#!/usr/bin/env python3
"""Data models for Claude Code Hooks system"""

from dataclasses import dataclass, field
from datetime import datetime
from typing import Dict, List, Optional, Any
import json


@dataclass
class ToolExecution:
    """Single tool execution record"""
    timestamp: datetime
    tool: str
    parameters: Dict[str, Any]
    success: bool
    error_message: Optional[str] = None
    execution_time: float = 0.0
    context: Dict[str, Any] = field(default_factory=dict)
    
    def to_dict(self) -> Dict[str, Any]:
        return {
            "timestamp": self.timestamp.isoformat(),
            "tool": self.tool,
            "parameters": self.parameters,
            "success": self.success,
            "error_message": self.error_message,
            "execution_time": self.execution_time,
            "context": self.context
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'ToolExecution':
        return cls(
            timestamp=datetime.fromisoformat(data["timestamp"]),
            tool=data["tool"],
            parameters=data["parameters"],
            success=data["success"],
            error_message=data.get("error_message"),
            execution_time=data.get("execution_time", 0.0),
            context=data.get("context", {})
        )


@dataclass 
class Pattern:
    """Learned pattern with confidence scoring"""
    pattern_id: str
    pattern_type: str  # "command_failure", "tool_sequence", "context_error"
    trigger: Dict[str, Any]  # What triggers this pattern
    prediction: Dict[str, Any]  # What we predict will happen
    confidence: float  # 0.0 to 1.0
    evidence_count: int  # How many times we've seen this
    last_seen: datetime
    success_rate: float = 0.0
    
    def to_dict(self) -> Dict[str, Any]:
        return {
            "pattern_id": self.pattern_id,
            "pattern_type": self.pattern_type,
            "trigger": self.trigger,
            "prediction": self.prediction,
            "confidence": self.confidence,
            "evidence_count": self.evidence_count,
            "last_seen": self.last_seen.isoformat(),
            "success_rate": self.success_rate
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'Pattern':
        return cls(
            pattern_id=data["pattern_id"],
            pattern_type=data["pattern_type"],
            trigger=data["trigger"],
            prediction=data["prediction"],
            confidence=data["confidence"],
            evidence_count=data["evidence_count"],
            last_seen=datetime.fromisoformat(data["last_seen"]),
            success_rate=data.get("success_rate", 0.0)
        )


@dataclass
class HookResult:
    """Result of hook execution"""
    allow: bool
    message: str = ""
    warning: bool = False
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    @classmethod
    def success(cls, message: str = "Operation allowed") -> 'HookResult':
        return cls(allow=True, message=message)
    
    @classmethod
    def blocked(cls, reason: str) -> 'HookResult':
        return cls(allow=False, message=reason)
    
    @classmethod
    def allow_with_warning(cls, warning: str) -> 'HookResult':
        return cls(allow=True, message=warning, warning=True)
    
    def to_claude_response(self) -> Dict[str, Any]:
        """Convert to Claude Code hook response format"""
        response = {
            "allow": self.allow,
            "message": self.message
        }
        if self.metadata:
            response.update(self.metadata)
        return response


@dataclass
class ValidationResult:
    """Result of validation operations"""
    allowed: bool
    reason: str = ""
    severity: str = "info"  # info, warning, medium, high, critical
    suggestions: List[str] = field(default_factory=list)
    
    @property
    def is_critical(self) -> bool:
        return self.severity == "critical"
    
    @property
    def is_blocking(self) -> bool:
        return not self.allowed


@dataclass
class BackupDecision:
    """Decision about whether to trigger backup"""
    should_backup: bool
    reason: str
    urgency: str = "medium"  # low, medium, high
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class BackupResult:
    """Result of backup operation"""
    success: bool
    backup_id: str = ""
    backup_path: str = ""
    error: str = ""
    git_success: bool = False
    fallback_performed: bool = False
    components: Dict[str, Any] = field(default_factory=dict)


@dataclass
class GitBackupResult:
    """Result of git backup operation"""
    success: bool
    commit_id: str = ""
    message: str = ""
    error: str = ""


class PatternDatabase:
    """Fast lookup database for learned patterns"""
    
    def __init__(self):
        self.command_patterns: Dict[str, Pattern] = {}
        self.sequence_patterns: List[Pattern] = []
        self.context_patterns: Dict[str, Pattern] = {}
        self.execution_history: List[ToolExecution] = []
        
    def to_dict(self) -> Dict[str, Any]:
        return {
            "command_patterns": {k: v.to_dict() for k, v in self.command_patterns.items()},
            "sequence_patterns": [p.to_dict() for p in self.sequence_patterns],
            "context_patterns": {k: v.to_dict() for k, v in self.context_patterns.items()},
            "execution_history": [e.to_dict() for e in self.execution_history[-100:]]  # Keep last 100
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'PatternDatabase':
        db = cls()
        
        # Load command patterns
        for k, v in data.get("command_patterns", {}).items():
            db.command_patterns[k] = Pattern.from_dict(v)
            
        # Load sequence patterns
        for p in data.get("sequence_patterns", []):
            db.sequence_patterns.append(Pattern.from_dict(p))
            
        # Load context patterns
        for k, v in data.get("context_patterns", {}).items():
            db.context_patterns[k] = Pattern.from_dict(v)
            
        # Load execution history
        for e in data.get("execution_history", []):
            db.execution_history.append(ToolExecution.from_dict(e))
            
        return db