import OpenAI from "openai";
import { encoding_for_model } from "@dqbd/tiktoken";
import { TiktokenModel } from "@dqbd/tiktoken";
import { Document, ExtractOptions } from "../../../controllers/v1/types";
import { Logger } from "winston";
import { EngineResultsTracker, Meta } from "..";

const maxTokens = 32000;
const modifier = 4;

export class LLMRefusalError extends Error {
  public refusal: string;
  public results: EngineResultsTracker | undefined;

  constructor(refusal: string) {
    super("LLM refused to extract the website's content");
    this.refusal = refusal;
  }
}

function normalizeSchema(x: any): any {
  if (typeof x !== "object" || x === null) return x;

  if (x["$defs"] !== null && typeof x["$defs"] === "object") {
    x["$defs"] = Object.fromEntries(
      Object.entries(x["$defs"]).map(([name, schema]) => [
        name,
        normalizeSchema(schema),
      ]),
    );
  }

  if (x && x.anyOf) {
    x.anyOf = x.anyOf.map((x) => normalizeSchema(x));
  }

  if (x && x.oneOf) {
    x.oneOf = x.oneOf.map((x) => normalizeSchema(x));
  }

  if (x && x.allOf) {
    x.allOf = x.allOf.map((x) => normalizeSchema(x));
  }

  if (x && x.not) {
    x.not = normalizeSchema(x.not);
  }

  if (x && x.type === "object") {
    return {
      ...x,
      properties: Object.fromEntries(
        Object.entries(x.properties).map(([k, v]) => [k, normalizeSchema(v)]),
      ),
      required: Object.keys(x.properties),
      additionalProperties: false,
    };
  } else if (x && x.type === "array") {
    return {
      ...x,
      items: normalizeSchema(x.items),
    };
  } else {
    return x;
  }
}

export async function generateOpenAICompletions(
  logger: Logger,
  options: ExtractOptions,
  markdown?: string,
  previousWarning?: string,
  isExtractEndpoint?: boolean,
): Promise<{ extract: any; numTokens: number; warning: string | undefined }> {
  let extract: any;
  let warning: string | undefined;

  const openai = new OpenAI();
  const model: TiktokenModel =
    (process.env.MODEL_NAME as TiktokenModel) ?? "gpt-4o-mini";

  if (markdown === undefined) {
    throw new Error("document.markdown is undefined -- this is unexpected");
  }

  // count number of tokens
  let numTokens = 0;
  const encoder = encoding_for_model(model as TiktokenModel);
  try {
    // Encode the message into tokens
    const tokens = encoder.encode(markdown);

    // Return the number of tokens
    numTokens = tokens.length;
  } catch (error) {
    logger.warn("Calculating num tokens of string failed", { error, markdown });

    markdown = markdown.slice(0, maxTokens * modifier);

    let w =
      "Failed to derive number of LLM tokens the extraction might use -- the input has been automatically trimmed to the maximum number of tokens (" +
      maxTokens +
      ") we support.";
    warning = previousWarning === undefined ? w : w + " " + previousWarning;
  } finally {
    // Free the encoder resources after use
    encoder.free();
  }

  if (numTokens > maxTokens) {
    // trim the document to the maximum number of tokens, tokens != characters
    markdown = markdown.slice(0, maxTokens * modifier);

    const w =
      "The extraction content would have used more tokens (" +
      numTokens +
      ") than the maximum we allow (" +
      maxTokens +
      "). -- the input has been automatically trimmed.";
    warning = previousWarning === undefined ? w : w + " " + previousWarning;
  }

  let schema = options.schema;
  if (schema) {
    schema = removeDefaultProperty(schema);
  }

  if (schema && schema.type === "array") {
    schema = {
      type: "object",
      properties: {
        items: options.schema,
      },
      required: ["items"],
      additionalProperties: false,
    };
  } else if (schema && typeof schema === "object" && !schema.type) {
    schema = {
      type: "object",
      properties: Object.fromEntries(
        Object.entries(schema).map(([key, value]) => {
          return [key, removeDefaultProperty(value)];
        }),
      ),
      required: Object.keys(schema),
      additionalProperties: false,
    };
  }

  schema = normalizeSchema(schema);

  const jsonCompletion = await openai.beta.chat.completions.parse({
    model,
    temperature: 0,
    messages: [
      {
        role: "system",
        content: options.systemPrompt,
      },
      {
        role: "user",
        content: [{ type: "text", text: markdown }],
      },
      {
        role: "user",
        content:
          options.prompt !== undefined
            ? `Transform the above content into structured JSON output based on the provided schema if any and the following user request: ${options.prompt}. If schema is provided, strictly follow it.`
            : "Transform the above content into structured JSON output based on the provided schema if any.",
      },
    ],
    response_format: options.schema
      ? {
          type: "json_schema",
          json_schema: {
            name: "websiteContent",
            schema: schema,
            strict: true,
          },
        }
      : { type: "json_object" },
  });

  if (jsonCompletion.choices[0].message.refusal !== null) {
    throw new LLMRefusalError(jsonCompletion.choices[0].message.refusal);
  }

  extract = jsonCompletion.choices[0].message.parsed;

  if (extract === null && jsonCompletion.choices[0].message.content !== null) {
    try {
      if (!isExtractEndpoint) {
        extract = JSON.parse(jsonCompletion.choices[0].message.content);
      } else {
        const extractData = JSON.parse(
          jsonCompletion.choices[0].message.content,
        );
        extract = options.schema ? extractData.data.extract : extractData;
      }
    } catch (e) {
      logger.error("Failed to parse returned JSON, no schema specified.", {
        error: e,
      });
      throw new LLMRefusalError(
        "Failed to parse returned JSON. Please specify a schema in the extract object.",
      );
    }
  }

  // If the users actually wants the items object, they can specify it as 'required' in the schema
  // otherwise, we just return the items array
  if (
    options.schema &&
    options.schema.type === "array" &&
    !schema?.required?.includes("items")
  ) {
    extract = extract?.items;
  }
  return { extract, warning, numTokens };
}

export async function performLLMExtract(
  meta: Meta,
  document: Document,
): Promise<Document> {
  if (meta.options.formats.includes("extract")) {
    const { extract, warning } = await generateOpenAICompletions(
      meta.logger.child({
        method: "performLLMExtract/generateOpenAICompletions",
      }),
      meta.options.extract!,
      document.markdown,
      document.warning,
    );
    document.extract = extract;
    document.warning = warning;
  }

  return document;
}

export function removeDefaultProperty(schema: any): any {
  if (typeof schema !== "object" || schema === null) return schema;

  const { default: _, ...rest } = schema;

  for (const key in rest) {
    if (Array.isArray(rest[key])) {
      rest[key] = rest[key].map((item: any) => removeDefaultProperty(item));
    } else if (typeof rest[key] === "object" && rest[key] !== null) {
      rest[key] = removeDefaultProperty(rest[key]);
    }
  }

  return rest;
}