#!/usr/bin/env python3
"""
CONCRETE DEMONSTRATION: Memory blocking due to 2k token limit.
This shows exactly when and how memory components get DROPPED.
"""

class MockMemory:
    """Mock memory to demonstrate the blocking behavior."""

    def __init__(self, max_tokens=2000):
        self.max_tokens = max_tokens

        # Simulate working memory with long conversations
        self.working_memory = [
            {"content": "This is a very long conversation about quantum computing and its applications in cryptography. " * 10, "role": "user"},
            {"content": "Here's another detailed discussion about machine learning architectures and neural networks. " * 10, "role": "assistant"},
            {"content": "A third conversation about distributed systems and microservices architecture patterns. " * 10, "role": "user"}
        ]

        # Simulate current reasoning cycle
        self.current_cycle = MockCycle()

        # Simulate knowledge facts
        self.facts = [f"Fact {i}: Some important knowledge about topic {i}. " * 5 for i in range(20)]

    def estimate_tokens(self, text: str) -> int:
        return len(text.split()) * 1.3  # Same estimation as real code

    def get_context_with_blocking_demo(self):
        """Demonstrate exactly where blocking occurs."""
        context_parts = []
        estimated_tokens = 0
        blocking_events = []

        print(f"🎯 MEMORY CONTEXT BUILDING (max_tokens={self.max_tokens})")
        print("="*60)

        # Recent Context (30% limit = 600 tokens with 2k limit)
        recent_limit = self.max_tokens * 0.3
        print(f"\n📋 RECENT CONTEXT (limit: {recent_limit} tokens)")

        working_section = ["\\n--- Recent Context ---"]
        for i, item in enumerate(self.working_memory):
            content = item["content"]
            content_tokens = self.estimate_tokens(content)

            print(f"  Item {i+1}: {content_tokens} tokens")

            if estimated_tokens < recent_limit:
                working_section.append(f"- [{item['role']}] {content}")
                estimated_tokens += content_tokens
                print(f"    ✅ INCLUDED (total: {estimated_tokens} tokens)")
            else:
                blocking_events.append(f"Working memory item {i+1} BLOCKED (would exceed {recent_limit} token limit)")
                print(f"    ❌ BLOCKED! Would exceed {recent_limit} token limit")

        context_parts.extend(working_section)

        # ReAct Reasoning (40% limit = 800 tokens with 2k limit)
        reasoning_limit = self.max_tokens * 0.4
        print(f"\n🧠 REACT REASONING (limit: {reasoning_limit} tokens)")

        reasoning_tokens = self.estimate_tokens(self.current_cycle.get_reasoning())
        print(f"  Reasoning cycle: {reasoning_tokens} tokens")

        if estimated_tokens < reasoning_limit:
            context_parts.append(self.current_cycle.get_reasoning())
            estimated_tokens += reasoning_tokens
            print(f"    ✅ INCLUDED (total: {estimated_tokens} tokens)")
        else:
            blocking_events.append(f"ReAct reasoning BLOCKED (would exceed {reasoning_limit} token limit)")
            print(f"    ❌ BLOCKED! Would exceed {reasoning_limit} token limit")

        # Knowledge Facts (60% limit = 1200 tokens with 2k limit)
        facts_limit = self.max_tokens * 0.6
        print(f"\n📚 KNOWLEDGE FACTS (limit: {facts_limit} tokens)")

        facts_section = ["\\n--- Relevant Knowledge ---"]
        included_facts = 0

        for i, fact in enumerate(self.facts):
            fact_tokens = self.estimate_tokens(fact)

            if estimated_tokens < facts_limit:
                facts_section.append(f"- {fact}")
                estimated_tokens += fact_tokens
                included_facts += 1
                print(f"  Fact {i+1}: ✅ INCLUDED ({fact_tokens} tokens, total: {estimated_tokens})")
            else:
                blocked_facts = len(self.facts) - included_facts
                blocking_events.append(f"{blocked_facts} knowledge facts BLOCKED (would exceed {facts_limit} token limit)")
                print(f"  Fact {i+1}+: ❌ BLOCKED! {blocked_facts} facts dropped")
                break

        context_parts.extend(facts_section)

        print(f"\n📊 FINAL RESULTS:")
        print(f"  Total context tokens: {estimated_tokens}/{self.max_tokens}")
        print(f"  Context utilization: {(estimated_tokens/self.max_tokens)*100:.1f}%")

        if blocking_events:
            print(f"\n🚫 BLOCKING EVENTS:")
            for event in blocking_events:
                print(f"  - {event}")
        else:
            print(f"\n✅ No blocking occurred")

        return "\\n".join(context_parts)


class MockCycle:
    """Mock ReAct cycle for testing."""

    def get_reasoning(self):
        return "\\n--- Current Reasoning ---\\nThought: Analyzing the user's request about file operations\\nAction: Planning to use list_files and read_file tools\\nObservation: Tools are available and ready"


def compare_limits():
    """Compare 2k vs 8k token limits."""
    print("\n" + "="*80)
    print("COMPARISON: 2k vs 8k Token Limits")
    print("="*80)

    print("\n🔴 OLD SYSTEM (2k token limit):")
    old_memory = MockMemory(max_tokens=2000)
    old_memory.get_context_with_blocking_demo()

    print("\n🟢 NEW SYSTEM (8k token limit):")
    new_memory = MockMemory(max_tokens=8000)
    new_memory.get_context_with_blocking_demo()


if __name__ == "__main__":
    print("🔬 CONCRETE MEMORY BLOCKING DEMONSTRATION")
    print("="*80)
    print("This shows exactly when the 2k token limit blocks memory components")
    print("from being included in the LLM context, making it LESS stateful.")

    compare_limits()

    print("\n" + "="*80)
    print("💡 CONCLUSION")
    print("="*80)
    print("The 2k token limit actively DROPS important context, making the LLM")
    print("forget recent conversations, reasoning steps, and learned facts.")
    print("This is why your ReAct cycle hit the limit so early!")