"""
Thinking Traces and Chain-of-Thought for Nthuku-Fast

Implements visible reasoning traces like Grok Code Fast 1:
- Step-by-step reasoning generation
- Structured thought process
- Tool calling with rationale
- Debugging explanations
"""

import torch
import torch.nn as nn
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
from enum import Enum


class ThoughtType(Enum):
    """Types of thinking steps"""
    ANALYSIS = "analysis"
    PLANNING = "planning"
    EXECUTION = "execution"
    VERIFICATION = "verification"
    REFLECTION = "reflection"


@dataclass
class ThinkingStep:
    """Single step in reasoning trace"""
    step_num: int
    thought_type: ThoughtType
    content: str
    confidence: float = 1.0
    metadata: Optional[Dict] = None
    
    def __str__(self) -> str:
        icon = {
            ThoughtType.ANALYSIS: "🔍",
            ThoughtType.PLANNING: "📋",
            ThoughtType.EXECUTION: "⚙️",
            ThoughtType.VERIFICATION: "✅",
            ThoughtType.REFLECTION: "💭"
        }
        return f"{icon[self.thought_type]} Step {self.step_num} [{self.thought_type.value}]: {self.content}"


class ThinkingTrace:
    """
    Container for reasoning trace
    Shows model's thought process step-by-step
    """
    
    def __init__(self):
        self.steps: List[ThinkingStep] = []
        self.current_step = 0
        
    def add_step(
        self,
        thought_type: ThoughtType,
        content: str,
        confidence: float = 1.0,
        metadata: Optional[Dict] = None
    ):
        """Add a thinking step"""
        step = ThinkingStep(
            step_num=self.current_step + 1,
            thought_type=thought_type,
            content=content,
            confidence=confidence,
            metadata=metadata or {}
        )
        self.steps.append(step)
        self.current_step += 1
        
    def add_analysis(self, content: str, **kwargs):
        """Add an analysis step"""
        self.add_step(ThoughtType.ANALYSIS, content, **kwargs)
        
    def add_planning(self, content: str, **kwargs):
        """Add a planning step"""
        self.add_step(ThoughtType.PLANNING, content, **kwargs)
        
    def add_execution(self, content: str, **kwargs):
        """Add an execution step"""
        self.add_step(ThoughtType.EXECUTION, content, **kwargs)
        
    def add_verification(self, content: str, **kwargs):
        """Add a verification step"""
        self.add_step(ThoughtType.VERIFICATION, content, **kwargs)
        
    def add_reflection(self, content: str, **kwargs):
        """Add a reflection step"""
        self.add_step(ThoughtType.REFLECTION, content, **kwargs)
        
    def format_trace(self, show_confidence: bool = True) -> str:
        """Format complete thinking trace"""
        if not self.steps:
            return "No thinking steps recorded."
        
        lines = ["=" * 70, "🧠 THINKING TRACE", "=" * 70]
        
        for step in self.steps:
            line = str(step)
            if show_confidence and step.confidence < 1.0:
                line += f" (confidence: {step.confidence:.2%})"
            lines.append(line)
            
            # Add metadata if present
            if step.metadata:
                for key, value in step.metadata.items():
                    lines.append(f"   └─ {key}: {value}")
        
        lines.append("=" * 70)
        return "\n".join(lines)
    
    def get_summary(self) -> str:
        """Get a brief summary of the thinking process"""
        if not self.steps:
            return "No thinking steps."
        
        type_counts = {}
        for step in self.steps:
            type_counts[step.thought_type] = type_counts.get(step.thought_type, 0) + 1
        
        summary_parts = [f"{count} {type.value}" for type, count in type_counts.items()]
        return f"Thinking: {', '.join(summary_parts)} ({len(self.steps)} total steps)"
    
    def clear(self):
        """Clear all thinking steps"""
        self.steps.clear()
        self.current_step = 0


class ChainOfThoughtGenerator(nn.Module):
    """
    Generates explicit chain-of-thought reasoning
    
    Uses special tokens to mark thinking sections:
    <think>...</think> - reasoning process
    <answer>...</answer> - final answer
    """
    
    def __init__(
        self,
        model: nn.Module,
        thinking_budget: int = 100,  # Max tokens for thinking
        temperature: float = 0.8
    ):
        super().__init__()
        self.model = model
        self.thinking_budget = thinking_budget
        self.temperature = temperature
        
        # Special tokens (would be added to tokenizer)
        self.THINK_START = "<think>"
        self.THINK_END = "</think>"
        self.ANSWER_START = "<answer>"
        self.ANSWER_END = "</answer>"
        
    @torch.no_grad()
    def generate_with_thinking(
        self,
        input_ids: torch.Tensor,
        vision_features: torch.Tensor,
        max_length: int = 200,
        show_thinking: bool = True
    ) -> Tuple[str, ThinkingTrace]:
        """
        Generate response with explicit thinking trace
        
        Args:
            input_ids: Input tokens
            vision_features: Vision context
            max_length: Maximum total length
            show_thinking: Whether to display thinking steps
            
        Returns:
            answer: Final answer text
            trace: Thinking trace object
        """
        device = input_ids.device
        trace = ThinkingTrace()
        
        # Phase 1: Generate thinking
        trace.add_analysis("Analyzing the problem...")
        
        thinking_tokens = self._generate_thinking_phase(
            input_ids, vision_features, trace
        )
        
        # Phase 2: Generate answer
        trace.add_execution("Generating response based on analysis...")
        
        answer_tokens = self._generate_answer_phase(
            input_ids, thinking_tokens, vision_features, trace
        )
        
        # Decode
        answer = self.model.tokenizer.decode(answer_tokens[0], skip_special_tokens=True)
        
        trace.add_verification("Response generated successfully")
        
        if show_thinking:
            print(trace.format_trace())
        
        return answer, trace
    
    def _generate_thinking_phase(
        self,
        input_ids: torch.Tensor,
        vision_features: torch.Tensor,
        trace: ThinkingTrace
    ) -> torch.Tensor:
        """Generate thinking tokens"""
        
        trace.add_planning("Breaking down the task into steps...")
        
        # In a real implementation, this would:
        # 1. Generate tokens with <think> prefix
        # 2. Generate reasoning steps
        # 3. End with </think>
        
        # For now, simulate
        thinking_text = """
        1. Understand the visual input
        2. Identify key elements
        3. Formulate response strategy
        """
        
        # Would tokenize and generate this
        thinking_tokens = input_ids.clone()
        
        trace.add_reflection("Reasoning complete", confidence=0.95)
        
        return thinking_tokens
    
    def _generate_answer_phase(
        self,
        input_ids: torch.Tensor,
        thinking_tokens: torch.Tensor,
        vision_features: torch.Tensor,
        trace: ThinkingTrace
    ) -> torch.Tensor:
        """Generate final answer tokens"""
        
        # Generate with thinking context
        # In practice, concatenate thinking + input and generate
        answer_tokens = thinking_tokens.clone()
        
        return answer_tokens


class ToolUseWithReasoning:
    """
    Tool calling with explicit reasoning
    Shows why tool is being called and what to do with results
    """
    
    def __init__(self):
        self.available_tools = {
            'grep': 'Search for patterns in text',
            'file_edit': 'Edit file contents',
            'shell': 'Execute shell command',
            'search': 'Search codebase or documentation'
        }
        
    def plan_tool_use(
        self,
        task: str,
        trace: ThinkingTrace
    ) -> List[Dict[str, str]]:
        """
        Plan which tools to use and in what order
        
        Args:
            task: Task description
            trace: Thinking trace to populate
            
        Returns:
            List of tool calls with reasoning
        """
        trace.add_analysis(f"Analyzing task: {task}")
        
        # Example reasoning process
        tool_plan = []
        
        if "find" in task.lower() or "search" in task.lower():
            trace.add_planning(
                "Task requires searching - will use grep tool",
                metadata={'tool': 'grep', 'reason': 'search pattern detected'}
            )
            tool_plan.append({
                'tool': 'grep',
                'reasoning': 'Need to locate relevant code/text',
                'expected_outcome': 'File locations and line numbers'
            })
        
        if "edit" in task.lower() or "change" in task.lower():
            trace.add_planning(
                "Task requires modifications - will use file_edit tool",
                metadata={'tool': 'file_edit', 'reason': 'modification needed'}
            )
            tool_plan.append({
                'tool': 'file_edit',
                'reasoning': 'Need to modify file contents',
                'expected_outcome': 'Updated file'
            })
        
        trace.add_planning(
            f"Tool sequence planned: {' → '.join(t['tool'] for t in tool_plan)}"
        )
        
        return tool_plan
    
    def execute_with_reasoning(
        self,
        tool_plan: List[Dict[str, str]],
        trace: ThinkingTrace
    ) -> List[str]:
        """Execute tools with reasoning trace"""
        results = []
        
        for i, tool_call in enumerate(tool_plan, 1):
            trace.add_execution(
                f"Executing {tool_call['tool']}: {tool_call['reasoning']}"
            )
            
            # Simulate tool execution
            result = f"[{tool_call['tool']} output]"
            results.append(result)
            
            trace.add_verification(
                f"Tool {tool_call['tool']} completed",
                metadata={'result_summary': 'Success'}
            )
        
        return results


def create_thinking_prompt(task: str, context: Optional[str] = None) -> str:
    """
    Create a prompt that encourages step-by-step thinking
    
    Args:
        task: The task to complete
        context: Optional context
        
    Returns:
        Formatted prompt with thinking structure
    """
    prompt = f"""Let's solve this step by step:

Task: {task}
"""
    
    if context:
        prompt += f"\nContext: {context}\n"
    
    prompt += """
Please think through this carefully:
1. First, analyze what's being asked
2. Break down the problem into steps
3. Consider the approach
4. Execute the solution
5. Verify the result

<think>
"""
    
    return prompt


# Example usage
def demonstrate_thinking_traces():
    """Demonstrate thinking trace functionality"""
    print("\n" + "=" * 70)
    print("🧠 Thinking Traces Demonstration")
    print("=" * 70 + "\n")
    
    # Create trace
    trace = ThinkingTrace()
    
    # Add reasoning steps
    trace.add_analysis(
        "User wants to implement a new feature in the codebase",
        confidence=0.95
    )
    
    trace.add_planning(
        "Steps: 1) Find relevant files, 2) Understand current implementation, 3) Make changes",
        metadata={'estimated_time': '5 minutes'}
    )
    
    trace.add_execution(
        "Searching for files matching pattern 'auth'...",
        metadata={'tool': 'grep', 'pattern': 'auth'}
    )
    
    trace.add_verification(
        "Found 3 files. Analyzing auth.py as main candidate",
        confidence=0.9
    )
    
    trace.add_reflection(
        "Current implementation uses JWT. Modification should preserve this pattern"
    )
    
    # Display
    print(trace.format_trace())
    print("\n" + trace.get_summary())
    print("\n" + "=" * 70)


if __name__ == "__main__":
    demonstrate_thinking_traces()
