from skills.plan_mode_aegis_v25_development import AEGISv25DataCollector

# 数学形式証明データの収集
data_collector = AEGISv25DataCollector()
datasets = {
    "minif2f": data_collector.collect_minif2f_dataset(),
    "lean_workbook": data_collector.collect_lean_workbook(),
    "math_competitions": data_collector.collect_math_competition_problems(),
    "arxiv_biorxiv": data_collector.collect_arxiv_biorxiv_papers()
}

print(f"Collected {sum(len(v) for v in datasets.values())} training samples")

from skills.plan_mode_aegis_v25_development import FormalProofEnvironment

# Lean4/Isabelle統合環境構築
proof_env = FormalProofEnvironment()
environments = {
    "lean4": proof_env.setup_lean4_environment(),
    "isabelle": proof_env.setup_isabelle_environment(),
    "integrated_pipeline": proof_env.create_integrated_pipeline()
}

print("Formal proof environments configured successfully")

from skills.plan_mode_aegis_v25_development import GRPOTrainingPipeline

# 証明生成特化GRPO訓練
grpo_pipeline = GRPOTrainingPipeline()
config = {
    "reward_functions": {
        "mathematical_correctness": grpo_pipeline.create_math_correctness_reward(),
        "proof_completeness": grpo_pipeline.create_proof_completeness_reward(),
        "reasoning_coherence": grpo_pipeline.create_reasoning_coherence_reward(),
        "scientific_novelty": grpo_pipeline.create_novelty_reward()
    },
    "spectral_regularization": True,
    "diversity_preservation": True
}

trained_model = grpo_pipeline.train_aegis_v25_model(config)
print("GRPO training pipeline completed")

from skills.plan_mode_aegis_v25_development import MCPA2AAgentSystem

# 多様なエージェント開発
agent_system = MCPA2AAgentSystem()
agents = {
    "math_reasoning": agent_system.create_mathematical_reasoning_agent(),
    "desktop_assistant": agent_system.create_desktop_assistant_agent(),
    "coding_assistant": agent_system.create_coding_assistant_agent(),
    "business_ai": agent_system.create_business_ai_agent()
}

print(f"Developed {len(agents)} specialized MCP/A2A agents")

from skills.plan_mode_aegis_v25_development import AEGISv25ProgressMonitor

# 開発進捗監視
monitor = AEGISv25ProgressMonitor()
progress_report = monitor.generate_progress_report()
quality_metrics = monitor.assess_quality_metrics()

print(f"Development Progress: {progress_report['overall_completion']:.1f}%")
print(f"Quality Score: {quality_metrics['overall_quality']:.2f}")

class MiniF2FCollector:
    def collect_minif2f_dataset(self):
        """Formal-to-Informal Mathematics Benchmark収集"""
        import requests
        from pathlib import Path

        # miniF2Fリポジトリからデータ取得
        url = "https://raw.githubusercontent.com/facebookresearch/miniF2F/main/minif2f.jsonl"
        response = requests.get(url)

        problems = []
        for line in response.text.strip().split('\n'):
            problem = json.loads(line)
            structured_problem = {
                "id": problem["id"],
                "informal_statement": problem["informal_stmt"],
                "formal_statement": problem["formal_stmt"],
                "informal_proof": problem.get("informal_proof", ""),
                "formal_proof": problem.get("formal_proof", ""),
                "domain": self.classify_math_domain(problem),
                "difficulty": self.assess_difficulty(problem)
            }
            problems.append(structured_problem)

        return problems

class LeanWorkbookIntegrator:
    def integrate_lean_workbook(self):
        """Lean数学ライブラリのワークブック統合"""
        # Leanコミュニティの数学ワークブック収集
        workbooks = [
            "mil_lean4_lib",  # Mathematics in Lean
            "lean4_metaprogramming",  # メタプログラミング
            "lean4_tactics",  # 証明戦術
            "lean4_mathematical_structures"  # 数学的構造
        ]

        integrated_content = []
        for workbook in workbooks:
            content = self.extract_lean_workbook_content(workbook)
            structured_content = self.structure_workbook_content(content)
            integrated_content.extend(structured_content)

        return integrated_content

class Lean4IntegrationSystem:
    def setup_lean4_environment(self):
        """Lean4証明環境の構築"""
        system_config = {
            "lean4_version": "4.0.0",
            "mathlib_version": "latest",
            "proof_automation_tools": [
                "aesop",  # 自動証明支援
                "omega",  # ω自動化
                "simp",   # 簡略化戦術
                "auto"    # 自動証明
            ],
            "integration_apis": {
                "python_bridge": "lean4-python-interface",
                "model_interface": "neural-proof-interface"
            }
        }

        # Lean4環境の初期化
        self.initialize_lean4_system(system_config)

        # 証明生成パイプライン構築
        proof_pipeline = self.build_proof_generation_pipeline(system_config)

        return {
            "system_config": system_config,
            "proof_pipeline": proof_pipeline,
            "validation_system": self.create_validation_system()
        }

class IsabelleIntegrationSystem:
    def setup_isabelle_environment(self):
        """Isabelle証明環境の構築"""
        isabelle_config = {
            "isabelle_version": "2023",
            "afp_version": "latest",  # Archive of Formal Proofs
            "proof_methods": [
                "auto",      # 自動証明
                "blast",     # テーブルオ制約
                "metis",     # メタ理論的推論
                "sledgehammer"  # ATP統合
            ]
        }

        # Isabelle環境構築
        self.initialize_isabelle_system(isabelle_config)

        # 証明支援パイプライン
        assistance_pipeline = self.build_assistance_pipeline(isabelle_config)

        return {
            "isabelle_config": isabelle_config,
            "assistance_pipeline": assistance_pipeline,
            "verification_system": self.create_verification_system()
        }

class ProofGenerationRewards:
    def create_mathematical_correctness_reward(self):
        """数学的正確性報酬関数"""
        def reward_fn(completion, **kwargs):
            correctness_score = 0.0

            # 論理的一貫性チェック
            if self.check_logical_consistency(completion):
                correctness_score += 0.4

            # 数学的記法の正確性
            if self.check_mathematical_notation(completion):
                correctness_score += 0.3

            # 証明の完全性
            if self.check_proof_completeness(completion):
                correctness_score += 0.3

            return correctness_score

        return reward_fn

    def create_proof_completeness_reward(self):
        """証明完全性報酬関数"""
        def reward_fn(completion, **kwargs):
            completeness_score = 0.0

            # 証明ステップの論理的接続
            if self.check_proof_steps_connection(completion):
                completeness_score += 0.5

            # 境界条件の考慮
            if self.check_boundary_conditions(completion):
                completeness_score += 0.3

            # 一般性の確保
            if self.check_generality(completion):
                completeness_score += 0.2

            return completeness_score

        return reward_fn

class SpectralRegularizer:
    def apply_spectral_regularization(self, model_outputs, regularization_weight=0.01):
        """スペクトル正則化の適用"""
        regularization_loss = 0.0

        for output in model_outputs:
            # 隠れ状態の共分散行列計算
            hidden_states = output['hidden_states']  # [batch_size, seq_len, hidden_dim]
            batch_cov = torch.cov(hidden_states.view(-1, hidden_states.size(-1)).T)

            # 特異値分解
            singular_values = torch.linalg.svdvals(batch_cov)

            # 有効ランク計算
            normalized_sv = singular_values / singular_values[0]
            effective_rank = torch.sum(normalized_sv > 0.1).float() / len(normalized_sv)

            # エントロピー計算
            entropy = -torch.sum(normalized_sv * torch.log(normalized_sv + 1e-8))

            # 正則化項
            rank_penalty = torch.relu(0.8 - effective_rank)
            entropy_penalty = torch.relu(0.5 - entropy)

            regularization_loss += rank_penalty + entropy_penalty

        return regularization_weight * regularization_loss / len(model_outputs)

class MathematicalReasoningAgent:
    def __init__(self):
        self.proof_generator = MCPTool("lean4-prover")
        self.symbolic_solver = MCPTool("sympy-solver")
        self.theorem_verifier = MCPTool("formal-verifier")
        self.knowledge_base = MCPTool("math-knowledge-base")

    async def solve_mathematical_problem(self, problem_statement):
        """数学的問題解決プロセス"""
        # 1. 問題理解と形式化
        formalized_problem = await self.knowledge_base.formalize_problem(problem_statement)

        # 2. 関連定理・補題検索
        relevant_theorems = await self.knowledge_base.search_relevant_theorems(formalized_problem)

        # 3. 証明戦略立案
        proof_strategy = await self.proof_generator.generate_proof_strategy(formalized_problem, relevant_theorems)

        # 4. 証明生成
        proof_candidates = await self.proof_generator.generate_proofs(formalized_problem, proof_strategy)

        # 5. 記号的検証
        verified_proofs = await self.symbolic_solver.verify_proofs(proof_candidates)

        # 6. 形式的証明
        final_proof = await self.theorem_verifier.certify_proof(verified_proofs[0])

        return {
            "formalized_problem": formalized_problem,
            "relevant_theorems": relevant_theorems,
            "proof_strategy": proof_strategy,
            "generated_proofs": proof_candidates,
            "verified_proof": verified_proofs[0],
            "certified_proof": final_proof
        }

class DesktopAssistantAgent:
    def __init__(self):
        self.file_manager = MCPTool("file-system-manager")
        self.application_launcher = MCPTool("app-launcher")
        self.productivity_tools = MCPTool("productivity-suite")
        self.system_monitor = MCPTool("system-monitor")

    async def optimize_workspace(self, user_context):
        """ワークスペース最適化"""
        # ファイル整理
        file_organization = await self.file_manager.organize_files(user_context)

        # アプリケーション起動
        app_recommendations = await self.application_launcher.recommend_apps(user_context)

        # 生産性向上提案
        productivity_suggestions = await self.productivity_tools.generate_suggestions(user_context)

        # システム状態監視
        system_health = await self.system_monitor.check_system_health()

        return {
            "file_organization": file_organization,
            "app_recommendations": app_recommendations,
            "productivity_suggestions": productivity_suggestions,
            "system_health": system_health
        }

class ProgressMonitor:
    def __init__(self):
        self.phase_status = {
            "data_collection": {"status": "pending", "completion": 0.0},
            "environment_setup": {"status": "pending", "completion": 0.0},
            "training_pipeline": {"status": "pending", "completion": 0.0},
            "agent_development": {"status": "pending", "completion": 0.0},
            "validation_testing": {"status": "pending", "completion": 0.0}
        }

    def update_phase_progress(self, phase_name, completion_percentage, status="in_progress"):
        """Phase進捗更新"""
        self.phase_status[phase_name] = {
            "status": status,
            "completion": completion_percentage,
            "last_updated": datetime.now().isoformat()
        }

        # 全体進捗計算
        total_completion = sum(phase["completion"] for phase in self.phase_status.values()) / len(self.phase_status)

        return {
            "phase_updates": self.phase_status,
            "overall_completion": total_completion,
            "estimated_completion_time": self.estimate_completion_time()
        }

class QualityAssuranceMonitor:
    def assess_quality_metrics(self):
        """品質メトリクス評価"""
        metrics = {
            "mathematical_correctness": self.evaluate_mathematical_correctness(),
            "proof_completeness": self.evaluate_proof_completeness(),
            "agent_performance": self.evaluate_agent_performance(),
            "system_stability": self.evaluate_system_stability(),
            "user_satisfaction": self.evaluate_user_satisfaction()
        }

        # 総合品質スコア
        overall_quality = sum(metrics.values()) / len(metrics)

        return {
            "individual_metrics": metrics,
            "overall_quality": overall_quality,
            "quality_trend": self.analyze_quality_trend(),
            "improvement_recommendations": self.generate_improvement_recommendations(metrics)
        }

# miniF2F, Lean Workbook, 数学競技問題の収集
python scripts/plan_mode/execute_data_collection.py \
  --datasets minif2f,lean_workbook,math_competitions \
  --output-path data/mathematical_datasets/

# Lean4, Isabelle統合開発環境構築
python scripts/plan_mode/setup_formal_proof_environment.py \
  --lean4-version 4.0.0 \
  --isabelle-version 2023 \
  --output-path environments/formal_proof/

# 証明生成特化GRPO訓練
python scripts/plan_mode/implement_grpo_training.py \
  --model-path microsoft/Phi-3.5-mini-instruct \
  --reward-functions mathematical_correctness,proof_completeness,reasoning_coherence \
  --spectral-regularization \
  --output-path training_output/grpo_proof_generation/

# 数学的推論特化エージェント等開発
python scripts/plan_mode/develop_mcp_a2a_agents.py \
  --agent-types math_reasoning,desktop_assistant,coding_assistant,business_ai \
  --output-path agents/mcp_a2a_specialized/

# 開発進捗リアルタイム監視
python scripts/plan_mode/monitor_development_progress.py \
  --real-time-monitoring \
  --quality-assurance \
  --report-generation