"""Token counting utilities for monitoring LLM usage."""

import tiktoken


def count_tokens(text: str, model: str = "gpt-3.5-turbo") -> int:
    """
    Count tokens in a text string using tiktoken.

    Args:
        text: Text to count tokens for
        model: Model name for tokenizer (default: gpt-3.5-turbo)

    Returns:
        Number of tokens in the text
    """
    try:
        encoding = tiktoken.encoding_for_model(model)
        return len(encoding.encode(text))
    except Exception as e:
        # Fallback: rough estimate (1 token ≈ 4 characters)
        print(f"Token counting error: {e}, using fallback")
        return len(text) // 4


def log_token_usage(
    conversation_id: str,
    system_tokens: int,
    history_tokens: int,
    user_tokens: int,
    total_tokens: int,
    output_mode: str,
) -> None:
    """
    Log token usage for monitoring and optimization.

    Args:
        conversation_id: UUID of the conversation
        system_tokens: Tokens in system context
        history_tokens: Tokens in message history
        user_tokens: Tokens in current user message
        total_tokens: Total tokens in prompt
        output_mode: Current output mode
    """
    log_entry = (
        f"[TOKEN_USAGE] conv={conversation_id[:8]} | "
        f"mode={output_mode} | "
        f"system={system_tokens} | "
        f"history={history_tokens} | "
        f"user={user_tokens} | "
        f"total={total_tokens}"
    )
    print(log_entry)