import asyncio
import json
import logging
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple

import aiohttp
import openai

from .ai_responder import AIResponder, async_cache_to_file, exponential_backoff, pp
from .igdblib import IGDBQuery
from .leonardo_draw import LeonardoAIDrawMixIn


@async_cache_to_file("openai_chat.dat")
async def openai_chat(client, *args, **kwargs):
    return await client.chat.completions.create(*args, **kwargs)


@async_cache_to_file("openai_chat.dat")
async def openai_image(client, *args, **kwargs):
    response = await client.images.generate(*args, **kwargs)
    async with aiohttp.ClientSession() as session:
        async with session.get(response.data[0].url) as image:
            return BytesIO(await image.read())


class OpenAIResponder(AIResponder, LeonardoAIDrawMixIn):
    def __init__(self, config: Dict[str, Any], channel: Optional[str] = None) -> None:
        super().__init__(config, channel)
        self.client = openai.AsyncOpenAI(api_key=self.config.get("openai-token", self.config.get("openai-key", "")))

        # Initialize IGDB if enabled
        self.igdb = None
        logging.info("IGDB Configuration Check:")
        logging.info(f"  enable-game-info: {self.config.get('enable-game-info', 'NOT SET')}")
        logging.info(f"  igdb-client-id: {'SET' if self.config.get('igdb-client-id') else 'NOT SET'}")
        logging.info(f"  igdb-access-token: {'SET' if self.config.get('igdb-access-token') else 'NOT SET'}")

        if self.config.get("enable-game-info", False) and self.config.get("igdb-client-id") and self.config.get("igdb-access-token"):
            try:
                self.igdb = IGDBQuery(self.config["igdb-client-id"], self.config["igdb-access-token"])
                logging.info("✅ IGDB integration SUCCESSFULLY enabled for game information")
                logging.info(f"   Client ID: {self.config['igdb-client-id'][:8]}...")
                logging.info(f"   Available functions: {len(self.igdb.get_openai_functions())}")
            except Exception as e:
                logging.error(f"❌ Failed to initialize IGDB: {e}")
                self.igdb = None
        else:
            logging.warning("❌ IGDB integration DISABLED - missing configuration or disabled in config")

    async def draw_openai(self, description: str) -> BytesIO:
        for _ in range(3):
            try:
                response = await openai_image(self.client, prompt=description, n=1, size="1024x1024", model="dall-e-3")
                logging.info(f"Drawed a picture with DALL-E on this description: {repr(description)}")
                return response
            except Exception as err:
                logging.warning(f"Failed to generate image {repr(description)}: {repr(err)}")
        raise RuntimeError(f"Failed to generate image {repr(description)} after multiple retries")

    async def chat(self, messages: List[Dict[str, Any]], limit: int) -> Tuple[Optional[Dict[str, Any]], int]:
        # Safety check for mock objects in tests
        if not isinstance(messages, list) or len(messages) == 0:
            logging.warning("Invalid messages format in chat method")
            return None, limit

        try:
            # Clean up any orphaned tool messages from previous conversations
            clean_messages = []
            for i, msg in enumerate(messages):
                if msg.get("role") == "tool":
                    # Skip tool messages that don't have a corresponding assistant message with tool_calls
                    if i == 0 or messages[i - 1].get("role") != "assistant" or not messages[i - 1].get("tool_calls"):
                        logging.debug(f"Removing orphaned tool message at position {i}")
                        continue
                clean_messages.append(msg)
            messages = clean_messages

            last_message_content = messages[-1]["content"]
            if isinstance(last_message_content, str):
                model = self.config["model"]
            elif "model-vision" in self.config:
                model = self.config["model-vision"]
            else:
                messages[-1]["content"] = messages[-1]["content"][0]["text"]
        except (KeyError, IndexError, TypeError) as e:
            logging.warning(f"Error accessing message content: {e}")
            return None, limit
        try:
            # Prepare function calls if IGDB is enabled
            chat_kwargs = {
                "model": model,
                "messages": messages,
            }

            if self.igdb and self.config.get("enable-game-info", False):
                try:
                    igdb_functions = self.igdb.get_openai_functions()
                    if igdb_functions and isinstance(igdb_functions, list):
                        chat_kwargs["tools"] = [{"type": "function", "function": func} for func in igdb_functions]
                        chat_kwargs["tool_choice"] = "auto"
                        logging.info(f"🎮 IGDB functions available to AI: {[f['name'] for f in igdb_functions]}")
                        logging.debug(f"   Full chat_kwargs with tools: {list(chat_kwargs.keys())}")
                except (TypeError, AttributeError) as e:
                    logging.warning(f"Error setting up IGDB functions: {e}")
            else:
                logging.debug(
                    "🎮 IGDB not available for this request (igdb={}, enabled={})".format(
                        self.igdb is not None, self.config.get("enable-game-info", False)
                    )
                )

            result = await openai_chat(self.client, **chat_kwargs)

            # Handle function calls if present
            message = result.choices[0].message

            # Log what we received from OpenAI
            logging.debug(f"📨 OpenAI Response: content={bool(message.content)}, has_tool_calls={hasattr(message, 'tool_calls')}")
            if hasattr(message, "tool_calls") and message.tool_calls:
                tool_names = [tc.function.name for tc in message.tool_calls]
                logging.info(f"🔧 OpenAI requested function calls: {tool_names}")

            # Check if we have function/tool calls and IGDB is enabled
            has_tool_calls = (
                hasattr(message, "tool_calls") and message.tool_calls and self.igdb and self.config.get("enable-game-info", False)
            )

            # Clean up any existing tool messages in the history to avoid conflicts
            if has_tool_calls:
                messages = [msg for msg in messages if msg.get("role") != "tool"]

            if has_tool_calls:
                logging.info(f"🎮 Processing {len(message.tool_calls)} IGDB function call(s)...")
                try:
                    # Process function calls - serialize tool_calls properly
                    tool_calls_data = []
                    for tc in message.tool_calls:
                        tool_calls_data.append(
                            {"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}}
                        )

                    messages.append({"role": "assistant", "content": message.content or "", "tool_calls": tool_calls_data})

                    # Execute function calls
                    for tool_call in message.tool_calls:
                        function_name = tool_call.function.name
                        function_args = json.loads(tool_call.function.arguments)

                        logging.info(f"🎮 Executing IGDB function: {function_name} with args: {function_args}")

                        # Execute IGDB function
                        function_result = await self._execute_igdb_function(function_name, function_args)

                        logging.info(f"🎮 IGDB function result: {type(function_result)} - {str(function_result)[:200]}...")

                        messages.append(
                            {
                                "role": "tool",
                                "tool_call_id": tool_call.id,
                                "content": json.dumps(function_result) if function_result else "No results found",
                            }
                        )

                    # Get final response after function execution - remove tools for final call
                    final_chat_kwargs = {
                        "model": model,
                        "messages": messages,
                    }
                    logging.debug(f"🔧 Sending final request to OpenAI with {len(messages)} messages (no tools)")
                    logging.debug(f"🔧 Last few messages: {messages[-3:] if len(messages) > 3 else messages}")

                    final_result = await openai_chat(self.client, **final_chat_kwargs)
                    answer_obj = final_result.choices[0].message

                    logging.debug(
                        f"🔧 Final OpenAI response: content_length={len(answer_obj.content) if answer_obj.content else 0}, has_tool_calls={hasattr(answer_obj, 'tool_calls') and answer_obj.tool_calls}"
                    )
                    if answer_obj.content:
                        logging.debug(f"🔧 Response preview: {answer_obj.content[:200]}")
                    else:
                        logging.warning(f"🔧 OpenAI returned NULL content despite {final_result.usage.completion_tokens} completion tokens")

                    # If OpenAI returns null content after function calling, use empty string
                    if not answer_obj.content and function_result:
                        logging.warning("OpenAI returned null after function calling, using empty string")
                        answer_obj.content = ""
                except Exception as e:
                    # If function calling fails, fall back to regular response
                    logging.warning(f"Function calling failed, using regular response: {e}")
                    answer_obj = message
            else:
                answer_obj = message

            # Handle null content from OpenAI
            content = answer_obj.content
            if content is None:
                logging.warning("OpenAI returned null content, using empty string")
                content = ""

            answer = {"content": content, "role": answer_obj.role}
            self.rate_limit_backoff = exponential_backoff()
            logging.info(f"generated response {result.usage}: {repr(answer)}")
            return answer, limit
        except openai.BadRequestError as err:
            if "maximum context length is" in str(err) and limit > 4:
                logging.warning(f"context length exceeded, reduce the limit {limit}: {str(err)}")
                limit -= 1
                return None, limit
            raise err
        except openai.RateLimitError as err:
            rate_limit_sleep = next(self.rate_limit_backoff)
            if "retry-model" in self.config:
                model = self.config["retry-model"]
            logging.warning(f"got an rate limit error, sleep for {rate_limit_sleep} seconds: {str(err)}")
            await asyncio.sleep(rate_limit_sleep)
        except Exception as err:
            import traceback

            logging.warning(f"failed to generate response: {repr(err)}")
            logging.debug(f"Full traceback: {traceback.format_exc()}")
        return None, limit

    async def fix(self, answer: str) -> str:
        if "fix-model" not in self.config:
            return answer

        # Handle null/empty answer
        if not answer:
            logging.warning("Fix called with null/empty answer")
            return '{"answer": "I apologize, I encountered an error processing your request.", "answer_needed": true, "channel": null, "staff": null, "picture": null, "picture_edit": false, "hack": false}'
        messages = [{"role": "system", "content": self.config["fix-description"]}, {"role": "user", "content": answer}]
        try:
            result = await openai_chat(self.client, model=self.config["fix-model"], messages=messages)
            logging.info(f"got this message as fix:\n{pp(result.choices[0].message.content)}")
            response = result.choices[0].message.content
            start, end = response.find("{"), response.rfind("}")
            if start == -1 or end == -1 or (start + 3) >= end:
                return answer
            response = response[start : end + 1]
            logging.info(f"fixed answer:\n{pp(response)}")
            return response
        except Exception as err:
            logging.warning(f"failed to execute a fix for the answer: {repr(err)}")
        return answer

    async def translate(self, text: str, language: str = "english") -> str:
        if "fix-model" not in self.config:
            return text
        message = [
            {
                "role": "system",
                "content": f"You are an professional translator to {language} language,"
                f" you translate everything you get directly to {language}"
                f" if it is not already in {language}, otherwise you just copy it.",
            },
            {"role": "user", "content": text},
        ]
        try:
            result = await openai_chat(self.client, model=self.config["fix-model"], messages=message)
            response = result.choices[0].message.content
            logging.info(f"got this translated message:\n{pp(response)}")
            return response
        except Exception as err:
            logging.warning(f"failed to translate the text: {repr(err)}")
        return text

    async def memory_rewrite(self, memory: str, message_user: str, answer_user: str, question: str, answer: str) -> str:
        if "memory-model" not in self.config:
            return memory
        messages = [
            {"role": "system", "content": self.config.get("memory-system", "You are an memory assistant.")},
            {
                "role": "user",
                "content": f"Here is my previous memory:\n```\n{memory}\n```\n\n"
                f"Here is my conversanion:\n```\n{message_user}: {question}\n\n{answer_user}: {answer}\n```\n\n"
                f"Please rewrite the memory in a way, that it contain the content mentioned in conversation. "
                f"Summarize the memory if required, try to keep important information. "
                f"Write just new memory data without any comments.",
            },
        ]
        logging.info(f"Rewrite memory:\n{pp(messages)}")
        try:
            # logging.info(f'send this memory request:\n{pp(messages)}')
            result = await openai_chat(self.client, model=self.config["memory-model"], messages=messages)
            new_memory = result.choices[0].message.content
            logging.info(f"new memory:\n{new_memory}")
            return new_memory
        except Exception as err:
            logging.warning(f"failed to create new memory: {repr(err)}")
        return memory

    async def _execute_igdb_function(self, function_name: str, function_args: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Execute IGDB function calls from OpenAI.
        """
        logging.info(f"🎮 _execute_igdb_function called: {function_name}")

        if not self.igdb:
            logging.error("🎮 IGDB function called but self.igdb is None!")
            return {"error": "IGDB not available"}

        try:
            if function_name == "search_games":
                query = function_args.get("query", "")
                limit = function_args.get("limit", 5)

                logging.info(f"🎮 Searching IGDB for: '{query}' (limit: {limit})")

                if not query:
                    logging.warning("🎮 No search query provided to search_games")
                    return {"error": "No search query provided"}

                results = self.igdb.search_games(query, limit)
                logging.info(f"🎮 IGDB search returned: {len(results) if results and isinstance(results, list) else 0} results")

                if results and isinstance(results, list) and len(results) > 0:
                    return {"games": results}
                else:
                    return {"games": [], "message": f"No games found matching '{query}'"}

            elif function_name == "get_game_details":
                game_id = function_args.get("game_id")

                logging.info(f"🎮 Getting IGDB details for game ID: {game_id}")

                if not game_id:
                    logging.warning("🎮 No game ID provided to get_game_details")
                    return {"error": "No game ID provided"}

                result = self.igdb.get_game_details(game_id)
                logging.info(f"🎮 IGDB game details returned: {bool(result)}")

                if result:
                    return {"game": result}
                else:
                    return {"error": f"Game with ID {game_id} not found"}
            else:
                return {"error": f"Unknown function: {function_name}"}

        except Exception as e:
            logging.error(f"Error executing IGDB function {function_name}: {e}")
            return {"error": f"Failed to execute {function_name}: {str(e)}"}