"""
Enhanced chain of thought and reasoning capabilities for the AI agent.
"""

import logging
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum

logger = logging.getLogger(__name__)

class ReasoningType(Enum):
    """Types of reasoning approaches."""
    LINEAR = "linear"  # Standard step-by-step reasoning
    TREE = "tree"      # Tree of thoughts with branching
    SELF_CONSISTENT = "self_consistent"  # Multiple reasoning paths
    LAYERED = "layered"  # Multi-layer verification

@dataclass
class ReasoningStep:
    """A single step in the reasoning process."""
    step_number: int
    description: str
    tool_name: Optional[str]
    tool_input: Optional[Dict[str, Any]]
    output: Optional[Any]
    confidence: float
    verification_status: bool

@dataclass
class ReasoningPath:
    """A complete reasoning path with steps."""
    steps: List[ReasoningStep]
    final_answer: str
    confidence: float
    verification_status: bool

class AdvancedReasoning:
    """Enhanced reasoning system with multiple approaches."""
    
    def __init__(self):
        self.reasoning_history = []
        self.verification_threshold = 0.8
        self.max_verification_steps = 4
    
    def generate_reasoning_plan(self, query: str, reasoning_type: ReasoningType) -> List[ReasoningStep]:
        """Generate a reasoning plan based on the query and reasoning type."""
        if reasoning_type == ReasoningType.LINEAR:
            return self._generate_linear_plan(query)
        elif reasoning_type == ReasoningType.TREE:
            return self._generate_tree_plan(query)
        elif reasoning_type == ReasoningType.SELF_CONSISTENT:
            return self._generate_self_consistent_plan(query)
        elif reasoning_type == ReasoningType.LAYERED:
            return self._generate_layered_plan(query)
        else:
            raise ValueError(f"Unknown reasoning type: {reasoning_type}")
    
    def _generate_linear_plan(self, query: str) -> List[ReasoningStep]:
        """Generate a linear, step-by-step reasoning plan."""
        # This would typically use an LLM to break down the query
        # For now, return a placeholder plan
        return [
            ReasoningStep(
                step_number=1,
                description="Analyze query and identify key components",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=2,
                description="Determine required tools and information",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=3,
                description="Execute tool calls and gather information",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=4,
                description="Synthesize information into final answer",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            )
        ]
    
    def _generate_tree_plan(self, query: str) -> List[ReasoningStep]:
        """Generate a tree-based reasoning plan with branching paths."""
        # This would use an LLM to generate multiple possible approaches
        # For now, return a placeholder plan
        return [
            ReasoningStep(
                step_number=1,
                description="Generate multiple reasoning paths",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=2,
                description="Evaluate each path's potential",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=3,
                description="Select and execute best path",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            )
        ]
    
    def _generate_self_consistent_plan(self, query: str) -> List[ReasoningStep]:
        """Generate a self-consistent reasoning plan with multiple paths."""
        # This would use an LLM to generate multiple independent solutions
        # For now, return a placeholder plan
        return [
            ReasoningStep(
                step_number=1,
                description="Generate multiple independent solutions",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=2,
                description="Compare solutions for consistency",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=3,
                description="Select most consistent solution",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            )
        ]
    
    def _generate_layered_plan(self, query: str) -> List[ReasoningStep]:
        """Generate a layered reasoning plan with verification at each step."""
        # This would use an LLM to generate a plan with built-in verification
        # For now, return a placeholder plan
        return [
            ReasoningStep(
                step_number=1,
                description="Initial reasoning and tool selection",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=2,
                description="First layer verification",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=3,
                description="Second layer verification",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            ),
            ReasoningStep(
                step_number=4,
                description="Final synthesis and verification",
                tool_name=None,
                tool_input=None,
                output=None,
                confidence=0.0,
                verification_status=False
            )
        ]
    
    def verify_step(self, step: ReasoningStep) -> bool:
        """Verify a single reasoning step."""
        if not step.output:
            return False
        
        # Check confidence threshold
        if step.confidence < self.verification_threshold:
            return False
        
        # Verify tool output if applicable
        if step.tool_name and step.tool_input:
            # This would typically use an LLM to verify the tool's output
            # For now, return True if we have output
            return bool(step.output)
        
        return True
    
    def verify_path(self, path: ReasoningPath) -> bool:
        """Verify an entire reasoning path."""
        # Check if all steps are verified
        if not all(step.verification_status for step in path.steps):
            return False
        
        # Check final confidence
        if path.confidence < self.verification_threshold:
            return False
        
        # Verify final answer
        if not path.final_answer:
            return False
        
        return True
    
    def record_reasoning(self, path: ReasoningPath):
        """Record a reasoning path for future reference."""
        self.reasoning_history.append(path)
    
    def get_reasoning_history(self) -> List[ReasoningPath]:
        """Get the history of reasoning paths."""
        return self.reasoning_history.copy()
    
    def analyze_reasoning_patterns(self) -> Dict[str, Any]:
        """Analyze patterns in reasoning history."""
        if not self.reasoning_history:
            return {}
        
        # Calculate success rate
        success_count = sum(1 for path in self.reasoning_history if path.verification_status)
        success_rate = success_count / len(self.reasoning_history)
        
        # Calculate average confidence
        avg_confidence = sum(path.confidence for path in self.reasoning_history) / len(self.reasoning_history)
        
        # Calculate average steps per path
        avg_steps = sum(len(path.steps) for path in self.reasoning_history) / len(self.reasoning_history)
        
        return {
            "success_rate": success_rate,
            "average_confidence": avg_confidence,
            "average_steps": avg_steps,
            "total_paths": len(self.reasoning_history)
        }