#!/usr/bin/env python3
"""COMPARISON REPORT"""
print("""
\033[1;36m============================================================\033[0m
\033[1;36m        GLYPHOS vs TRANSFORMER - COMPARATIVE ANALYSIS         \033[0m
\033[1;36m============================================================\033[0m

\033[1;33m1. WHAT EACH BENCHMARK MEASURES\033[0m
------------------------------------------------------------
\033[1mTRANSFORMER:\033[0m
  ✓ Full vocabulary embedding (10,000+ classes)
  ✓ Multi-head attention O(N²) for ALL token pairs
  ✓ Softmax normalization (exponential operations)
  ✓ Residual connections + Layer Normalization
  ✓ Language model output head
  
\033[1mGLYPHOS:\033[0m
  ✓ Sparse graph with 4 edges per node
  ✓ Simple weighted averaging of neighbors  
  ✓ Binary similarity check (fixed threshold)
  ✓ Sigmoid activation (cheap approximation)
  ✓ No vocabulary, no language modeling

\033[91mKEY POINT: These solve fundamentally DIFFERENT problems!\033[0m

\033[1;33m2. OPERATIONAL COST COMPARISON\033[0m
------------------------------------------------------------
| Component          | Transformer      | GlyphOS          |
|--------------------|------------------|------------------|
| Attention Ops      | \033[91m~500M/token\033[0m     | \033[92m~16K/node\033[0m     |
| Memory Pattern     | \033[91mRandom/Cache miss\033[0m|\033[92m Sequential/Clean\033[0m|
| Scaling Behavior   | \033[91mO(N²)\033[0m          | \033[92mO(edges) ≈ O(N)\033[0m |
| Training Required  | \033[91mYes (weeks)\033[0m     | \033[92mNo (static)\033[0m    |
| Capability         | \033[93mText generation\033[0m  | \033[93mGraph relaxation\033[0m |

\033[1;33m3. APPLES-TO-APPLES COMPARISON NEEDS\033[0m
------------------------------------------------------------
□ Same task (e.g., text completion)
✓ Same sequence length
✓ Same hardware  
□ Same evaluation metric (perplexity, BLEU, etc.)
□ Same parameter budget

\033[91mWithout these, performance claims are misleading.\033[0m

\033[36mRun actual benchmarks to see real timings.\033[0m
""")