"""
Lab 6.9: Cost Optimization

Profile a session, identify savings, and implement cascade routing.
"""

# TODO 1: Define cost profiles for different models
MODEL_COSTS = {
    "gemini-flash": {"input": 0.15, "output": 0.60},    # $/M tokens
    "deepseek-v3": {"input": 0.27, "output": 1.10},
    "claude-sonnet": {"input": 3.00, "output": 15.00},
    "claude-opus": {"input": 15.00, "output": 75.00},
}


# TODO 2: Profile a real agent session
SESSION_LOG = [
    # (step, model, input_tokens, output_tokens, step_type)
    ("retrieve context", "claude-opus", 8000, 200, "retrieval"),
    ("analyze data", "claude-opus", 5000, 1500, "analysis"),
    ("make decision", "claude-opus", 3000, 800, "decision"),
    ("format output", "claude-opus", 2000, 400, "formatting"),
    ("verify result", "claude-opus", 2500, 600, "verification"),
]


# TODO 3: Implement cost calculation
def calculate_session_cost(session: list[tuple], model: str) -> float:
    """Calculate total cost of a session using given model's pricing."""
    pass  # TODO


# TODO 4: Implement cascade routing optimization
CASCADE_MAP = {
    "retrieval": "gemini-flash",   # Cheap: simple retrieval
    "formatting": "gemini-flash",   # Cheap: formatting
    "verification": "deepseek-v3",  # Medium: structured checks
    "analysis": "claude-sonnet",    # Expensive: reasoning
    "decision": "claude-opus",      # Most expensive: critical thinking
}


def calculate_cascade_cost(session: list[tuple]) -> float:
    """Calculate session cost with cascade routing."""
    pass  # TODO


# TODO 5: Find additional savings
def find_optimizations(session: list[tuple], current_cost: float) -> list[dict]:
    """
    Analyze the session and suggest optimizations.
    
    Look for:
    - Steps where cascade routing saves >50%
    - Steps with unusually high token counts (context waste)
    - Opportunities to batch small retrievals
    """
    pass  # TODO


if __name__ == "__main__":
    print("=" * 55)
    print(" COST OPTIMIZATION ANALYSIS")
    print("=" * 55)
    
    opus_cost = calculate_session_cost(SESSION_LOG, "claude-opus")
    cascade_cost = calculate_cascade_cost(SESSION_LOG)
    
    print(f"\nAll Opus:    ${opus_cost:.4f}")
    print(f"Cascade:     ${cascade_cost:.4f}")
    print(f"Savings:     {((opus_cost - cascade_cost) / opus_cost * 100):.0f}%")
    
    optimizations = find_optimizations(SESSION_LOG, opus_cost)
    if optimizations:
        print(f"\nOptimization suggestions:")
        for opt in optimizations:
            print(f"  - {opt['description']}: save ${opt['savings']:.4f}")