┌─────────────────────────────────────────────────────────────────────────────┐
│                    LAYER 2 SCALING SOLUTIONS                                │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    STATE CHANNELS                                   │   │
│  │  • Off-chain transactions with on-chain settlement                 │   │
│  │  • Examples: Lightning Network, Raiden Network                    │   │
│  │  • Pros: Instant, low cost                                          │   │
│  │  • Cons: Limited use cases, complex routing                        │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    ROLLUPS                                            │   │
│  │  • Transaction execution off-chain, data on-chain                  │   │
│  │  • Types: Optimistic (fraud proofs) and ZK (validity proofs)       │   │
│  │  • Examples: Arbitrum, Optimism, zkSync, StarkNet                 │   │
│  │  • Pros: Scalable, secure                                           │   │
│  │  • Cons: Withdrawal delay (Optimistic), complexity (ZK)           │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    PLASMA                                            │   │
│  │  • Child chains with fraud proofs                                   │   │
│  │  • Examples: OMG Network, Plasma MVP                                │   │
│  │  • Pros: Scalable                                                    │   │
│  │  • Cons: Limited smart contract support                              │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    SIDECHAINS                                         │   │
│  │  • Independent chains connected via bridges                         │   │
│  │  • Examples: Polygon PoS, xDai                                     │   │
│  │  • Pros: High throughput                                              │   │
│  │  • Cons: Separate security assumptions                              │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    VALIDIUMS                                          │   │
│  │  • Off-chain data availability with validity proofs                 │   │
│  │  • Examples: zkSync Lite                                            │   │
│  │  • Pros: High throughput, low cost                                   │   │
│  │  • Cons: Trust assumptions in data availability                    │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    DATA AVAILABILITY MODELS                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ON-CHAIN DATA AVAILABILITY                                                │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Data is posted on Layer 1                                        │   │
│  │ • Anyone can verify                                                 │   │
│  │ • Examples: Optimistic and ZK Rollups                               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  OFF-CHAIN DATA AVAILABILITY                                               │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Data is stored off-chain                                          │   │
│  │ • Requires trust or economic guarantees                             │   │
│  │ • Examples: Validiums, Plasma                                        │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  HYBRID DATA AVAILABILITY                                                  │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ • Some data on-chain, some off-chain                               │   │
│  │ • Examples: Data Availability Committees (DAC)                      │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 9, LESSON 2: LAYER 2 AND SCALING SOLUTIONS DEEP DIVE
# ===================================================================

import hashlib
import time
import random
from typing import Dict, List, Tuple
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("LAYER 2 AND SCALING SOLUTIONS DEEP DIVE")
print("="*70)

# ----------------------------------------------------------------
# PART A: ROLLUP SIMULATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Rollup Simulation (Optimistic Rollup)")
print("-"*60)

class OptimisticRollup:
    """
    Simplified Optimistic Rollup simulation.
    """
    def __init__(self):
        self.l2_state = {}
        self.batches = []
        self.challenge_period = 5  # blocks
        self.finalized_batches = []
        self.current_batch = []
        self.is_challenged = False
    
    def execute_tx(self, sender: str, recipient: str, amount: float) -> Dict:
        """Execute a transaction off-chain."""
        tx = {
            'sender': sender,
            'recipient': recipient,
            'amount': amount,
            'hash': hashlib.sha256(f"{sender}{recipient}{amount}{time.time()}".encode()).hexdigest()[:8]
        }
        self.current_batch.append(tx)
        print(f"TX {tx['hash']}: {sender} -> {recipient} {amount}")
        return tx
    
    def submit_batch(self) -> Dict:
        """Submit a batch of transactions to Layer 1."""
        if not self.current_batch:
            return {'error': 'No transactions to batch'}
        
        # Compute state root (simplified)
        state_root = self._compute_state_root()
        batch = {
            'transactions': self.current_batch,
            'state_root': state_root,
            'timestamp': time.time(),
            'status': 'pending'
        }
        self.batches.append(batch)
        self.current_batch = []
        print(f"Batch submitted. State root: {state_root[:16]}...")
        return batch
    
    def _compute_state_root(self) -> str:
        """Compute state root from current state."""
        state_str = "|".join(f"{k}:{v}" for k, v in self.l2_state.items())
        return hashlib.sha256(state_str.encode()).hexdigest()
    
    def challenge_batch(self, batch_index: int) -> bool:
        """Challenge a batch with a fraud proof."""
        if batch_index >= len(self.batches):
            return False
        batch = self.batches[batch_index]
        # Simulate fraud detection (random)
        is_fraud = random.random() < 0.1
        if is_fraud:
            batch['status'] = 'challenged'
            self.is_challenged = True
            print(f"Batch {batch_index} challenged for fraud")
            return True
        return False
    
    def finalize_batch(self, batch_index: int) -> bool:
        """Finalize a batch after challenge period."""
        if batch_index >= len(self.batches):
            return False
        batch = self.batches[batch_index]
        if batch['status'] != 'pending':
            return False
        # Simulate finalisation
        if not self.is_challenged:
            batch['status'] = 'finalized'
            self.finalized_batches.append(batch)
            # Update state
            for tx in batch['transactions']:
                # Simple state update: just record transfer
                self.l2_state[tx['sender']] = self.l2_state.get(tx['sender'], 0) - tx['amount']
                self.l2_state[tx['recipient']] = self.l2_state.get(tx['recipient'], 0) + tx['amount']
            print(f"Batch {batch_index} finalized")
            return True
        return False

# Simulate rollup
rollup = OptimisticRollup()

print("Optimistic Rollup Simulation:")

# Execute transactions
rollup.execute_tx('Alice', 'Bob', 10)
rollup.execute_tx('Bob', 'Charlie', 5)
rollup.execute_tx('Alice', 'Charlie', 3)

# Submit batch
rollup.submit_batch()

# Simulate challenge period
print("\nWaiting for challenge period...")
time.sleep(1)  # Simulate time passing

# Challenge (optional - simulate no challenge)
rollup.finalize_batch(0)

print("\nFinal State:")
for account, balance in rollup.l2_state.items():
    print(f"  {account}: {balance}")

# ----------------------------------------------------------------
# PART B: LAYER 2 COMPARISON
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Layer 2 Comparison")
print("-"*60)

comparison_data = {
    'Solution': ['State Channels', 'Optimistic Rollup', 'ZK Rollup', 'Plasma', 'Sidechain', 'Validium'],
    'TPS (estimate)': [10000, 2000, 3000, 1000, 1000, 4000],
    'Finality (minutes)': [0, 10080, 10, 60, 10, 10],
    'Security': ['High', 'High', 'Very High', 'Medium', 'Low-Medium', 'Medium'],
    'Gas Cost': ['Very Low', 'Low', 'Low', 'Low', 'Low', 'Very Low'],
    'EVM Compat': ['Limited', 'Yes', 'Partial', 'Limited', 'Yes', 'Partial']
}

comparison_df = pd.DataFrame(comparison_data)
print(comparison_df.to_string(index=False))

# ----------------------------------------------------------------
# PART C: DATA AVAILABILITY MODELS
# -----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Data Availability Models")
print("-"*60)

da_models = {
    'Model': ['On-Chain Data Availability', 'Off-Chain Data Availability', 'Hybrid Data Availability'],
    'Description': [
        'Data posted on Layer 1',
        'Data stored off-chain',
        'Some data on-chain, some off-chain'
    ],
    'Security': ['High', 'Medium', 'Medium-High'],
    'Cost': ['High', 'Low', 'Medium'],
    'Scalability': ['Limited', 'High', 'High'],
    'Examples': ['Rollups', 'Validiums, Plasma', 'DAC-based solutions']
}

da_df = pd.DataFrame(da_models)
print(da_df.to_string(index=False))

# ----------------------------------------------------------------
# PART D: SUMMARY AND RECOMMENDATIONS
# -----------------------------------------------------------------

print("\n" + "="*70)
print("PART D: Summary and Recommendations")
print("="*70)

print("""
Layer 2 and Scaling Solutions Deep Dive – Key Takeaways:

1. Layer 2 solutions: state channels, rollups, plasma, sidechains, validiums.
2. Rollups are the dominant approach: Optimistic (fraud proofs) and ZK (validity proofs).
3. Optimistic rollups: 7-day withdrawal delay, EVM-compatible, lower cost.
4. ZK rollups: fast finality, high security, limited EVM compatibility.
5. Data availability is critical: on-chain (high security), off-chain (lower cost), hybrid.
6. Sidechains are independent with separate security assumptions.
7. Trade-offs: security vs speed, compatibility vs innovation.

Recommendations:
  - Choose Optimistic Rollups for EVM-compatible applications.
  - Choose ZK Rollups for high security and fast finality.
  - Consider data availability trade-offs.
  - Use sidechains for experimentation.
  - Monitor Layer 2 ecosystem developments.
  - Plan for migration between solutions.
""")