/**
 * OpenRouter AI provider implementation
 */

import OpenAI from "openai";
import type {
  ChatCompletionMessageParam,
  ChatCompletionMessageToolCall,
  ChatCompletionTool,
} from "openai/resources/chat/completions";
import { config } from "../../core/config";
import { createLogger } from "../../core/logger";
import type { AiProvider, AiResponse, AskOptions, AskWithToolsOptions, MessageStyle, TextStreamHandler } from "./types";
import { MEMORY_EXTRACTION_TOOLS, getToolsForContext, type ToolCall, type ToolContext } from "./tools";
import { executeTools } from "./tool-handlers";

const logger = createLogger("AI:OpenRouter");

// Style classification options
const STYLE_OPTIONS: MessageStyle[] = ["story", "snarky", "insult", "explicit", "helpful"];

// Maximum tool call iterations to prevent infinite loops
const MAX_TOOL_ITERATIONS = 5;

interface StreamedToolCall {
  id: string;
  type: "function";
  function: {
    name: string;
    arguments: string;
  };
}

interface StreamedCompletionResult {
  text: string;
  toolCalls: StreamedToolCall[];
}

export class OpenRouterProvider implements AiProvider {
  private client: OpenAI;

  constructor() {
    this.client = new OpenAI({
      baseURL: "https://openrouter.ai/api/v1",
      apiKey: config.ai.openRouterApiKey,
      defaultHeaders: {
        "HTTP-Referer": "https://github.com/crunk-bun",
        "X-Title": "Joel Discord Bot",
      },
    });
  }

  async health(): Promise<boolean> {
    try {
      // Simple health check - verify we can list models
      await this.client.models.list();
      return true;
    } catch (error) {
      logger.error("Health check failed", error);
      return false;
    }
  }

  private getClassificationModelCandidates(): string[] {
    const models = [
      config.ai.classificationModel,
      ...config.ai.classificationFallbackModels,
      config.ai.model,
    ];

    return Array.from(new Set(models.map((model) => model.trim()).filter((model) => model.length > 0)));
  }

  private getErrorStatus(error: unknown): number | undefined {
    const err = error as {
      status?: number;
      code?: number | string;
      error?: { code?: number | string };
    };

    const code = typeof err.code === "number"
      ? err.code
      : typeof err.error?.code === "number"
        ? err.error.code
        : undefined;

    return err.status ?? code;
  }

  async ask(options: AskOptions): Promise<AiResponse> {
    const { prompt, systemPrompt, maxTokens, temperature, onTextStream } = options;
    const model = config.ai.model;

    try {
      if (onTextStream) {
        const streamed = await this.streamChatCompletion({
          model,
          messages: [
            { role: "system", content: systemPrompt },
            { role: "user", content: prompt },
          ],
          max_tokens: maxTokens ?? config.ai.maxTokens,
          temperature: temperature ?? config.ai.temperature,
        }, onTextStream);

        return { text: streamed.text };
      }

      const completion = await this.client.chat.completions.create({
        model,
        messages: [
          { role: "system", content: systemPrompt },
          { role: "user", content: prompt },
        ],
        max_tokens: maxTokens ?? config.ai.maxTokens,
        temperature: temperature ?? config.ai.temperature,
      });

      const text = completion.choices[0]?.message?.content ?? "";
      return { text };
    } catch (error: unknown) {
      logger.error("Failed to generate response (ask)", {
        method: "ask",
        model,
        promptLength: prompt.length,
        ...(error instanceof Error ? {} : { rawError: error }),
      });
      logger.error("API error details", error);
      throw error;
    }
  }

  /**
   * Generate a response with tool calling support
   * The AI can call tools (like looking up memories) during response generation
   */
  async askWithTools(options: AskWithToolsOptions): Promise<AiResponse> {
    const { prompt, systemPrompt, context, maxTokens, temperature, onTextStream } = options;

    const messages: ChatCompletionMessageParam[] = [
      { role: "system", content: systemPrompt },
      { role: "user", content: prompt },
    ];

    // Get the appropriate tools for this context (includes optional tools like GIF search)
    const tools = getToolsForContext(context);

    let iterations = 0;

    while (iterations < MAX_TOOL_ITERATIONS) {
      iterations++;

      try {
        if (onTextStream) {
          const streamed = await this.streamChatCompletion({
            model: config.ai.model,
            messages,
            tools,
            tool_choice: "auto",
            max_tokens: maxTokens ?? config.ai.maxTokens,
            temperature: temperature ?? config.ai.temperature,
          }, onTextStream);

          if (streamed.toolCalls.length > 0) {
            logger.debug("AI requested tool calls", {
              count: streamed.toolCalls.length,
              tools: streamed.toolCalls.map((tc) => tc.function.name),
            });

            messages.push({
              role: "assistant",
              content: streamed.text || null,
              tool_calls: streamed.toolCalls,
            });

            await onTextStream("");

            const toolCalls = this.parseToolCalls(streamed.toolCalls);
            const results = await executeTools(toolCalls, context);

            for (let i = 0; i < toolCalls.length; i++) {
              messages.push({
                role: "tool",
                tool_call_id: toolCalls[i].id,
                content: results[i].result,
              });
            }

            continue;
          }

          logger.debug("AI response generated", {
            iterations,
            textLength: streamed.text.length,
            streamed: true,
          });

          return { text: streamed.text };
        }

        const completion = await this.client.chat.completions.create({
          model: config.ai.model,
          messages,
          tools,
          tool_choice: "auto",
          max_tokens: maxTokens ?? config.ai.maxTokens,
          temperature: temperature ?? config.ai.temperature,
        });

        const choice = completion.choices[0];
        const message = choice?.message;

        if (!message) {
          logger.warn("No message in completion");
          return { text: "" };
        }

        // Check if the AI wants to call tools
        if (message.tool_calls && message.tool_calls.length > 0) {
          logger.debug("AI requested tool calls", { 
            count: message.tool_calls.length,
            tools: message.tool_calls.map(tc => tc.function.name)
          });

          // Add the assistant's message with tool calls
          messages.push(message);

          // Parse and execute tool calls
          const toolCalls: ToolCall[] = message.tool_calls.map((tc) => ({
            id: tc.id,
            name: tc.function.name,
            arguments: JSON.parse(tc.function.arguments || "{}"),
          }));

          const results = await executeTools(toolCalls, context);

          // Add tool results as messages
          for (let i = 0; i < toolCalls.length; i++) {
            messages.push({
              role: "tool",
              tool_call_id: toolCalls[i].id,
              content: results[i].result,
            });
          }

          // Continue the loop to get the AI's response after tool execution
          continue;
        }

        // No tool calls - we have a final response
        const text = message.content ?? "";
        logger.debug("AI response generated", { 
          iterations, 
          textLength: text.length 
        });

        return { text };
      } catch (error: unknown) {
        logger.error("Failed to generate response with tools (askWithTools)", {
          method: "askWithTools",
          model: config.ai.model,
          iteration: iterations,
          messageCount: messages.length,
          toolCount: tools.length,
          ...(error instanceof Error ? {} : { rawError: error }),
        });
        logger.error("API error details", error);
        throw error;
      }
    }

    logger.warn("Max tool iterations reached");
    return { text: "I got stuck in a loop thinking about that..." };
  }

  private async streamChatCompletion(
    params: {
      model: string;
      messages: ChatCompletionMessageParam[];
      tools?: ChatCompletionTool[];
      tool_choice?: "auto" | "none";
      max_tokens: number;
      temperature: number;
    },
    onTextStream: TextStreamHandler,
  ): Promise<StreamedCompletionResult> {
    const stream = await this.client.chat.completions.create({
      ...params,
      stream: true,
    });

    let text = "";
    const toolCalls = new Map<number, StreamedToolCall>();

    for await (const chunk of stream) {
      const choice = chunk.choices[0];
      if (!choice) {
        continue;
      }

      const delta = choice.delta;
      const content = delta.content ?? "";
      if (content) {
        text += content;
        await onTextStream(text);
      }

      for (const toolCallDelta of delta.tool_calls ?? []) {
        const current = toolCalls.get(toolCallDelta.index) ?? {
          id: "",
          type: "function" as const,
          function: {
            name: "",
            arguments: "",
          },
        };

        if (toolCallDelta.id) {
          current.id = toolCallDelta.id;
        }

        if (toolCallDelta.function?.name) {
          current.function.name = toolCallDelta.function.name;
        }

        if (toolCallDelta.function?.arguments) {
          current.function.arguments += toolCallDelta.function.arguments;
        }

        toolCalls.set(toolCallDelta.index, current);
      }
    }

    return {
      text,
      toolCalls: Array.from(toolCalls.entries())
        .sort((a, b) => a[0] - b[0])
        .map(([, toolCall]) => toolCall),
    };
  }

  private parseToolCalls(toolCalls: ChatCompletionMessageToolCall[]): ToolCall[] {
    return toolCalls.map((toolCall) => {
      try {
        return {
          id: toolCall.id,
          name: toolCall.function.name,
          arguments: JSON.parse(toolCall.function.arguments || "{}"),
        };
      } catch (error) {
        logger.error("Failed to parse streamed tool call arguments", {
          toolName: toolCall.function.name,
          toolCallId: toolCall.id,
          arguments: toolCall.function.arguments,
          error,
        });
        throw error;
      }
    });
  }

  /**
   * Analyze a message to extract memorable information
   */
  async extractMemories(
    message: string,
    authorName: string,
    context: ToolContext
  ): Promise<void> {
    const systemPrompt = `You are analyzing a Discord message to determine if it contains any memorable or useful information about the user "${authorName}".

Look for:
- Personal information (name, age, location, job, hobbies)
- Preferences (likes, dislikes, favorites)
- Embarrassing admissions or confessions
- Strong opinions or hot takes
- Achievements or accomplishments
- Relationships or social information
- Recurring patterns or habits

If you find something worth remembering, use the extract_memory tool. Only extract genuinely interesting or useful information - don't save trivial things.

The user's Discord ID is: ${context.userId}`;

    try {
      const completion = await this.client.chat.completions.create({
        model: config.ai.model, // Use main model - needs tool support
        messages: [
          { role: "system", content: systemPrompt },
          { role: "user", content: `Analyze this message for memorable content:\n\n"${message}"` },
        ],
        tools: MEMORY_EXTRACTION_TOOLS,
        tool_choice: "auto",
        max_tokens: 200,
        temperature: 0.3,
      });

      const toolCalls = completion.choices[0]?.message?.tool_calls;

      if (toolCalls && toolCalls.length > 0) {
        const parsedCalls: ToolCall[] = toolCalls.map((tc) => ({
          id: tc.id,
          name: tc.function.name,
          arguments: JSON.parse(tc.function.arguments || "{}"),
        }));

        await executeTools(parsedCalls, context);
        logger.debug("Memory extraction complete", { 
          extracted: parsedCalls.length,
          authorName 
        });
      }
    } catch (error) {
      // Don't throw - memory extraction is non-critical
      logger.error("Memory extraction failed", {
        method: "extractMemories",
        model: config.ai.model,
        authorName,
        messageLength: message.length,
      });
      logger.error("Memory extraction error details", error);
    }
  }

  /**
   * Classify a message to determine the appropriate response style
   */
  async classifyMessage(message: string): Promise<MessageStyle> {
    const models = this.getClassificationModelCandidates();

    for (let i = 0; i < models.length; i++) {
      const model = models[i];
      const hasNextModel = i < models.length - 1;

      try {
        const classification = await this.client.chat.completions.create({
          model,
          messages: [
            {
              role: "user",
              content: `Classify this message into exactly one category. Only respond with the category name, nothing else.

Message: "${message}"

Categories:
- story: User wants a story, narrative, or creative writing
- snarky: User is being sarcastic or deserves a witty comeback
- insult: User is being rude or hostile, respond with brutal roasts (non-sexual)
- explicit: User wants adult/NSFW content
- helpful: User has a genuine question or needs actual help

Category:`,
            },
          ],
          max_tokens: 10,
          temperature: 0.1,
        });

        const result = classification.choices[0]?.message?.content?.toLowerCase().trim() as MessageStyle;

        if (STYLE_OPTIONS.includes(result)) {
          logger.debug("Message classified", { style: result, model });
          return result;
        }

        logger.debug("Classification returned invalid style, defaulting to snarky", { result, model });
        return "snarky";
      } catch (error) {
        if (hasNextModel) {
          logger.warn("Classification model failed, trying fallback", {
            method: "classifyMessage",
            model,
            nextModel: models[i + 1],
            status: this.getErrorStatus(error),
          });
          continue;
        }

        logger.error("Failed to classify message", {
          method: "classifyMessage",
          model,
          messageLength: message.length,
          attempts: models.length,
        });
        logger.error("Classification error details", error);
      }
    }

    return "snarky";
  }

  /**
   * Cheap binary classifier to detect if a message is directed at Joel
   */
  async classifyJoelDirected(message: string): Promise<boolean> {
    const models = this.getClassificationModelCandidates();

    for (let i = 0; i < models.length; i++) {
      const model = models[i];
      const hasNextModel = i < models.length - 1;

      try {
        const classification = await this.client.chat.completions.create({
          model,
          messages: [
            {
              role: "user",
              content: `Determine if this Discord message is directed at Joel (the bot), or talking about Joel in a way Joel should respond.

Only respond with one token: YES or NO.

Guidance:
- YES if the user is asking Joel a question, requesting Joel to do something, replying conversationally to Joel, or maybe discussing Joel as a participant.
- NO if it's general chat between humans, statements that do not involve Joel.

Message: "${message}"

Answer:`,
            },
          ],
          max_tokens: 3,
          temperature: 0,
        });

        const result = classification.choices[0]?.message?.content?.trim().toUpperCase();
        return result?.startsWith("YES") ?? false;
      } catch (error) {
        if (hasNextModel) {
          logger.warn("Directed classification model failed, trying fallback", {
            method: "classifyJoelDirected",
            model,
            nextModel: models[i + 1],
            status: this.getErrorStatus(error),
          });
          continue;
        }

        logger.error("Failed to classify directed message", {
          method: "classifyJoelDirected",
          model,
          messageLength: message.length,
          attempts: models.length,
        });
        logger.error("Directed classification error details", error);
      }
    }

    return false;
  }
}