import type { AssistantMessage } from "../types.js";

/**
 * Regex patterns to detect context overflow errors from different providers.
 *
 * These patterns match error messages returned when the input exceeds
 * the model's context window.
 *
 * Provider-specific patterns (with example error messages):
 *
 * - Anthropic: "prompt is too long: 213462 tokens > 200000 maximum"
 * - OpenAI: "Your input exceeds the context window of this model"
 * - Google: "The input token count (1196265) exceeds the maximum number of tokens allowed (1048575)"
 * - xAI: "This model's maximum prompt length is 131072 but the request contains 537812 tokens"
 * - Groq: "Please reduce the length of the messages or completion"
 * - OpenRouter: "This endpoint's maximum context length is X tokens. However, you requested about Y tokens"
 * - llama.cpp: "the request exceeds the available context size, try increasing it"
 * - LM Studio: "tokens to keep from the initial prompt is greater than the context length"
 * - GitHub Copilot: "prompt token count of X exceeds the limit of Y"
 * - MiniMax: "invalid params, context window exceeds limit"
 * - Kimi For Coding: "Your request exceeded model token limit: X (requested: Y)"
 * - Cerebras: Returns "400/413 status code (no body)" - handled separately below
 * - Mistral: "Prompt contains X tokens ... too large for model with Y maximum context length"
 * - z.ai: Does NOT error, accepts overflow silently - handled via usage.input > contextWindow
 * - Ollama: Silently truncates input - not detectable via error message
 */
const OVERFLOW_PATTERNS = [
  /prompt is too long/i, // Anthropic
  /input is too long for requested model/i, // Amazon Bedrock
  /exceeds the context window/i, // OpenAI (Completions & Responses API)
  /input token count.*exceeds the maximum/i, // Google (Gemini)
  /maximum prompt length is \d+/i, // xAI (Grok)
  /reduce the length of the messages/i, // Groq
  /maximum context length is \d+ tokens/i, // OpenRouter (all backends)
  /exceeds the limit of \d+/i, // GitHub Copilot
  /exceeds the available context size/i, // llama.cpp server
  /greater than the context length/i, // LM Studio
  /context window exceeds limit/i, // MiniMax
  /exceeded model token limit/i, // Kimi For Coding
  /too large for model with \d+ maximum context length/i, // Mistral
  /context[_ ]length[_ ]exceeded/i, // Generic fallback
  /too many tokens/i, // Generic fallback
  /token limit exceeded/i, // Generic fallback
];

/**
 * Check if an assistant message represents a context overflow error.
 *
 * This handles two cases:
 * 1. Error-based overflow: Most providers return stopReason "error" with a
 *    specific error message pattern.
 * 2. Silent overflow: Some providers accept overflow requests and return
 *    successfully. For these, we check if usage.input exceeds the context window.
 *
 * ## Reliability by Provider
 *
 * **Reliable detection (returns error with detectable message):**
 * - Anthropic: "prompt is too long: X tokens > Y maximum"
 * - OpenAI (Completions & Responses): "exceeds the context window"
 * - Google Gemini: "input token count exceeds the maximum"
 * - xAI (Grok): "maximum prompt length is X but request contains Y"
 * - Groq: "reduce the length of the messages"
 * - Cerebras: 400/413 status code (no body)
 * - Mistral: "Prompt contains X tokens ... too large for model with Y maximum context length"
 * - OpenRouter (all backends): "maximum context length is X tokens"
 * - llama.cpp: "exceeds the available context size"
 * - LM Studio: "greater than the context length"
 * - Kimi For Coding: "exceeded model token limit: X (requested: Y)"
 *
 * **Unreliable detection:**
 * - z.ai: Sometimes accepts overflow silently (detectable via usage.input > contextWindow),
 *   sometimes returns rate limit errors. Pass contextWindow param to detect silent overflow.
 * - Ollama: Silently truncates input without error. Cannot be detected via this function.
 *   The response will have usage.input < expected, but we don't know the expected value.
 *
 * ## Custom Providers
 *
 * If you've added custom models via settings.json, this function may not detect
 * overflow errors from those providers. To add support:
 *
 * 1. Send a request that exceeds the model's context window
 * 2. Check the errorMessage in the response
 * 3. Create a regex pattern that matches the error
 * 4. The pattern should be added to OVERFLOW_PATTERNS in this file, or
 *    check the errorMessage yourself before calling this function
 *
 * @param message - The assistant message to check
 * @param contextWindow - Optional context window size for detecting silent overflow (z.ai)
 * @returns true if the message indicates a context overflow
 */
export function isContextOverflow(
  message: AssistantMessage,
  contextWindow?: number,
): boolean {
  // Case 1: Check error message patterns
  if (message.stopReason === "error" && message.errorMessage) {
    // Check known patterns
    if (OVERFLOW_PATTERNS.some((p) => p.test(message.errorMessage!))) {
      return true;
    }

    // Cerebras returns 400/413 with no body for context overflow
    // Note: 429 is rate limiting (requests/tokens per time), NOT context overflow
    if (
      /^4(00|13)\s*(status code)?\s*\(no body\)/i.test(message.errorMessage)
    ) {
      return true;
    }
  }

  // Case 2: Silent overflow (z.ai style) - successful but usage exceeds context
  if (contextWindow && message.stopReason === "stop") {
    const inputTokens = message.usage.input + message.usage.cacheRead;
    if (inputTokens > contextWindow) {
      return true;
    }
  }

  return false;
}

/**
 * Get the overflow patterns for testing purposes.
 */
export function getOverflowPatterns(): RegExp[] {
  return [...OVERFLOW_PATTERNS];
}