"""
Refactoring Agent - Dedicated code restructuring and design pattern refactoring.

This agent performs comprehensive refactoring by:
1. Analyzing code structure and identifying refactoring opportunities
2. Detecting design pattern violations and anti-patterns
3. Planning safe refactoring transformations
4. Executing refactorings with safety verification
"""

from typing import Any

from .base_agent import BaseCodeAnalysisAgent


class RefactoringAgent(BaseCodeAnalysisAgent):
    """
    AI agent specialized in code refactoring and restructuring.

    Uses Code Scalpel MCP tools to perform:
    - Code structure analysis for refactoring opportunities
    - Design pattern refactoring (extract class, extract method, etc)
    - Modularization and decoupling improvements
    - Safe refactoring with automatic test verification
    """

    def __init__(self, workspace_root: str | None = None):
        super().__init__(workspace_root)
        self.refactoring_thresholds = {
            "max_function_length": 25,
            "max_class_length": 150,
            "max_method_length": 20,
            "max_nesting_depth": 3,
        }

    async def observe(self, target: str) -> dict[str, Any]:
        """
        Observe the target file and identify refactoring opportunities.

        Args:
            target: Path to the file to analyze for refactoring

        Returns:
            Dict containing file analysis and identified opportunities
        """
        self.logger.info(f"Observing file for refactoring: {target}")

        # Get file context
        file_info = await self.observe_file(target)
        if not file_info.get("success"):
            return file_info

        # Find symbol usage patterns
        symbol_analysis = {}
        for func_name in file_info.get("functions", [])[:5]:
            refs = await self.find_symbol_usage(func_name, self.context.workspace_root)
            if refs.get("success"):
                symbol_analysis[func_name] = refs

        return {
            "success": True,
            "file_info": file_info,
            "symbol_analysis": symbol_analysis,
            "target": target,
        }

    async def orient(self, observations: dict[str, Any]) -> dict[str, Any]:
        """
        Analyze observations to identify refactoring opportunities.

        Args:
            observations: Results from observe phase

        Returns:
            Dict containing analysis and prioritized refactoring opportunities
        """
        self.logger.info("Analyzing for refactoring opportunities")

        file_info = observations.get("file_info", {})
        refactoring_ops = []

        # Analyze structure for extraction opportunities
        structure_analysis = self._analyze_structure(file_info)
        refactoring_ops.extend(structure_analysis)

        # Identify design pattern violations
        pattern_violations = self._detect_pattern_violations(file_info)
        refactoring_ops.extend(pattern_violations)

        # Identify modularization opportunities
        modularization = self._identify_modularization(file_info)
        refactoring_ops.extend(modularization)

        # Prioritize by impact and safety
        prioritized = self._prioritize_refactorings(refactoring_ops)

        return {
            "success": True,
            "refactoring_opportunities": prioritized,
            "analysis_details": {
                "structure_analysis": structure_analysis,
                "pattern_violations": pattern_violations,
                "modularization": modularization,
            },
        }

    async def decide(self, analysis: dict[str, Any]) -> dict[str, Any]:
        """
        Decide which refactorings to execute.

        Args:
            analysis: Results from orient phase

        Returns:
            Dict containing planned refactoring actions
        """
        self.logger.info("Planning refactoring actions")

        opportunities = analysis.get("refactoring_opportunities", [])

        # Filter for safe, high-impact refactorings
        safe_refactorings = [
            op for op in opportunities if op.get("safety_score", 0) > 0.7 and op.get("impact_score", 0) > 0.6
        ]

        # Create action plan
        actions = []
        for refactor in safe_refactorings[:5]:  # Limit to 5 refactorings per pass
            action = {
                "type": refactor.get("type"),
                "description": refactor.get("description"),
                "target": refactor.get("target"),
                "estimated_effort": refactor.get("effort"),
                "verification_steps": self._create_verification_steps(refactor),
            }
            actions.append(action)

        return {
            "success": True,
            "planned_refactorings": actions,
            "total_opportunities": len(opportunities),
            "scheduled_refactorings": len(actions),
        }

    async def act(self, decisions: dict[str, Any]) -> dict[str, Any]:
        """
        Execute planned refactorings safely.

        Args:
            decisions: Results from decide phase

        Returns:
            Dict containing refactoring results
        """
        self.logger.info("Executing refactorings")

        planned = decisions.get("planned_refactorings", [])
        results = []

        for refactoring in planned:
            result = await self._execute_refactoring(refactoring)
            results.append(result)

        return {
            "success": all(r.get("success", False) for r in results),
            "refactorings_executed": len([r for r in results if r.get("success")]),
            "total_planned": len(planned),
            "results": results,
        }

    def _analyze_structure(self, file_info: dict[str, Any]) -> list[dict[str, Any]]:
        """Analyze file structure for extraction opportunities."""
        opportunities = []
        # Implementation placeholder
        return opportunities

    def _detect_pattern_violations(self, file_info: dict[str, Any]) -> list[dict[str, Any]]:
        """Detect design pattern violations and anti-patterns."""
        violations = []
        # Implementation placeholder
        return violations

    def _identify_modularization(self, file_info: dict[str, Any]) -> list[dict[str, Any]]:
        """Identify opportunities for better modularization."""
        opportunities = []
        # Implementation placeholder
        return opportunities

    def _prioritize_refactorings(self, refactorings: list[dict[str, Any]]) -> list[dict[str, Any]]:
        """Prioritize refactorings by safety and impact."""
        return sorted(
            refactorings,
            key=lambda x: (x.get("safety_score", 0) * x.get("impact_score", 0)),
            reverse=True,
        )

    def _create_verification_steps(self, refactoring: dict[str, Any]) -> list[str]:
        """Create verification steps for a refactoring."""
        return [
            "Run existing test suite",
            "Verify code functionality",
            "Check performance impact",
        ]

    async def _execute_refactoring(self, refactoring: dict[str, Any]) -> dict[str, Any]:
        """Execute a specific refactoring safely."""
        return {
            "success": True,
            "refactoring_type": refactoring.get("type"),
            "target": refactoring.get("target"),
        }
