4.3)
        assert len(very_high_rated) == 1
        assert very_high_rated[0]["reader_rating"] == 4.5


class TestReaderFeedbackRepository:
    """ReaderFeedbackRepository抽象インターフェーステスト"""

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-READER_FEEDBACK_REPO")
    def test_reader_feedback_repository_is_abstract(self) -> None:
        """ReaderFeedbackRepositoryが抽象クラスであることを確認"""
        assert issubclass(ReaderFeedbackRepository, ABC)

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-READER_FEEDBACK_REPO")
    def test_reader_feedback_repository_abstract_methods(self) -> None:
        """ReaderFeedbackRepositoryの抽象メソッド確認"""
        abstract_methods = ReaderFeedbackRepository.__abstractmethods__
        expected_methods = {
            "save_reader_feedback",
            "find_feedback_by_episode",
            "find_feedback_by_project",
            "find_feedback_by_date_range",
            "calculate_average_rating",
            "find_trending_episodes",
        }
        assert abstract_methods == expected_methods

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-READER_FEEDBACK_REPO")
    def test_reader_feedback_repository_save_and_find_signature(self) -> None:
        """フィードバック保存・検索メソッドのシグネチャ確認"""

        class MockFeedbackRepo(ReaderFeedbackRepository):
            def __init__(self) -> None:
                self.feedback_data = []

            def save_reader_feedback(self, episode_id: str, feedback_data: dict[str, object]) -> None:
                feedback_data["episode_id"] = episode_id
                feedback_data["created_at"] = project_now().datetime
                self.feedback_data.append(feedback_data)

            def find_feedback_by_episode(self, episode_id: str) -> list[dict[str, object]]:
                return [f for f in self.feedback_data if f["episode_id"] == episode_id]

            def find_feedback_by_project(self, _project_id: str) -> list[dict[str, object]]:
                return [f for f in self.feedback_data if f.get("project_id") == _project_id]

            def find_feedback_by_date_range(
                self,
                _project_id: str,
                _start_date: datetime,
                _end_date: datetime,
            ) -> list[dict[str, object]]:
                return []

            def calculate_average_rating(self, _project_id: str) -> float:
                return 0.0

            def find_trending_episodes(self, _project_id: str, _days: int = 7) -> list[dict[str, object]]:
                return []

        repo = MockFeedbackRepo()

        # フィードバックの保存
        feedback = {"project_id": "p1", "rating": 4.5, "comment": "とても面白かった!", "user_id": "user-001"}
        repo.save_reader_feedback("ep-001", feedback)

        # エピソード別フィードバック取得
        ep_feedback = repo.find_feedback_by_episode("ep-001")
        assert len(ep_feedback) == 1
        assert ep_feedback[0]["rating"] == 4.5

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-READER_FEEDBACK_REPO")
    def test_reader_feedback_repository_date_range_signature(self) -> None:
        """期間検索メソッドのシグネチャ確認"""

        class MockFeedbackRepo(ReaderFeedbackRepository):
            def __init__(self) -> None:
                now = project_now().datetime
                self.feedback_data = [
                    {"project_id": "p1", "created_at": now - timedelta(days=10)},
                    {"project_id": "p1", "created_at": now - timedelta(days=5)},
                    {"project_id": "p1", "created_at": now - timedelta(days=1)},
                    {"project_id": "p2", "created_at": now - timedelta(days=3)},
                ]

            def save_reader_feedback(self, episode_id: str, feedback_data: dict[str, object]) -> None:
                pass

            def find_feedback_by_episode(self, _episode_id: str) -> list[dict[str, object]]:
                return []

            def find_feedback_by_project(self, _project_id: str) -> list[dict[str, object]]:
                return []

            def find_feedback_by_date_range(
                self,
                _project_id: str,
                _start_date: datetime,
                _end_date: datetime,
            ) -> list[dict[str, object]]:
                return [
                    f
                    for f in self.feedback_data
                    if f["project_id"] == _project_id and _start_date <= f["created_at"] <= _end_date
                ]

            def calculate_average_rating(self, _project_id: str) -> float:
                return 0.0

            def find_trending_episodes(self, _project_id: str, _days: int = 7) -> list[dict[str, object]]:
                return []

        repo = MockFeedbackRepo()

        # 過去7日間のフィードバック
        now = project_now().datetime
        week_ago = now - timedelta(days=7)
        recent_feedback = repo.find_feedback_by_date_range("p1", week_ago, now)
        assert len(recent_feedback) == 2

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-READER_FEEDBACK_REPO")
    def test_reader_feedback_repository_average_rating_signature(self) -> None:
        """平均評価計算メソッドのシグネチャ確認"""

        class MockFeedbackRepo(ReaderFeedbackRepository):
            def __init__(self) -> None:
                self.ratings = {"p1": [4.5, 4.0, 3.5, 5.0, 4.5], "p2": [3.0, 3.5, 4.0]}

            def save_reader_feedback(self, episode_id: str, feedback_data: dict[str, object]) -> None:
                pass

            def find_feedback_by_episode(self, _episode_id: str) -> list[dict[str, object]]:
                return []

            def find_feedback_by_project(self, _project_id: str) -> list[dict[str, object]]:
                return []

            def find_feedback_by_date_range(
                self,
                _project_id: str,
                _start_date: datetime,
                _end_date: datetime,
            ) -> list[dict[str, object]]:
                return []

            def calculate_average_rating(self, _project_id: str) -> float:
                if self.ratings.get(_project_id):
                    return sum(self.ratings[_project_id]) / len(self.ratings[_project_id])
                return 0.0

            def find_trending_episodes(self, _project_id: str, _days: int = 7) -> list[dict[str, object]]:
                return []

        repo = MockFeedbackRepo()

        # プロジェクト1の平均評価
        avg_p1 = repo.calculate_average_rating("p1")
        assert avg_p1 == 4.3

        # データがないプロジェクト
        avg_none = repo.calculate_average_rating("p3")
        assert avg_none == 0.0

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-READER_FEEDBACK_REPO")
    def test_reader_feedback_repository_trending_signature(self) -> None:
        """トレンドエピソード検索メソッドのシグネチャ確認"""

        class MockFeedbackRepo(ReaderFeedbackRepository):
            def save_reader_feedback(self, episode_id: str, feedback_data: dict[str, object]) -> None:
                pass

            def find_feedback_by_episode(self, _episode_id: str) -> list[dict[str, object]]:
                return []

            def find_feedback_by_project(self, _project_id: str) -> list[dict[str, object]]:
                return []

            def find_feedback_by_date_range(
                self,
                _project_id: str,
                _start_date: datetime,
                _end_date: datetime,
            ) -> list[dict[str, object]]:
                return []

            def calculate_average_rating(self, _project_id: str) -> float:
                return 0.0

            def find_trending_episodes(self, project_id: str, days: int = 7) -> list[dict[str, object]]:
                # 模擬的なトレンドデータ
                if project_id == "p1" and days == 7:
                    return [
                        {"episode_id": "ep-010", "trend_score": 95.0, "view_count": 1500},
                        {"episode_id": "ep-009", "trend_score": 88.0, "view_count": 1200},
                        {"episode_id": "ep-011", "trend_score": 82.0, "view_count": 1000},
                    ]
                return []

        repo = MockFeedbackRepo()

        # デフォルト期間(7日)
        trending = repo.find_trending_episodes("p1")
        assert len(trending) == 3
        assert trending[0]["trend_score"] == 95.0

        # カスタム期間
        trending_30 = repo.find_trending_episodes("p1", days=30)
        assert len(trending_30) == 0


class TestWritingStyleProfileRepository:
    """WritingStyleProfileRepository抽象インターフェーステスト"""

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-WRITING_STYLE_PROFIL")
    def test_writing_style_profile_repository_is_abstract(self) -> None:
        """WritingStyleProfileRepositoryが抽象クラスであることを確認"""
        assert issubclass(WritingStyleProfileRepository, ABC)

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-WRITING_STYLE_PROFIL")
    def test_writing_style_profile_repository_abstract_methods(self) -> None:
        """WritingStyleProfileRepositoryの抽象メソッド確認"""
        abstract_methods = WritingStyleProfileRepository.__abstractmethods__
        expected_methods = {
            "save_profile",
            "find_profile_by_id",
            "find_profiles_by_project",
            "find_similar_profiles",
            "update_profile",
            "find_profiles_by_confidence",
        }
        assert abstract_methods == expected_methods

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-WRITING_STYLE_PROFIL")
    def test_writing_style_profile_repository_save_and_find_signature(self) -> None:
        """プロファイル保存・検索メソッドのシグネチャ確認"""

        class MockStyleRepo(WritingStyleProfileRepository):
            def __init__(self) -> None:
                self.profiles = {}

            def save_profile(self, profile: WritingStyleProfile) -> None:
                self.profiles[profile.profile_id] = profile

            def find_profile_by_id(self, _profile_id: str) -> WritingStyleProfile | None:
                return self.profiles.get(_profile_id)

            def find_profiles_by_project(self, _project_id: str) -> list[WritingStyleProfile]:
                return []

            def find_similar_profiles(
                self,
                _reference_profile: WritingStyleProfile,
                _similarity_threshold: float = 0.7,
            ) -> list[WritingStyleProfile]:
                return []

            def update_profile(self, profile: WritingStyleProfile) -> None:
                pass

            def find_profiles_by_confidence(self, _min_confidence: float) -> list[WritingStyleProfile]:
                return []

        repo = MockStyleRepo()
        profile = Mock(spec=WritingStyleProfile)
        profile.profile_id = "style-001"

        # 保存と取得
        repo.save_profile(profile)
        saved = repo.find_profile_by_id("style-001")
        assert saved == profile

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-WRITING_STYLE_PROFIL")
    def test_writing_style_profile_repository_similar_profiles_signature(self) -> None:
        """類似プロファイル検索メソッドのシグネチャ確認"""

        class MockStyleRepo(WritingStyleProfileRepository):
            def __init__(self) -> None:
                # 特徴量を持つモックプロファイル
                self.profiles = []
                for i in range(5):
                    profile = Mock(spec=WritingStyleProfile)
                    profile.profile_id = f"style-{i:03d}"
                    profile.features = {
                        "sentence_length_avg": 20.0 + i * 2,
                        "dialogue_ratio": 0.3 + i * 0.05,
                        "description_density": 0.5 - i * 0.05,
                    }
                    self.profiles.append(profile)

            def save_profile(self, profile: WritingStyleProfile) -> None:
                pass

            def find_profile_by_id(self, _profile_id: str) -> WritingStyleProfile | None:
                return None

            def find_profiles_by_project(self, _project_id: str) -> list[WritingStyleProfile]:
                return []

            def find_similar_profiles(
                self,
                _reference_profile: WritingStyleProfile,
                _similarity_threshold: float = 0.7,
            ) -> list[WritingStyleProfile]:
                # 簡易的な類似度計算
                similar = []
                ref_features = _reference_profile.features

                for profile in self.profiles:
                    if profile.profile_id == _reference_profile.profile_id:
                        continue

                    # 特徴量の差分計算
                    diff = sum(abs(profile.features[key] - ref_features[key]) for key in ref_features)
                    similarity = 1.0 - (diff / len(ref_features))

                    if similarity >= _similarity_threshold:
                        similar.append(profile)

                return similar

            def update_profile(self, profile: WritingStyleProfile) -> None:
                pass

            def find_profiles_by_confidence(self, _min_confidence: float) -> list[WritingStyleProfile]:
                return []

        repo = MockStyleRepo()

        # 参照プロファイル
        ref_profile = Mock(spec=WritingStyleProfile)
        ref_profile.profile_id = "ref-style"
        ref_profile.features = {"sentence_length_avg": 22.0, "dialogue_ratio": 0.35, "description_density": 0.45}

        # デフォルト閾値での検索
        similar = repo.find_similar_profiles(ref_profile)
        assert isinstance(similar, list)

        # 高い閾値での検索
        very_similar = repo.find_similar_profiles(ref_profile, similarity_threshold=0.9)
        assert len(very_similar) <= len(similar)

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-WRITING_STYLE_PROFIL")
    def test_writing_style_profile_repository_confidence_signature(self) -> None:
        """信頼度検索メソッドのシグネチャ確認"""

        class MockStyleRepo(WritingStyleProfileRepository):
            def __init__(self) -> None:
                self.profiles = []
                for i in range(5):
                    profile = Mock(spec=WritingStyleProfile)
                    profile.profile_id = f"style-{i:03d}"
                    profile.confidence = 0.6 + i * 0.1
                    self.profiles.append(profile)

            def save_profile(self, profile: WritingStyleProfile) -> None:
                pass

            def find_profile_by_id(self, _profile_id: str) -> WritingStyleProfile | None:
                return None

            def find_profiles_by_project(self, _project_id: str) -> list[WritingStyleProfile]:
                return []

            def find_similar_profiles(
                self,
                _reference_profile: WritingStyleProfile,
                _similarity_threshold: float = 0.7,
            ) -> list[WritingStyleProfile]:
                return []

            def update_profile(self, profile: WritingStyleProfile) -> None:
                pass

            def find_profiles_by_confidence(self, min_confidence: float) -> list[WritingStyleProfile]:
                return [p for p in self.profiles if p.confidence >= min_confidence]

        repo = MockStyleRepo()

        # 信頼度0.8以上
        high_confidence = repo.find_profiles_by_confidence(0.8)
        assert len(high_confidence) == 3
        assert all(p.confidence >= 0.8 for p in high_confidence)


class TestLearningEvaluatorRepository:
    """LearningEvaluatorRepository抽象インターフェーステスト"""

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-LEARNING_EVALUATOR_R")
    def test_learning_evaluator_repository_is_abstract(self) -> None:
        """LearningEvaluatorRepositoryが抽象クラスであることを確認"""
        assert issubclass(LearningEvaluatorRepository, ABC)

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-LEARNING_EVALUATOR_R")
    def test_learning_evaluator_repository_abstract_methods(self) -> None:
        """LearningEvaluatorRepositoryの抽象メソッド確認"""
        abstract_methods = LearningEvaluatorRepository.__abstractmethods__
        expected_methods = {
            "save_evaluator",
            "find_evaluator_by_id",
            "find_evaluator_by_project",
            "save_evaluation_result",
            "find_evaluation_history",
            "find_evaluation_improvements",
        }
        assert abstract_methods == expected_methods

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-LEARNING_EVALUATOR_R")
    def test_learning_evaluator_repository_save_and_find_signature(self) -> None:
        """評価器保存・検索メソッドのシグネチャ確認"""

        class MockEvaluatorRepo(LearningEvaluatorRepository):
            def __init__(self) -> None:
                self.evaluators = {}
                self.project_evaluators = {}

            def save_evaluator(self, evaluator: LearningQualityEvaluator) -> None:
                self.evaluators[evaluator.evaluator_id] = evaluator
                self.project_evaluators[evaluator.project_id] = evaluator

            def find_evaluator_by_id(self, _evaluator_id: str) -> LearningQualityEvaluator | None:
                return self.evaluators.get(_evaluator_id)

            def find_evaluator_by_project(self, project_id: str) -> LearningQualityEvaluator | None:
                return self.project_evaluators.get(project_id)

            def save_evaluation_result(
                self, _evaluator_id: str, episode_id: str, evaluation_data: dict[str, object]
            ) -> None:
                pass

            def find_evaluation_history(self, _evaluator_id: str, _limit: int = 100) -> list[dict[str, object]]:
                return []

            def find_evaluation_improvements(self, _evaluator_id: str, _days: int = 30) -> list[dict[str, object]]:
                return []

        repo = MockEvaluatorRepo()
        evaluator = Mock(spec=LearningQualityEvaluator)
        evaluator._evaluator_id = "eval-001"
        evaluator.project_id = "project-1"

        # 保存
        repo.save_evaluator(evaluator)

        # ID検索
        found_by_id = repo.find_evaluator_by_id("eval-001")
        assert found_by_id == evaluator

        # プロジェクト検索
        found_by_project = repo.find_evaluator_by_project("project-1")
        assert found_by_project == evaluator

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-LEARNING_EVALUATOR_R")
    def test_learning_evaluator_repository_evaluation_result_signature(self) -> None:
        """評価結果保存メソッドのシグネチャ確認"""

        class MockEvaluatorRepo(LearningEvaluatorRepository):
            def __init__(self) -> None:
                self.evaluation_results = {}

            def save_evaluator(self, evaluator: LearningQualityEvaluator) -> None:
                pass

            def find_evaluator_by_id(self, _evaluator_id: str) -> LearningQualityEvaluator | None:
                return None

            def find_evaluator_by_project(self, _project_id: str) -> LearningQualityEvaluator | None:
                return None

            def save_evaluation_result(
                self, evaluator_id: str, episode_id: str, evaluation_data: dict[str, object]
            ) -> None:
                if evaluator_id not in self.evaluation_results:
                    self.evaluation_results[evaluator_id] = []

                result = {"episode_id": episode_id, "evaluated_at": project_now().datetime, **evaluation_data}
                self.evaluation_results[evaluator_id].append(result)

            def find_evaluation_history(self, evaluator_id: str, _limit: int = 100) -> list[dict[str, object]]:
                results = self.evaluation_results.get(evaluator_id, [])
                return results[-_limit:][::-1]  # 最新のものから返す

            def find_evaluation_improvements(self, _evaluator_id: str, _days: int = 30) -> list[dict[str, object]]:
                return []

        repo = MockEvaluatorRepo()

        # 評価結果の保存
        eval_data = {"quality_score": 85.0, "confidence": 0.92, "metrics": {"readability": 88.0, "engagement": 82.0}}
        repo.save_evaluation_result("eval-001", "ep-001", eval_data)

        # 評価履歴の取得
        history = repo.find_evaluation_history("eval-001")
        assert len(history) == 1
        assert history[0]["episode_id"] == "ep-001"
        assert history[0]["quality_score"] == 85.0

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-LEARNING_EVALUATOR_R")
    def test_learning_evaluator_repository_improvements_signature(self) -> None:
        """改善履歴検索メソッドのシグネチャ確認"""

        class MockEvaluatorRepo(LearningEvaluatorRepository):
            def save_evaluator(self, evaluator: LearningQualityEvaluator) -> None:
                pass

            def find_evaluator_by_id(self, _evaluator_id: str) -> LearningQualityEvaluator | None:
                return None

            def find_evaluator_by_project(self, _project_id: str) -> LearningQualityEvaluator | None:
                return None

            def save_evaluation_result(
                self, evaluator_id: str, episode_id: str, evaluation_data: dict[str, object]
            ) -> None:
                pass

            def find_evaluation_history(self, _evaluator_id: str, _limit: int = 100) -> list[dict[str, object]]:
                return []

            def find_evaluation_improvements(self, evaluator_id: str, days: int = 30) -> list[dict[str, object]]:
                # 模擬的な改善データ
                if evaluator_id == "eval-001" and days == 30:
                    return [
                        {
                            "date": project_now().datetime - timedelta(days=25),
                            "improvement_type": "accuracy",
                            "before": 0.85,
                            "after": 0.88,
                            "description": "学習データ追加による精度向上",
                        },
                        {
                            "date": project_now().datetime - timedelta(days=15),
                            "improvement_type": "confidence",
                            "before": 0.90,
                            "after": 0.93,
                            "description": "評価基準の最適化",
                        },
                    ]
                return []

        repo = MockEvaluatorRepo()

        # 30日間の改善履歴
        improvements = repo.find_evaluation_improvements("eval-001", days=30)
        assert len(improvements) == 2
        assert improvements[0]["improvement_type"] == "accuracy"
        assert improvements[0]["after"] > improvements[0]["before"]


class TestRepositoryImplementationExample:
    """学習リポジトリ実装例のテスト"""

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-IN_MEMORY_LEARNING_R")
    def test_in_memory_learning_repository_implementation(self) -> None:
        """インメモリ学習リポジトリ実装例"""

        class InMemoryQualityLearningRepo(QualityLearningRepository):
            def __init__(self) -> None:
                self.models = {}
                self.learning_history = {}

            def save_model(self, model: QualityLearningModel) -> None:
                self.models[model.model_id] = model

            def find_model_by_id(self, model_id: str) -> QualityLearningModel | None:
                return self.models.get(model_id)

            def find_models_by_project(self, project_id: str) -> list[QualityLearningModel]:
                return [m for m in self.models.values() if m.project_id == project_id]

            def save_learning_history(self, model_id: str, training_data: list[dict], accuracy: float) -> None:
                if model_id not in self.learning_history:
                    self.learning_history[model_id] = []

                history_entry = {
                    "timestamp": project_now().datetime,
                    "training_data_size": len(training_data),
                    "accuracy": accuracy,
                    "training_summary": {
                        "avg_score": sum(d.get("score", 0) for d in training_data) / len(training_data)
                        if training_data
                        else 0,
                        "episode_count": len(training_data),
                    },
                }
                self.learning_history[model_id].append(history_entry)

            def get_learning_history(self, model_id: str) -> list[dict]:
                return self.learning_history.get(model_id, [])

        # リポジトリのテスト
        repo = InMemoryQualityLearningRepo()

        # モデルの作成と保存
        model = QualityLearningModel(
            model_id="model-001",
            project_id="project-1",
            model_type="quality_predictor",
            version="1.0.0",
            created_at=project_now().datetime,
        )

        repo.save_model(model)

        # モデルの取得
        saved_model = repo.find_model_by_id("model-001")
        assert saved_model is not None
        assert saved_model.version == "1.0.0"

        # 学習履歴の保存
        training_data = [{"episode_id": f"ep-{i:03d}", "score": 80 + i} for i in range(10)]
        repo.save_learning_history("model-001", training_data, 0.92)

        # 学習履歴の取得
        history = repo.get_learning_history("model-001")
        assert len(history) == 1
        assert history[0]["accuracy"] == 0.92
        assert history[0]["training_data_size"] == 10

    @pytest.mark.spec("SPEC-LEARNING_REPOSITORIES-COMPLETE_LEARNING_WO")
    def test_complete_learning_workflow_with_repositories(self) -> None:
        """全学習リポジトリを使用した完全なワークフロー"""

        # 各リポジトリの実装
        class MockLearningRepo(QualityLearningRepository):
            def __init__(self) -> None:
                self.models = {}

            def save_model(self, model: QualityLearningModel) -> None:
                self.models[model.model_id] = model

            def find_model_by_id(self, model_id: str) -> QualityLearningModel | None:
                return self.models.get(model_id)

            def find_models_by_project(self, project_id: str) -> list[QualityLearningModel]:
                return [m for m in self.models.values() if m.project_id == project_id]

            def save_learning_history(self, model_id: str, training_data: list[dict], accuracy: float) -> None:
                pass

            def get_learning_history(self, _model_id: str) -> list[dict]:
                return []

        class MockAnalysisRepo(EpisodeAnalysisRepository):
            def __init__(self) -> None:
                self.analysis_data = {}

            def save_episode_analysis(self, episode_id: str, analysis_data: dict[str, object]) -> None:
                self.analysis_data[episode_id] = analysis_data

            def find_episode_analysis(self, episode_id: str) -> dict[str, object] | None:
                return self.analysis_data.get(episode_id)

            def find_project_episodes_analysis(self, project_id: str) -> list[dict[str, object]]:
                return [a for a in self.analysis_data.values() if a.get("project_id") == project_id]

            def find_episodes_by_quality_range(
                self,
                project_id: str,
                metric: QualityMetric,
                min_score: float,
                max_score: float,
            ) -> list[dict[str, object]]:
                result = []
                for analysis in self.analysis_data.values():
                    if analysis.get("project_id") == project_id:
                        score = analysis.get("metrics", {}).get(metric.value.lower(), 0)
                        if min_score <= score <= max_score:
                            result.append(analysis)
                return result

            def find_episodes_with_high_reader_rating(
                self, _project_id: str, _min_rating: float = 4.0
            ) -> list[dict[str, object]]:
                return []

        class MockFeedbackRepo(ReaderFeedbackRepository):
            def __init__(self) -> None:
                self.feedback_data = []

            def save_reader_feedback(self, episode_id: str, feedback_data: dict[str, object]) -> None:
                feedback_data["episode_id"] = episode_id
                self.feedback_data.append(feedback_data)

            def find_feedback_by_episode(self, episode_id: str) -> list[dict[str, object]]:
                return [f for f in self.feedback_data if f["episode_id"] == episode_id]

            def find_feedback_by_project(self, project_id: str) -> list[dict[str, object]]:
                return [f for f in self.feedback_data if f.get("project_id") == project_id]

            def find_feedback_by_date_range(
                self,
                _project_id: str,
                _start_date: datetime,
                _end_date: datetime,
            ) -> list[dict[str, object]]:
                return []

            def calculate_average_rating(self, project_id: str) -> float:
                project_feedback = self.find_feedback_by_project(project_id)
                if not project_feedback:
                    return 0.0
                ratings = [f.get("rating", 0) for f in project_feedback]
                return sum(ratings) / len(ratings)

            def find_trending_episodes(self, _project_id: str, _days: int = 7) -> list[dict[str, object]]:
                return []

        # ワークフローの実行
        learning_repo = MockLearningRepo()
        analysis_repo = MockAnalysisRepo()
        feedback_repo = MockFeedbackRepo()

        # 1. 学習モデルの作成
        model = QualityLearningModel(
            model_id="quality-model-001",
            project_id="novel-project-1",
            model_type="adaptive_quality",
            version="1.0.0",
            created_at=project_now().datetime,
        )

        learning_repo.save_model(model)

        # 2. エピソードの分析
        for i in range(5):
            episode_id = f"episode-{i:03d}"
            analysis = {
                "episode_id": episode_id,
                "project_id": "novel-project-1",
                "analyzed_at": project_now().datetime,
                "metrics": {
                    "readability": 80 + i * 2,
                    "engagement": 85 + i * 1.5,
                    "character_development": 82 + i * 1.8,
                },
            }
            analysis_repo.save_episode_analysis(episode_id, analysis)

        # 3. 読者フィードバックの収集
        for i in range(5):
            episode_id = f"episode-{i:03d}"
            for j in range(3):  # 各エピソードに3つのフィードバック
                feedback = {
                    "project_id": "novel-project-1",
                    "rating": 3.5 + (i * 0.3) + (j * 0.1),
                    "comment": f"エピソード{i}のコメント{j}",
                    "created_at": project_now().datetime,
                }
                feedback_repo.save_reader_feedback(episode_id, feedback)

        # 4. 分析結果の確認
        project_analyses = analysis_repo.find_project_episodes_analysis("novel-project-1")
        assert len(project_analyses) == 5

        # 5. 高品質エピソードの抽出
        high_quality = analysis_repo.find_episodes_by_quality_range(
            "novel-project-1", QualityMetric.READABILITY, 85, 100
        )

        assert len(high_quality) >= 2

        # 6. 平均評価の計算
        avg_rating = feedback_repo.calculate_average_rating("novel-project-1")
        assert avg_rating > 3.5
