"""
OpenAI-specific patching logic for LLM tracking.
"""

import functools
import time
import traceback
import json
import os
import uuid
import requests
from datetime import datetime
from loguru import logger

from .constants import (
    TOKEN_COUNT_KEYS,
    DEFAULT_TOKEN_COUNT
)
from .stack_tracing import format_stack_trace
from .transport import send_log
from .utils import (
    create_base_log_entry,
    create_generic_method_wrapper,
    get_user_id,
    process_template_matching,
)

def process_messages(messages):
    """Process OpenAI messages to handle special content types"""
    processed_messages = []
    for msg in messages:
        if isinstance(msg, dict) or hasattr(msg, "keys"):
            # Convert frozendict to dict if needed
            msg_dict = dict(msg) if not isinstance(msg, dict) else msg
            processed_msg = msg_dict.copy()
            content = msg_dict.get("content")

            # Handle list-type content (multimodal messages)
            if isinstance(content, list):
                processed_content = []
                for item in content:
                    if isinstance(item, dict):
                        item_copy = item.copy()
                        # Handle image types in OpenAI messages
                        if "image_url" in item_copy:
                            url = item_copy["image_url"].get("url", "")
                            if url and url.startswith("data:image"):
                                item_copy["image_url"]["url"] = "[BASE64_IMAGE_REMOVED]"
                        processed_content.append(item_copy)
                    else:
                        processed_content.append(item)
                processed_msg["content"] = processed_content
            elif isinstance(content, str):
                # Strip leading and trailing newlines from string content
                processed_msg["content"] = content.strip('\n')

            processed_messages.append(processed_msg)
        else:
            # Handle non-dict message objects
            try:
                # For OpenAI message objects
                content = getattr(msg, "content", str(msg))
                if isinstance(content, str):
                    content = content.strip('\n')
                
                processed_msg = {
                    "role": getattr(msg, "role", "unknown"),
                    "content": content
                }
                
                # Add tool calls if present
                if getattr(msg, "tool_calls", None):
                    if hasattr(msg.tool_calls, "model_dump"):
                        processed_msg["tool_calls"] = [t.model_dump() for t in msg.tool_calls]
                    else:
                        # Try to extract tool calls as dictionaries
                        tool_calls = []
                        for t in msg.tool_calls:
                            if hasattr(t, "__dict__"):
                                tool_calls.append(vars(t))
                            else:
                                tool_calls.append(str(t))
                        processed_msg["tool_calls"] = tool_calls
                
                # Add tool call ID if present (for tool results)
                if getattr(msg, "tool_call_id", None):
                    processed_msg["tool_call_id"] = msg.tool_call_id
                
                # Add name if present (for functions/tools)
                if getattr(msg, "name", None):
                    processed_msg["name"] = msg.name
                
                processed_messages.append(processed_msg)
            except Exception as e:
                # If all else fails, create a basic message
                logger.warning(f"Error processing message object: {e}")
                processed_messages.append({
                    "role": getattr(msg, "role", "unknown"),
                    "content": str(getattr(msg, "content", str(msg)))
                })

    return processed_messages

def count_tokens_openai(text, model):
    """Count tokens in text using tiktoken for OpenAI models"""
    try:
        import tiktoken
        encoding = tiktoken.encoding_for_model(model)
        return {
            TOKEN_COUNT_KEYS["PROMPT_TOKENS"]: len(encoding.encode(text)),
            TOKEN_COUNT_KEYS["COMPLETION_TOKENS"]: 0,
            TOKEN_COUNT_KEYS["TOTAL_TOKENS"]: len(encoding.encode(text))
        }
    except Exception as e:
        logger.warning(f"Failed to count tokens for OpenAI: {e}")
        return DEFAULT_TOKEN_COUNT

def log_openai_call(provider, request_args, response, duration, success):
    """Log an OpenAI API call according to the unified TROPIR schema."""
    try:
        # Get stack trace for template substitutions
        stack = traceback.extract_stack()
        stack_info = format_stack_trace(stack)

        # Extract messages from request_args
        messages = request_args.get("messages", [])
        processed_messages = process_messages(messages)
        
        # Create standardized request structure
        standardized_request = {
            "model": request_args.get("model", "unknown"),
            "messages": processed_messages,
            "temperature": request_args.get("temperature"),
            "max_tokens": request_args.get("max_tokens"),
            "top_p": request_args.get("top_p"),
            "frequency_penalty": request_args.get("frequency_penalty"),
            "presence_penalty": request_args.get("presence_penalty"),
            "stop": request_args.get("stop"),
            "n": request_args.get("n")
        }
        
        # Standardize tools format from functions or tools
        tools = []
        
        # Check if this is a function/tool call request
        if "functions" in request_args:
            for func in request_args.get("functions", []):
                standardized_tool = {
                    "name": func.get("name", ""),
                    "description": func.get("description", ""),
                    "parameters": func.get("parameters", {})
                }
                tools.append(standardized_tool)
                
            # Add tool_choice if function_call was specified
            if "function_call" in request_args:
                if isinstance(request_args["function_call"], dict):
                    standardized_request["tool_choice"] = {
                        "type": "function",
                        "function": {
                            "name": request_args["function_call"].get("name", "auto")
                        }
                    }
                else:
                    standardized_request["tool_choice"] = request_args["function_call"]
                    
        # Process tools field if it exists
        elif "tools" in request_args:
            tools = request_args.get("tools", [])
            if "tool_choice" in request_args:
                standardized_request["tool_choice"] = request_args["tool_choice"]
                
        # Add tools to request if we have any
        if tools:
            standardized_request["tools"] = tools
        
       

        response_text = ""
        usage = {}
        function_call_info = None
        model = standardized_request.get("model", "unknown")
        
        # Handle OpenAI responses
        if hasattr(response, "to_dict"):
            # Dictionary approach
            response_dict = response.to_dict()
            
            # Extract response text from choices
            if "choices" in response_dict and response_dict["choices"]:
                choice = response_dict["choices"][0]
                
                if "message" in choice and choice["message"]:
                    message = choice["message"]
                    
                    # Check for content
                    if "content" in message and message["content"] is not None:
                        response_text = message["content"]
                    
                    # Check for function call
                    if "function_call" in message and message["function_call"]:
                        func_call = message["function_call"]
                        name = func_call.get("name", "unknown_function")
                        args = func_call.get("arguments", "{}")
                        
                        # Store function call info in tool_calls format
                        function_call_info = {
                            "calls": [
                                {
                                    "id": f"call_{uuid.uuid4().hex[:8]}",
                                    "type": "function",
                                    "function": {
                                        "name": name,
                                        "arguments": args
                                    }
                                }
                            ]
                        }
                        
                        # Try to parse arguments
                        try:
                            args_obj = json.loads(args)
                            function_call_info["parsed_arguments"] = args_obj
                        except Exception:
                            function_call_info["parsed_arguments"] = {}
                        
                        # Format response text if content was None
                        if not response_text:
                            response_text = f"[FUNCTION_CALL: {name}({args})]"
                    
                    # Check for tool calls
                    elif "tool_calls" in message and message["tool_calls"]:
                        tool_calls = message["tool_calls"]
                        tool_call_details = []
                        parsed_args_combined = {}
                        
                        for tool in tool_calls:
                            tool_details = {
                                "id": tool.get("id", f"call_{uuid.uuid4().hex[:8]}"),
                                "type": tool.get("type", "function"),
                                "function": {
                                    "name": tool.get("function", {}).get("name", "unknown_function"),
                                    "arguments": tool.get("function", {}).get("arguments", "{}")
                                }
                            }
                            
                            # Try to parse arguments
                            try:
                                args_obj = json.loads(tool_details["function"]["arguments"])
                                # Store args for the first tool call or the one with most data
                                if not parsed_args_combined or len(args_obj) > len(parsed_args_combined):
                                    parsed_args_combined = args_obj
                            except Exception:
                                pass
                                
                            tool_call_details.append(tool_details)
                        
                        # Store all tool calls
                        function_call_info = {
                            "calls": tool_call_details,
                            "parsed_arguments": parsed_args_combined
                        }
                        
                        # Format response text if content was None
                        if not response_text:
                            response_text = f"[TOOL_CALLS: {len(message['tool_calls'])}]"
                
                elif "delta" in choice and choice["delta"]:
                    # Streaming response
                    if "content" in choice["delta"] and choice["delta"]["content"]:
                        response_text = choice["delta"]["content"]
                    else:
                        response_text = "[STREAMING_RESPONSE]"
            
            # Extract usage information
            if "usage" in response_dict:
                usage = response_dict["usage"]
        
        # Attribute-based approach if dictionary approach failed
        if not response_text and hasattr(response, "choices") and response.choices:
            if hasattr(response.choices[0], "message") and response.choices[0].message:
                message = response.choices[0].message
                
                # Check content
                if hasattr(message, "content") and message.content is not None:
                    response_text = message.content
                
                # Check function call
                if hasattr(message, "function_call") and message.function_call:
                    try:
                        func_call = message.function_call
                        name = getattr(func_call, "name", "unknown_function")
                        args = getattr(func_call, "arguments", "{}")
                        
                        # Store function call info in tool_calls format
                        function_call_info = {
                            "calls": [
                                {
                                    "id": f"call_{uuid.uuid4().hex[:8]}",
                                    "type": "function",
                                    "function": {
                                        "name": name,
                                        "arguments": args
                                    }
                                }
                            ]
                        }
                        
                        # Try to parse arguments
                        try:
                            if isinstance(args, str):
                                args_obj = json.loads(args)
                                function_call_info["parsed_arguments"] = args_obj
                            else:
                                function_call_info["parsed_arguments"] = {}
                        except Exception:
                            function_call_info["parsed_arguments"] = {}
                        
                        # Format response text if content was None
                        if not response_text:
                            response_text = f"[FUNCTION_CALL: {name}({args})]"
                    except Exception as e:
                        logger.error(f"Error extracting function call: {e}")
                
                # Check tool calls
                elif hasattr(message, "tool_calls") and message.tool_calls:
                    try:
                        tool_calls = message.tool_calls
                        tool_call_details = []
                        parsed_args_combined = {}
                        
                        for tool in tool_calls:
                            tool_details = {
                                "id": getattr(tool, "id", f"call_{uuid.uuid4().hex[:8]}"),
                                "type": getattr(tool, "type", "function"),
                            }
                            
                            if hasattr(tool, "function"):
                                func = tool.function
                                tool_details["function"] = {
                                    "name": getattr(func, "name", "unknown_function"),
                                    "arguments": getattr(func, "arguments", "{}")
                                }
                                
                                # Try to parse arguments
                                try:
                                    if isinstance(tool_details["function"]["arguments"], str):
                                        args_obj = json.loads(tool_details["function"]["arguments"])
                                        # Store args for the first tool call or the one with most data
                                        if not parsed_args_combined or len(args_obj) > len(parsed_args_combined):
                                            parsed_args_combined = args_obj
                                except Exception:
                                    pass
                            
                            tool_call_details.append(tool_details)
                        
                        # Store all tool calls in standardized format
                        function_call_info = {
                            "calls": tool_call_details,
                            "parsed_arguments": parsed_args_combined
                        }
                        
                        # Format response text if content was None
                        if not response_text:
                            response_text = f"[TOOL_CALLS: {len(tool_calls)}]"
                    except Exception as e:
                        logger.error(f"Error extracting tool calls: {e}")
            
            # Check for streaming response
            elif hasattr(response.choices[0], "delta") and response.choices[0].delta:
                if hasattr(response.choices[0].delta, "content") and response.choices[0].delta.content is not None:
                    response_text = response.choices[0].delta.content
                else:
                    response_text = "[STREAMING_RESPONSE]"

        # Extract usage if available
        if not usage and hasattr(response, "usage"):
            if hasattr(response.usage, "model_dump"):
                usage = response.usage.model_dump()
            else:
                try:
                    usage = vars(response.usage)
                except:
                    usage = {}

        # Count tokens if not provided
        if not usage:
            prompt_text = " ".join(str(msg.get("content", "")) for msg in messages if msg.get("content"))
            resp_text_str = str(response_text) if response_text else ""
            usage = count_tokens_openai(prompt_text + resp_text_str, model)
        
        # Standardize the usage structure with token_details
        standardized_usage = {
            "prompt_tokens": usage.get("prompt_tokens", 0),
            "completion_tokens": usage.get("completion_tokens", 0),
            "total_tokens": usage.get("total_tokens", 0),
            "token_details": {
                "cached_tokens": usage.get("prompt_tokens_details", {}).get("cached_tokens", None),
                "audio_tokens": (
                    usage.get("prompt_tokens_details", {}).get("audio_tokens", 0) + 
                    usage.get("completion_tokens_details", {}).get("audio_tokens", 0)
                ) or None,
                "reasoning_tokens": usage.get("completion_tokens_details", {}).get("reasoning_tokens", None),
                "accepted_prediction_tokens": usage.get("completion_tokens_details", {}).get("accepted_prediction_tokens", None),
                "rejected_prediction_tokens": usage.get("completion_tokens_details", {}).get("rejected_prediction_tokens", None)
            }
        }

        # Generate log entry with standardized request
        log_entry = create_base_log_entry(provider, standardized_request)
        
        # Add remaining fields
        log_entry.update({
            "response": response_text,
            "usage": standardized_usage,
            "duration": duration,
            "success": success,
        })
        
        # Add tool_calls field if we have function call information
        if function_call_info:
            log_entry["tool_calls"] = function_call_info
        
        # Remove any existing function_call field to adhere to schema
        if "function_call" in log_entry:
            del log_entry["function_call"]
        
        # Remove functions_info field from request if it exists
        if "functions_info" in log_entry["request"]:
            del log_entry["request"]["functions_info"]
        
        # Get user_id from API
        user_id = get_user_id()
        if user_id:
            log_entry["user_id"] = user_id

        # Call template matching API and update log entry
        log_entry = process_template_matching(processed_messages, user_id, stack_info, log_entry)
                
        # Write to log file
        send_log(log_entry)

    except Exception as e:
        logger.error(f"Error logging OpenAI call: {str(e)}")
        logger.error(traceback.format_exc())

def setup_openai_patching():
    """Set up tracking for OpenAI by patching target methods."""
    logger.info("Setting up OpenAI tracking...")

    try:
        # Import the specific class where the 'create' method resides
        from openai.resources.chat.completions import Completions as OpenAICompletions

        # Check if the 'create' method exists and hasn't been patched yet
        if hasattr(OpenAICompletions, "create") and not getattr(OpenAICompletions.create, '_llm_tracker_patched', False):
            logger.debug("Attempting to patch OpenAI Completions.create")
            original_create_method = OpenAICompletions.create  # Get the original function

            # Create the wrapped version using our helper function
            patched_create = create_generic_method_wrapper(original_create_method, "openai", log_openai_call)

            # Replace the original method on the class with the patched one
            OpenAICompletions.create = patched_create
            logger.info("Successfully patched openai.resources.chat.completions.Completions.create")

        elif hasattr(OpenAICompletions, "create") and getattr(OpenAICompletions.create, '_llm_tracker_patched', False):
            logger.debug("OpenAI Completions.create already patched.")
        else:
            # This case means the class exists but doesn't have 'create'
            logger.warning("Could not find 'create' method on OpenAI Completions class for patching.")

    except ImportError:
        logger.warning("Could not import 'openai.resources.chat.completions.Completions'. OpenAI tracking may not work.")
    except Exception as e:
        logger.error(f"Failed during OpenAI patching process: {e}")
        logger.error(traceback.format_exc())

    logger.info("OpenAI tracking setup complete.") 