"""STEP 13: 文字数最適化サービス

A38執筆プロンプトガイドのSTEP13「文字数最適化」を実装するサービス。
目標文字数に対する最適化を行い、読み応えと簡潔さのバランスを保ちながら
適切な文章長に調整します。
"""

from abc import abstractmethod
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import Any

from noveler.domain.interfaces.writing_step_protocol import BaseWritingStep
from noveler.domain.models.project_model import ProjectModel
from noveler.domain.services.configuration_manager_service import ConfigurationManagerService


class OptimizationType(Enum):
    """最適化タイプ"""
    EXPANSION = "expansion"  # 拡張（文字数を増やす）
    COMPRESSION = "compression"  # 圧縮（文字数を減らす）
    RESTRUCTURING = "restructuring"  # 再構成（構造の最適化）
    BALANCING = "balancing"  # バランス調整


class ContentPriority(Enum):
    """コンテンツ優先度"""
    ESSENTIAL = "essential"  # 必須内容
    IMPORTANT = "important"  # 重要内容
    SUPPORTING = "supporting"  # 支援内容
    DECORATIVE = "decorative"  # 装飾内容
    EXPENDABLE = "expendable"  # 削除可能内容


class LengthAdjustmentStrategy(Enum):
    """文字数調整戦略"""
    DETAIL_ENHANCEMENT = "detail_enhancement"  # 詳細強化
    SCENE_EXPANSION = "scene_expansion"  # シーン拡張
    DIALOGUE_ENRICHMENT = "dialogue_enrichment"  # 対話強化
    DESCRIPTION_ADDITION = "description_addition"  # 描写追加
    REDUNDANCY_REMOVAL = "redundancy_removal"  # 冗長性除去
    CONTENT_CONDENSATION = "content_condensation"  # 内容凝縮
    STRUCTURE_OPTIMIZATION = "structure_optimization"  # 構造最適化


@dataclass
class TextSegment:
    """テキストセグメント"""
    segment_id: str
    content: str
    segment_type: str  # paragraph, dialogue, description, etc.
    character_count: int
    word_count: int
    priority: ContentPriority
    function: str  # plot_advancement, character_development, etc.
    emotional_impact: float  # 感情的インパクト (0-1)
    narrative_importance: float  # ナラティブ重要度 (0-1)
    redundancy_score: float  # 冗長性スコア (0-1)
    expansion_potential: float  # 拡張可能性 (0-1)
    compression_potential: float  # 圧縮可能性 (0-1)
    related_segments: list[str]  # 関連セグメント


@dataclass
class OptimizationAction:
    """最適化アクション"""
    action_id: str
    action_type: OptimizationType
    strategy: LengthAdjustmentStrategy
    target_segment_id: str
    description: str
    original_content: str
    optimized_content: str
    character_change: int  # 文字数変化
    impact_assessment: str
    risk_level: float  # リスク レベル (0-1)
    confidence: float  # 信頼度 (0-1)
    execution_order: int  # 実行順序


@dataclass
class LengthAnalysis:
    """文字数分析"""
    current_length: int
    target_length: int
    variance_from_target: int
    variance_percentage: float
    length_distribution: dict[str, int]  # セグメントタイプ別文字数
    density_analysis: dict[str, float]  # 密度分析
    pacing_analysis: dict[str, Any]  # ペース分析
    balance_assessment: dict[str, float]  # バランス評価


@dataclass
class OptimizationPlan:
    """最適化計画"""
    plan_id: str
    optimization_type: OptimizationType
    target_adjustment: int  # 目標調整文字数
    planned_actions: list[OptimizationAction]
    execution_phases: list[str]  # 実行フェーズ
    risk_mitigation: list[str]  # リスク軽減策
    quality_checkpoints: list[str]  # 品質チェックポイント
    fallback_strategies: list[str]  # フォールバック戦略


@dataclass
class OptimizationResult:
    """最適化結果"""
    result_id: str
    executed_actions: list[OptimizationAction]
    original_length: int
    optimized_length: int
    length_change: int
    quality_metrics: dict[str, float]
    readability_impact: float  # 読みやすさへの影響
    narrative_integrity: float  # ナラティブ完全性
    optimization_effectiveness: float  # 最適化効果


@dataclass
class TextLengthOptimizationReport:
    """文字数最適化レポート"""
    report_id: str
    episode_number: int
    optimization_timestamp: datetime
    length_analysis: LengthAnalysis
    text_segments: list[TextSegment]
    optimization_plan: OptimizationPlan
    optimization_result: OptimizationResult
    final_text: str
    optimization_score: float  # 最適化スコア (0-1)
    target_achievement: float  # 目標達成度 (0-1)
    quality_preservation: float  # 品質保持度 (0-1)
    optimization_summary: str
    recommendations: list[str]
    optimization_metadata: dict[str, Any]


@dataclass
class TextLengthOptimizerConfig:
    """文字数最適化設定"""
    target_length: int = 4000  # 目標文字数
    length_tolerance: float = 0.1  # 許容誤差（割合）
    min_length_threshold: int = 3000  # 最小文字数閾値
    max_length_threshold: int = 5000  # 最大文字数閾値
    optimization_aggressiveness: float = 0.5  # 最適化積極性 (0-1)
    quality_weight: float = 0.7  # 品質重み
    length_weight: float = 0.3  # 文字数重み
    enable_expansion: bool = True
    enable_compression: bool = True
    enable_restructuring: bool = True
    preserve_dialogue: bool = True  # 対話保持
    preserve_key_scenes: bool = True  # 重要シーン保持
    max_iterations: int = 3  # 最大反復回数


class TextLengthOptimizerService(BaseWritingStep):
    """STEP 13: 文字数最適化サービス

    目標文字数に対する最適化を行い、読み応えと簡潔さのバランスを保ちながら
    適切な文章長に調整するサービス。
    A38ガイドのSTEP13「文字数最適化」を実装。
    """

    def __init__(
        self,
        config_manager: ConfigurationManagerService | None = None,
        path_service: Any | None = None,
        file_system_service: Any | None = None
    ) -> None:
        super().__init__()
        self._config_manager = config_manager
        self._path_service = path_service
        self._file_system = file_system_service
        self._optimizer_config = TextLengthOptimizerConfig()

        # 最適化戦略テンプレート
        self._optimization_strategies = self._initialize_optimization_strategies()
        self._segment_analyzers = self._initialize_segment_analyzers()

    @abstractmethod
    def get_step_name(self) -> str:
        """ステップ名を取得"""
        return "文字数最適化"

    @abstractmethod
    def get_step_description(self) -> str:
        """ステップの説明を取得"""
        return "目標文字数に対する最適化を行い、読み応えと簡潔さのバランスを保ちながら適切な文章長に調整します"

    @abstractmethod
    def execute_step(self, context: dict[str, Any]) -> dict[str, Any]:
        """STEP 13: 文字数最適化の実行

        Args:
            context: 実行コンテキスト

        Returns:
            文字数最適化結果を含むコンテキスト
        """
        try:
            episode_number = context.get("episode_number")
            project = context.get("project")

            if not episode_number or not project:
                msg = "episode_numberまたはprojectが指定されていません"
                raise ValueError(msg)

            # 原稿テキストの取得
            manuscript_text = self._get_manuscript_text(context)
            if not manuscript_text:
                msg = "最適化対象の原稿テキストが見つかりません"
                raise ValueError(msg)

            # 文字数最適化の実行
            optimization_report = self._execute_text_length_optimization(
                episode_number=episode_number,
                project=project,
                manuscript_text=manuscript_text,
                context=context
            )

            # 結果をコンテキストに追加
            context["text_length_optimization"] = optimization_report
            context["optimized_text"] = optimization_report.final_text
            context["text_length_optimization_completed"] = True

            return context

        except Exception as e:
            context["text_length_optimization_error"] = str(e)
            raise

    def _get_manuscript_text(self, context: dict[str, Any]) -> str:
        """原稿テキストの取得"""
        # 様々なソースから原稿テキストを取得を試行
        manuscript_text = context.get("manuscript_text")
        if manuscript_text:
            return manuscript_text

        # 生成された原稿から取得
        manuscript_data = context.get("manuscript_generator", {})
        if manuscript_data and isinstance(manuscript_data, dict):
            return manuscript_data.get("generated_text", "")

        # 初稿データから取得
        draft_data = context.get("manuscript_draft", {})
        if draft_data and isinstance(draft_data, dict):
            return draft_data.get("content", "")

        return ""

    def _execute_text_length_optimization(
        self,
        episode_number: int,
        project: ProjectModel,
        manuscript_text: str,
        context: dict[str, Any]
    ) -> TextLengthOptimizationReport:
        """文字数最適化の実行"""

        # 目標文字数の決定
        target_length = self._determine_target_length(context)
        self._optimizer_config.target_length = target_length

        # テキスト分析
        length_analysis = self._analyze_text_length(manuscript_text, target_length)

        # テキストセグメンテーション
        text_segments = self._segment_text(manuscript_text)

        # セグメント分析
        analyzed_segments = self._analyze_segments(text_segments, context)

        # 最適化計画の作成
        optimization_plan = self._create_optimization_plan(
            length_analysis, analyzed_segments, context
        )

        # 最適化の実行
        optimization_result = self._execute_optimization(
            manuscript_text, optimization_plan, analyzed_segments
        )

        # レポート生成
        return self._generate_optimization_report(
            episode_number=episode_number,
            length_analysis=length_analysis,
            text_segments=analyzed_segments,
            optimization_plan=optimization_plan,
            optimization_result=optimization_result
        )

    def _determine_target_length(self, context: dict[str, Any]) -> int:
        """目標文字数の決定"""
        # コンテキストから目標文字数を取得
        context_target = context.get("target_word_count")
        if context_target:
            return int(context_target)

        # プロジェクト設定から取得
        project_settings = context.get("project", {})
        if hasattr(project_settings, "default_episode_length"):
            return project_settings.default_episode_length

        # デフォルト値を使用
        return self._optimizer_config.target_length

    def _analyze_text_length(self, text: str, target_length: int) -> LengthAnalysis:
        """テキスト文字数分析"""
        current_length = len(text)
        variance = current_length - target_length
        variance_percentage = (variance / target_length) * 100 if target_length > 0 else 0

        # セグメントタイプ別文字数分布の分析
        length_distribution = self._analyze_length_distribution(text)

        # 密度分析
        density_analysis = self._analyze_text_density(text)

        # ペース分析
        pacing_analysis = self._analyze_text_pacing(text)

        # バランス評価
        balance_assessment = self._assess_text_balance(text)

        return LengthAnalysis(
            current_length=current_length,
            target_length=target_length,
            variance_from_target=variance,
            variance_percentage=variance_percentage,
            length_distribution=length_distribution,
            density_analysis=density_analysis,
            pacing_analysis=pacing_analysis,
            balance_assessment=balance_assessment
        )

    def _segment_text(self, text: str) -> list[TextSegment]:
        """テキストのセグメンテーション"""
        segments = []

        # 段落単位でセグメント分割
        paragraphs = text.split("\n\n")

        for i, paragraph in enumerate(paragraphs):
            if paragraph.strip():  # 空でない段落のみ
                segment = TextSegment(
                    segment_id=f"segment_{i}",
                    content=paragraph.strip(),
                    segment_type=self._identify_segment_type(paragraph),
                    character_count=len(paragraph.strip()),
                    word_count=len(paragraph.strip().split()),
                    priority=ContentPriority.SUPPORTING,  # 初期値
                    function="unknown",  # 初期値
                    emotional_impact=0.5,  # 初期値
                    narrative_importance=0.5,  # 初期値
                    redundancy_score=0.0,  # 初期値
                    expansion_potential=0.5,  # 初期値
                    compression_potential=0.5,  # 初期値
                    related_segments=[]  # 初期値
                )
                segments.append(segment)

        return segments

    def _analyze_segments(
        self,
        segments: list[TextSegment],
        context: dict[str, Any]
    ) -> list[TextSegment]:
        """セグメントの詳細分析"""
        analyzed_segments = []

        for segment in segments:
            # 優先度の分析
            priority = self._analyze_segment_priority(segment, context)

            # 機能の分析
            function = self._analyze_segment_function(segment, context)

            # 感情的インパクトの分析
            emotional_impact = self._analyze_emotional_impact(segment)

            # ナラティブ重要度の分析
            narrative_importance = self._analyze_narrative_importance(segment, context)

            # 冗長性の分析
            redundancy_score = self._analyze_redundancy(segment, segments)

            # 拡張可能性の分析
            expansion_potential = self._analyze_expansion_potential(segment)

            # 圧縮可能性の分析
            compression_potential = self._analyze_compression_potential(segment)

            # 関連セグメントの特定
            related_segments = self._find_related_segments(segment, segments)

            # 分析結果で更新
            segment.priority = priority
            segment.function = function
            segment.emotional_impact = emotional_impact
            segment.narrative_importance = narrative_importance
            segment.redundancy_score = redundancy_score
            segment.expansion_potential = expansion_potential
            segment.compression_potential = compression_potential
            segment.related_segments = related_segments

            analyzed_segments.append(segment)

        return analyzed_segments

    def _create_optimization_plan(
        self,
        length_analysis: LengthAnalysis,
        segments: list[TextSegment],
        context: dict[str, Any]
    ) -> OptimizationPlan:
        """最適化計画の作成"""

        variance = length_analysis.variance_from_target
        optimization_type = self._determine_optimization_type(variance)

        # 最適化アクションの計画
        planned_actions = self._plan_optimization_actions(
            optimization_type, variance, segments, context
        )

        # 実行フェーズの設計
        execution_phases = self._design_execution_phases(planned_actions)

        # リスク軽減策の設計
        risk_mitigation = self._design_risk_mitigation(planned_actions)

        # 品質チェックポイントの設定
        quality_checkpoints = self._set_quality_checkpoints(planned_actions)

        # フォールバック戦略の準備
        fallback_strategies = self._prepare_fallback_strategies(optimization_type, variance)

        return OptimizationPlan(
            plan_id=f"opt_plan_{datetime.now(tz=datetime.timezone.utc).strftime('%Y%m%d_%H%M%S')}",
            optimization_type=optimization_type,
            target_adjustment=-variance,  # 目標調整量
            planned_actions=planned_actions,
            execution_phases=execution_phases,
            risk_mitigation=risk_mitigation,
            quality_checkpoints=quality_checkpoints,
            fallback_strategies=fallback_strategies
        )

    def _execute_optimization(
        self,
        original_text: str,
        optimization_plan: OptimizationPlan,
        segments: list[TextSegment]
    ) -> OptimizationResult:
        """最適化の実行"""

        current_text = original_text
        executed_actions = []

        # 計画されたアクションを順次実行
        for action in optimization_plan.planned_actions:
            try:
                # アクションの実行
                optimized_segment = self._execute_action(action, current_text, segments)

                # テキストの更新
                current_text = self._apply_segment_change(
                    current_text, action.target_segment_id, optimized_segment, segments
                )

                # 実行済みアクションに追加
                action.optimized_content = optimized_segment
                executed_actions.append(action)

                # 品質チェック
                if not self._quality_check_passed(current_text, action):
                    # 品質チェック失敗時はフォールバック
                    current_text = self._apply_fallback(current_text, action, segments)

            except Exception as e:
                # アクション実行失敗時の処理
                self._handle_action_failure(action, str(e))
                continue

        # 結果の計算
        original_length = len(original_text)
        optimized_length = len(current_text)
        length_change = optimized_length - original_length

        # 品質メトリクスの計算
        quality_metrics = self._calculate_quality_metrics(
            original_text, current_text, executed_actions
        )

        return OptimizationResult(
            result_id=f"opt_result_{datetime.now(tz=datetime.timezone.utc).strftime('%Y%m%d_%H%M%S')}",
            executed_actions=executed_actions,
            original_length=original_length,
            optimized_length=optimized_length,
            length_change=length_change,
            quality_metrics=quality_metrics,
            readability_impact=quality_metrics.get("readability_impact", 0.0),
            narrative_integrity=quality_metrics.get("narrative_integrity", 1.0),
            optimization_effectiveness=self._calculate_optimization_effectiveness(
                length_change, optimization_plan.target_adjustment
            )
        )

    def _generate_optimization_report(
        self,
        episode_number: int,
        length_analysis: LengthAnalysis,
        text_segments: list[TextSegment],
        optimization_plan: OptimizationPlan,
        optimization_result: OptimizationResult
    ) -> TextLengthOptimizationReport:
        """最適化レポートの生成"""

        # 最適化スコアの計算
        optimization_score = self._calculate_optimization_score(
            optimization_result, optimization_plan
        )

        # 目標達成度の計算
        target_achievement = self._calculate_target_achievement(
            optimization_result, length_analysis
        )

        # 品質保持度の計算
        quality_preservation = optimization_result.narrative_integrity

        # 最終テキストの構築
        final_text = self._build_final_text(text_segments, optimization_result)

        # サマリーの生成
        optimization_summary = self._generate_optimization_summary(
            length_analysis, optimization_result, optimization_score
        )

        # 推奨事項の生成
        recommendations = self._generate_recommendations(
            optimization_result, target_achievement, quality_preservation
        )

        return TextLengthOptimizationReport(
            report_id=f"text_opt_{episode_number}_{datetime.now(tz=datetime.timezone.utc).strftime('%Y%m%d_%H%M%S')}",
            episode_number=episode_number,
            optimization_timestamp=datetime.now(tz=datetime.timezone.utc),
            length_analysis=length_analysis,
            text_segments=text_segments,
            optimization_plan=optimization_plan,
            optimization_result=optimization_result,
            final_text=final_text,
            optimization_score=optimization_score,
            target_achievement=target_achievement,
            quality_preservation=quality_preservation,
            optimization_summary=optimization_summary,
            recommendations=recommendations,
            optimization_metadata={
                "config": self._optimizer_config.__dict__,
                "execution_time": datetime.now(tz=datetime.timezone.utc),
                "total_segments": len(text_segments),
                "executed_actions": len(optimization_result.executed_actions)
            }
        )

    # ヘルパーメソッドの実装

    def _initialize_optimization_strategies(self) -> dict[str, Any]:
        """最適化戦略の初期化"""
        return {
            OptimizationType.EXPANSION: {
                LengthAdjustmentStrategy.DETAIL_ENHANCEMENT: {
                    "description": "描写の詳細化による拡張",
                    "applicability": 0.8,
                    "risk_level": 0.2
                },
                LengthAdjustmentStrategy.SCENE_EXPANSION: {
                    "description": "シーンの拡張",
                    "applicability": 0.7,
                    "risk_level": 0.3
                },
                LengthAdjustmentStrategy.DIALOGUE_ENRICHMENT: {
                    "description": "対話の充実化",
                    "applicability": 0.9,
                    "risk_level": 0.1
                }
            },
            OptimizationType.COMPRESSION: {
                LengthAdjustmentStrategy.REDUNDANCY_REMOVAL: {
                    "description": "冗長部分の削除",
                    "applicability": 0.9,
                    "risk_level": 0.2
                },
                LengthAdjustmentStrategy.CONTENT_CONDENSATION: {
                    "description": "内容の凝縮",
                    "applicability": 0.8,
                    "risk_level": 0.3
                }
            }
        }

    def _initialize_segment_analyzers(self) -> dict[str, Any]:
        """セグメント分析器の初期化"""
        return {
            "dialogue_detector": {
                "patterns": ["「", "」", "『", "』"],
                "weight": 1.0
            },
            "description_detector": {
                "patterns": ["様子", "風景", "表情", "雰囲気"],
                "weight": 0.8
            },
            "action_detector": {
                "patterns": ["した", "する", "歩く", "走る"],
                "weight": 0.9
            }
        }

    def _identify_segment_type(self, paragraph: str) -> str:
        """セグメントタイプの識別"""
        # 対話の検出
        if "「" in paragraph and "」" in paragraph:
            return "dialogue"

        # 描写の検出
        if any(word in paragraph for word in ["様子", "風景", "表情", "雰囲気", "景色"]):
            return "description"

        # アクションの検出
        if any(word in paragraph for word in ["した", "する", "歩", "走", "移動"]):
            return "action"

        return "narrative"

    def _determine_optimization_type(self, variance: int) -> OptimizationType:
        """最適化タイプの決定"""
        tolerance = self._optimizer_config.target_length * self._optimizer_config.length_tolerance

        if variance > tolerance:
            return OptimizationType.COMPRESSION
        if variance < -tolerance:
            return OptimizationType.EXPANSION
        return OptimizationType.BALANCING

    def _plan_optimization_actions(
        self,
        optimization_type: OptimizationType,
        variance: int,
        segments: list[TextSegment],
        context: dict[str, Any]
    ) -> list[OptimizationAction]:
        """最適化アクションの計画"""
        actions = []

        if optimization_type == OptimizationType.EXPANSION:
            actions = self._plan_expansion_actions(variance, segments)
        elif optimization_type == OptimizationType.COMPRESSION:
            actions = self._plan_compression_actions(variance, segments)
        elif optimization_type == OptimizationType.BALANCING:
            actions = self._plan_balancing_actions(segments)

        return actions

    def _plan_expansion_actions(
        self,
        needed_expansion: int,
        segments: list[TextSegment]
    ) -> list[OptimizationAction]:
        """拡張アクションの計画"""
        actions = []
        remaining_expansion = abs(needed_expansion)

        # 拡張可能性の高いセグメントを優先
        expandable_segments = sorted(
            segments,
            key=lambda s: s.expansion_potential,
            reverse=True
        )

        order = 0
        for segment in expandable_segments:
            if remaining_expansion <= 0:
                break

            if segment.expansion_potential > 0.5:  # 拡張可能性が高い
                # 拡張量を計算
                expansion_amount = min(
                    remaining_expansion,
                    int(segment.character_count * 0.3)  # 最大30%拡張
                )

                action = OptimizationAction(
                    action_id=f"expand_{segment.segment_id}",
                    action_type=OptimizationType.EXPANSION,
                    strategy=LengthAdjustmentStrategy.DETAIL_ENHANCEMENT,
                    target_segment_id=segment.segment_id,
                    description=f"セグメント「{segment.segment_id}」の詳細化拡張",
                    original_content=segment.content,
                    optimized_content="",  # 実行時に設定
                    character_change=expansion_amount,
                    impact_assessment="品質を保ちながら文字数を増加",
                    risk_level=0.2,
                    confidence=0.8,
                    execution_order=order
                )
                actions.append(action)
                remaining_expansion -= expansion_amount
                order += 1

        return actions

    def _plan_compression_actions(
        self,
        needed_compression: int,
        segments: list[TextSegment]
    ) -> list[OptimizationAction]:
        """圧縮アクションの計画"""
        actions = []
        remaining_compression = abs(needed_compression)

        # 冗長性の高いセグメントを優先
        compressible_segments = sorted(
            segments,
            key=lambda s: s.redundancy_score + s.compression_potential,
            reverse=True
        )

        order = 0
        for segment in compressible_segments:
            if remaining_compression <= 0:
                break

            if segment.compression_potential > 0.3:  # 圧縮可能
                # 圧縮量を計算
                compression_amount = min(
                    remaining_compression,
                    int(segment.character_count * 0.2)  # 最大20%圧縮
                )

                action = OptimizationAction(
                    action_id=f"compress_{segment.segment_id}",
                    action_type=OptimizationType.COMPRESSION,
                    strategy=LengthAdjustmentStrategy.REDUNDANCY_REMOVAL,
                    target_segment_id=segment.segment_id,
                    description=f"セグメント「{segment.segment_id}」の冗長性除去",
                    original_content=segment.content,
                    optimized_content="",  # 実行時に設定
                    character_change=-compression_amount,
                    impact_assessment="冗長性を除去して簡潔化",
                    risk_level=0.3,
                    confidence=0.7,
                    execution_order=order
                )
                actions.append(action)
                remaining_compression -= compression_amount
                order += 1

        return actions

    def _plan_balancing_actions(
        self,
        segments: list[TextSegment]
    ) -> list[OptimizationAction]:
        """バランシングアクションの計画"""
        # バランス調整のための微細な最適化
        actions = []

        # 構造最適化アクション
        for i, segment in enumerate(segments):
            if segment.priority == ContentPriority.SUPPORTING and segment.redundancy_score > 0.5:
                action = OptimizationAction(
                    action_id=f"balance_{segment.segment_id}",
                    action_type=OptimizationType.RESTRUCTURING,
                    strategy=LengthAdjustmentStrategy.STRUCTURE_OPTIMIZATION,
                    target_segment_id=segment.segment_id,
                    description=f"セグメント「{segment.segment_id}」の構造最適化",
                    original_content=segment.content,
                    optimized_content="",
                    character_change=0,
                    impact_assessment="構造を最適化して品質向上",
                    risk_level=0.1,
                    confidence=0.9,
                    execution_order=i
                )
                actions.append(action)

        return actions

    # 分析メソッドのスタブ実装

    def _analyze_length_distribution(self, text: str) -> dict[str, int]:
        """文字数分布の分析"""
        return {"paragraph": 80, "dialogue": 20}

    def _analyze_text_density(self, text: str) -> dict[str, float]:
        """テキスト密度の分析"""
        return {"information_density": 0.8, "emotional_density": 0.6}

    def _analyze_text_pacing(self, text: str) -> dict[str, Any]:
        """テキストペースの分析"""
        return {"pacing_score": 0.7, "rhythm_consistency": 0.8}

    def _assess_text_balance(self, text: str) -> dict[str, float]:
        """テキストバランスの評価"""
        return {"narrative_balance": 0.8, "descriptive_balance": 0.7}

    def _analyze_segment_priority(self, segment: TextSegment, context: dict[str, Any]) -> ContentPriority:
        """セグメント優先度の分析"""
        if segment.segment_type == "dialogue":
            return ContentPriority.IMPORTANT
        if segment.narrative_importance > 0.8:
            return ContentPriority.ESSENTIAL
        return ContentPriority.SUPPORTING

    def _analyze_segment_function(self, segment: TextSegment, context: dict[str, Any]) -> str:
        """セグメント機能の分析"""
        if segment.segment_type == "dialogue":
            return "character_development"
        if segment.segment_type == "description":
            return "atmosphere_building"
        return "plot_advancement"

    def _analyze_emotional_impact(self, segment: TextSegment) -> float:
        """感情的インパクトの分析"""
        emotional_words = ["悲しい", "嬉しい", "驚く", "怒り", "愛"]
        count = sum(1 for word in emotional_words if word in segment.content)
        return min(count / 5.0, 1.0)

    def _analyze_narrative_importance(self, segment: TextSegment, context: dict[str, Any]) -> float:
        """ナラティブ重要度の分析"""
        return 0.7 if segment.segment_type in ["dialogue", "action"] else 0.5

    def _analyze_redundancy(self, segment: TextSegment, all_segments: list[TextSegment]) -> float:
        """冗長性の分析"""
        # 同様の内容の重複チェック（簡易実装）
        similar_count = 0
        for other_segment in all_segments:
            if other_segment.segment_id != segment.segment_id:
                # 内容の類似性をチェック（簡易実装）
                common_words = set(segment.content.split()) & set(other_segment.content.split())
                if len(common_words) > 3:
                    similar_count += 1

        return min(similar_count / len(all_segments), 1.0)

    def _analyze_expansion_potential(self, segment: TextSegment) -> float:
        """拡張可能性の分析"""
        if segment.segment_type == "description":
            return 0.8
        if segment.segment_type == "dialogue":
            return 0.6
        return 0.5

    def _analyze_compression_potential(self, segment: TextSegment) -> float:
        """圧縮可能性の分析"""
        if len(segment.content) > 200:  # 長いセグメントは圧縮可能性が高い
            return 0.7
        if segment.redundancy_score > 0.5:
            return 0.8
        return 0.3

    def _find_related_segments(self, segment: TextSegment, all_segments: list[TextSegment]) -> list[str]:
        """関連セグメントの発見"""
        related = []
        for other_segment in all_segments:
            if other_segment.segment_id != segment.segment_id:
                # 内容の関連性をチェック（簡易実装）
                if segment.segment_type == other_segment.segment_type:
                    related.append(other_segment.segment_id)
        return related[:3]  # 最大3個まで

    # 実行関連メソッドのスタブ実装

    def _design_execution_phases(self, actions: list[OptimizationAction]) -> list[str]:
        """実行フェーズの設計"""
        if not actions:
            return ["no_action_required"]

        phases = []
        if any(action.action_type == OptimizationType.COMPRESSION for action in actions):
            phases.append("compression_phase")
        if any(action.action_type == OptimizationType.EXPANSION for action in actions):
            phases.append("expansion_phase")
        if any(action.action_type == OptimizationType.RESTRUCTURING for action in actions):
            phases.append("restructuring_phase")

        return phases if phases else ["optimization_phase"]

    def _design_risk_mitigation(self, actions: list[OptimizationAction]) -> list[str]:
        """リスク軽減策の設計"""
        return [
            "品質チェックポイントでの検証",
            "段階的な最適化実行",
            "重要セグメントの保護"
        ]

    def _set_quality_checkpoints(self, actions: list[OptimizationAction]) -> list[str]:
        """品質チェックポイントの設定"""
        return [
            "アクション実行前の品質ベースライン確立",
            "各アクション実行後の品質確認",
            "最終結果の総合品質評価"
        ]

    def _prepare_fallback_strategies(self, optimization_type: OptimizationType, variance: int) -> list[str]:
        """フォールバック戦略の準備"""
        strategies = ["元の状態への復元"]

        if optimization_type == OptimizationType.COMPRESSION:
            strategies.append("軽微な圧縮への調整")
        elif optimization_type == OptimizationType.EXPANSION:
            strategies.append("必要最小限の拡張への調整")

        return strategies

    def _execute_action(
        self,
        action: OptimizationAction,
        current_text: str,
        segments: list[TextSegment]
    ) -> str:
        """個別アクションの実行"""

        # 対象セグメントを取得
        target_segment = next(
            (s for s in segments if s.segment_id == action.target_segment_id),
            None
        )

        if not target_segment:
            return action.original_content

        # 戦略に応じた最適化を実行
        if action.strategy == LengthAdjustmentStrategy.DETAIL_ENHANCEMENT:
            return self._enhance_details(target_segment.content)
        if action.strategy == LengthAdjustmentStrategy.REDUNDANCY_REMOVAL:
            return self._remove_redundancy(target_segment.content)
        if action.strategy == LengthAdjustmentStrategy.STRUCTURE_OPTIMIZATION:
            return self._optimize_structure(target_segment.content)
        return target_segment.content

    def _enhance_details(self, content: str) -> str:
        """詳細の強化"""
        # 簡易実装：形容詞や副詞を追加
        enhanced = content
        if "歩いた" in content:
            enhanced = content.replace("歩いた", "ゆっくりと歩いた")
        if "見た" in content:
            enhanced = enhanced.replace("見た", "じっと見つめた")
        return enhanced

    def _remove_redundancy(self, content: str) -> str:
        """冗長性の除去"""
        # 簡易実装：重複表現の削除
        compressed = content
        # 重複する副詞の削除
        compressed = compressed.replace("とても非常に", "非常に")
        return compressed.replace("本当にとても", "とても")

    def _optimize_structure(self, content: str) -> str:
        """構造の最適化"""
        # 簡易実装：文構造の整理
        return content.strip()

    def _apply_segment_change(
        self,
        current_text: str,
        segment_id: str,
        new_content: str,
        segments: list[TextSegment]
    ) -> str:
        """セグメント変更の適用"""

        target_segment = next(
            (s for s in segments if s.segment_id == segment_id),
            None
        )

        if target_segment:
            # 元のコンテンツを新しいコンテンツに置き換え
            updated_text = current_text.replace(target_segment.content, new_content)
            # セグメント情報も更新
            target_segment.content = new_content
            target_segment.character_count = len(new_content)
            return updated_text

        return current_text

    def _quality_check_passed(self, current_text: str, action: OptimizationAction) -> bool:
        """品質チェック"""
        # 簡易品質チェック
        return len(current_text.strip()) > 0 and action.risk_level < 0.8

    def _apply_fallback(
        self,
        current_text: str,
        failed_action: OptimizationAction,
        segments: list[TextSegment]
    ) -> str:
        """フォールバックの適用"""
        # 元の内容に戻す
        return self._apply_segment_change(
            current_text,
            failed_action.target_segment_id,
            failed_action.original_content,
            segments
        )

    def _handle_action_failure(self, action: OptimizationAction, error: str) -> None:
        """アクション失敗の処理"""
        # ログ記録など（実装は省略）

    def _calculate_quality_metrics(
        self,
        original_text: str,
        optimized_text: str,
        actions: list[OptimizationAction]
    ) -> dict[str, float]:
        """品質メトリクスの計算"""
        return {
            "readability_impact": 0.9,
            "narrative_integrity": 0.95,
            "coherence_score": 0.9,
            "style_consistency": 0.85
        }

    def _calculate_optimization_effectiveness(self, actual_change: int, target_change: int) -> float:
        """最適化効果の計算"""
        if target_change == 0:
            return 1.0

        effectiveness = 1.0 - abs(actual_change - target_change) / abs(target_change)
        return max(0.0, effectiveness)

    def _calculate_optimization_score(
        self,
        result: OptimizationResult,
        plan: OptimizationPlan
    ) -> float:
        """最適化スコアの計算"""

        # 効果スコア (0-0.4)
        effectiveness_score = result.optimization_effectiveness * 0.4

        # 品質スコア (0-0.4)
        quality_score = result.narrative_integrity * 0.4

        # 実行成功度 (0-0.2)
        execution_score = len(result.executed_actions) / len(plan.planned_actions) * 0.2 if plan.planned_actions else 0.2

        return effectiveness_score + quality_score + execution_score

    def _calculate_target_achievement(
        self,
        result: OptimizationResult,
        analysis: LengthAnalysis
    ) -> float:
        """目標達成度の計算"""
        final_variance = abs(result.optimized_length - analysis.target_length)
        tolerance = analysis.target_length * self._optimizer_config.length_tolerance

        if final_variance <= tolerance:
            return 1.0
        # 許容範囲を超えた分のペナルティ
        penalty = (final_variance - tolerance) / analysis.target_length
        return max(0.0, 1.0 - penalty)

    def _build_final_text(
        self,
        segments: list[TextSegment],
        result: OptimizationResult
    ) -> str:
        """最終テキストの構築"""
        # セグメントを結合して最終テキストを生成
        return "\n\n".join(segment.content for segment in segments)

    def _generate_optimization_summary(
        self,
        analysis: LengthAnalysis,
        result: OptimizationResult,
        score: float
    ) -> str:
        """最適化サマリーの生成"""

        change_direction = "増加" if result.length_change > 0 else "減少" if result.length_change < 0 else "変化なし"

        summary_parts = [
            f"文字数を{abs(result.length_change)}文字{change_direction}。",
            f"目標{analysis.target_length}文字に対して{result.optimized_length}文字。"
        ]

        if score >= 0.8:
            summary_parts.append("高品質な最適化が完了しました。")
        elif score >= 0.6:
            summary_parts.append("良好な最適化結果です。")
        else:
            summary_parts.append("最適化に課題があります。")

        return " ".join(summary_parts)

    def _generate_recommendations(
        self,
        result: OptimizationResult,
        target_achievement: float,
        quality_preservation: float
    ) -> list[str]:
        """推奨事項の生成"""
        recommendations = []

        if target_achievement < 0.8:
            recommendations.append("📏 目標文字数により近づける追加調整を検討してください")

        if quality_preservation < 0.8:
            recommendations.append("📝 品質向上のための見直しを推奨します")

        if result.optimization_effectiveness < 0.7:
            recommendations.append("🔄 最適化手法の見直しを検討してください")

        if not recommendations:
            recommendations.append("✅ 最適化は良好に完了しました")

        return recommendations
