import json
import re

from langchain_core.tools import BaseTool
from langgraph.graph.graph import CompiledGraph
from langgraph.graph.message import BaseMessage

from ra_aid.agents.ciayn_agent import CiaynAgent
from ra_aid.config import (
    DEFAULT_MAX_TOOL_FAILURES,
    FALLBACK_TOOL_MODEL_LIMIT,
    RETRY_FALLBACK_COUNT,
)
from ra_aid.console.output import cpm
from ra_aid.exceptions import ToolExecutionError
from ra_aid.llm import initialize_llm, validate_provider_env
from ra_aid.logging_config import get_logger
from ra_aid.tool_configs import get_all_tools
from ra_aid.tool_leaderboard import supported_top_tool_models

logger = get_logger(__name__)


class FallbackHandler:
    """
    FallbackHandler manages fallback logic when tool execution fails.

    It loads fallback models from configuration and validated provider settings,
    maintains failure counts, and triggers appropriate fallback methods for both
    prompt-based and function-calling tool invocations. It also resets internal
    counters when a tool call succeeds.
    """

    def __init__(self, config, tools):
        """
        Initialize the FallbackHandler with the given configuration and tools.

        Args:
            config (dict): Configuration dictionary that may include fallback settings.
            tools (list): List of available tools.
        """
        self.config = config
        self.tools: list[BaseTool] = tools
        self.fallback_enabled = config.get("fallback_tool_enabled", True)
        self.fallback_tool_models = self._load_fallback_tool_models(config)
        self.max_failures = config.get("max_tool_failures", DEFAULT_MAX_TOOL_FAILURES)
        self.tool_failure_consecutive_failures = 0
        self.failed_messages = set()
        self.tool_failure_used_fallbacks = set()
        self.current_failing_tool_name = ""
        self.current_tool_to_bind = None

    def _load_fallback_tool_models(self, config):
        """
        Load and return fallback tool models based on the provided configuration.

        If the config specifies 'fallback_tool_models', those are used (assuming comma-separated names).
        Otherwise, this method filters the supported_top_tool_models based on provider environment validation,
        selecting up to FALLBACK_TOOL_MODEL_LIMIT models.

        Args:
            config (dict): Configuration dictionary.

        Returns:
            list of dict: Each dictionary contains keys 'model' and 'type' representing a fallback model.
        """
        supported = []
        skipped = []
        for item in supported_top_tool_models:
            provider = item.get("provider")
            model_name = item.get("model")
            if validate_provider_env(provider):
                supported.append(item)
                if len(supported) == FALLBACK_TOOL_MODEL_LIMIT:
                    break
            else:
                skipped.append(model_name)
        final_models = []
        for item in supported:
            if "type" not in item:
                item["type"] = "prompt"
            item["model"] = item["model"].lower()
            final_models.append(item)
        message = "Fallback models selected: " + ", ".join(
            [m["model"] for m in final_models]
        )
        if skipped:
            message += (
                "\nSkipped top tool calling models due to missing provider ENV API keys: "
                + ", ".join(skipped)
            )
        cpm(message, title="Fallback Models")
        return final_models

    def handle_failure(self, error: Exception, agent: CiaynAgent | CompiledGraph):
        """
        Handle a tool failure by incrementing the failure counter and triggering fallback if thresholds are exceeded.

        Args:
            error (Exception): The exception raised during execution. If the exception has a 'base_message' attribute, that message is recorded.
            agent: The agent instance on which fallback may be executed.
        """
        if not self.fallback_enabled:
            return None

        failed_tool_call_name = self.extract_failed_tool_name(error)
        if (
            self.current_failing_tool_name
            and failed_tool_call_name != self.current_failing_tool_name
        ):
            logger.debug(
                "New failing tool name identified. Resetting consecutive tool failures."
            )
            self.reset_fallback_handler()

        logger.debug(
            f"_handle_tool_failure: tool failure encountered for code '{failed_tool_call_name}' with error: {error}"
        )

        self.current_failing_tool_name = failed_tool_call_name
        self.current_tool_to_bind = self._find_tool_to_bind(
            agent, failed_tool_call_name
        )

        if hasattr(error, "base_message") and error.base_message:
            self.failed_messages.add(str(error.base_message))

        self.tool_failure_consecutive_failures += 1
        logger.debug(
            f"_handle_tool_failure: failure count {self.tool_failure_consecutive_failures}, max_failures {self.max_failures}"
        )

        if (
            self.tool_failure_consecutive_failures >= self.max_failures
            and self.fallback_tool_models
        ):
            logger.debug(
                "_handle_tool_failure: threshold reached, invoking fallback mechanism."
            )
            return self.attempt_fallback()

    def attempt_fallback(self):
        """
        Initiate the fallback process by iterating over all fallback models and triggering the appropriate fallback method.

        Returns:
            The response from a fallback model if any, otherwise None.
        """
        logger.error(
            f"Tool call failed {self.tool_failure_consecutive_failures} times. Attempting fallback for tool: {self.current_failing_tool_name}"
        )
        cpm(
            f"**Tool fallback activated**: Attempting fallback for tool {self.current_failing_tool_name}.",
            title="Fallback Notification",
        )
        for fallback_model in self.fallback_tool_models:
            if fallback_model.get("type", "prompt").lower() == "fc":
                response = self.attempt_fallback_function(fallback_model)
            else:
                response = self.attempt_fallback_prompt(fallback_model)
            if response:
                return response
        cpm("All fallback models have failed", title="Fallback Failed")
        return None

    def reset_fallback_handler(self):
        """
        Reset the fallback handler's internal failure counters and clear the record of used fallback models.
        """
        self.tool_failure_consecutive_failures = 0
        self.failed_messages.clear()
        self.tool_failure_used_fallbacks.clear()
        self.fallback_tool_models = self._load_fallback_tool_models(self.config)

    def extract_failed_tool_name(self, error: ToolExecutionError):
        if error.tool_name:
            failed_tool_call_name = error.tool_name
        else:
            msg = str(error)
            logger.debug("Error message: %s", msg)
            match = re.search(r"name=['\"](\w+)['\"]", msg)
            if match:
                failed_tool_call_name = str(match.group(1))
                logger.debug(
                    "Extracted failed_tool_call_name using regex: %s",
                    failed_tool_call_name,
                )
            else:
                failed_tool_call_name = "Tool execution error"
                raise Exception("Fallback failed: Could not extract failed tool name.")

        return failed_tool_call_name

    def _find_tool_to_bind(self, agent, failed_tool_call_name):
        logger.debug(f"failed_tool_call_name={failed_tool_call_name}")
        tool_to_bind = None
        if hasattr(agent, "tools"):
            tool_to_bind = next(
                (t for t in agent.tools if t.func.__name__ == failed_tool_call_name),
                None,
            )
        if tool_to_bind is None:
            all_tools = get_all_tools()
            tool_to_bind = next(
                (t for t in all_tools if t.func.__name__ == failed_tool_call_name),
                None,
            )
        if tool_to_bind is None:
            # TODO: Would be nice to try fuzzy match or levenstein str match to find closest correspond tool name
            raise Exception(
                f"Fallback failed: {failed_tool_call_name} not found in all tools."
            )
        return tool_to_bind

    def attempt_fallback_prompt(self, fallback_model):
        """
        Attempt a prompt-based fallback by invoking the current failing tool with the given fallback model.

        Args:
            fallback_model (dict): The fallback model to use.

        Returns:
            The response from the fallback model invocation, or None if failed.
        """
        try:
            logger.debug(f"Trying fallback model: {fallback_model['model']}")
            simple_model = initialize_llm(
                fallback_model["provider"], fallback_model["model"]
            )
            binded_model = simple_model.bind_tools(
                [self.current_tool_to_bind],
                tool_choice=self.current_failing_tool_name,
            )
            retry_model = binded_model.with_retry(
                stop_after_attempt=RETRY_FALLBACK_COUNT
            )
            response = retry_model.invoke(self.current_failing_tool_name)
            cpm(f"response={response}")
            self.tool_failure_used_fallbacks.add(fallback_model["model"])
            tool_call = self.base_message_to_tool_call_dict(response)
            if tool_call:
                result = self.invoke_prompt_tool_call(tool_call)
                cpm(f"result={result}")
                logger.debug(
                    "Prompt-based fallback executed successfully with model: "
                    + fallback_model["model"]
                )
                self.reset_fallback_handler()
                return result
            else:
                cpm(
                    response.content if hasattr(response, "content") else response,
                    title="Fallback Model Response: " + fallback_model["model"],
                )
                return response
        except Exception as e:
            if isinstance(e, KeyboardInterrupt):
                raise
            logger.error(
                f"Prompt-based fallback with model {fallback_model['model']} failed: {e}"
            )
            return None

    def attempt_fallback_function(self, fallback_model):
        """
        Attempt a function-calling fallback by invoking the current failing tool with the given fallback model.

        Args:
            fallback_model (dict): The fallback model to use.

        Returns:
            The response from the fallback model invocation, or None if failed.
        """
        try:
            logger.debug(f"Trying fallback model: {fallback_model['model']}")
            simple_model = initialize_llm(
                fallback_model["provider"], fallback_model["model"]
            )
            binded_model = simple_model.bind_tools(
                [self.current_tool_to_bind],
                tool_choice=self.current_failing_tool_name,
            )
            retry_model = binded_model.with_retry(
                stop_after_attempt=RETRY_FALLBACK_COUNT
            )
            response = retry_model.invoke(self.current_failing_tool_name)
            cpm(f"response={response}")
            self.tool_failure_used_fallbacks.add(fallback_model["model"])
            logger.debug(
                "Function-calling fallback executed successfully with model: "
                + fallback_model["model"]
            )
            cpm(
                response.content if hasattr(response, "content") else response,
                title="Fallback Model Response: " + fallback_model["model"],
            )
            return response
        except Exception as e:
            if isinstance(e, KeyboardInterrupt):
                raise
            logger.error(
                f"Function-calling fallback with model {fallback_model['model']} failed: {e}"
            )
            return None

    def invoke_prompt_tool_call(self, tool_call_request: dict):
        """
        Invoke a tool call from a prompt-based fallback response.

        Args:
            tool_call_request (dict): The tool call request containing keys 'type', 'name', and 'arguments'.

        Returns:
            The result of invoking the tool.
        """
        tool_name_to_tool = {tool.func.__name__: tool for tool in self.tools}
        name = tool_call_request["name"]
        arguments = tool_call_request["arguments"]
        return tool_name_to_tool[name].invoke(arguments)

    def base_message_to_tool_call_dict(self, response: BaseMessage):
        """
        Extracts a tool call dictionary from a BaseMessage.

        Args:
            response: The response object containing tool call data.

        Returns:
            A tool call dictionary with keys 'id', 'type', 'name', and 'arguments' if a tool call is found,
            otherwise None.
        """
        tool_calls = self.get_tool_calls(response)
        if tool_calls:
            if len(tool_calls) > 1:
                logger.warning("Multiple tool calls detected, using the first one")
            tool_call = tool_calls[0]
            return {
                "id": tool_call["id"],
                "type": tool_call["type"],
                "name": tool_call["function"]["name"],
                "arguments": self._parse_tool_arguments(
                    tool_call["function"]["arguments"]
                ),
            }
        return None

    def _parse_tool_arguments(self, tool_arguments):
        """
        Helper method to parse tool call arguments.
        If tool_arguments is a string, it returns the JSON-parsed dictionary.
        Otherwise, returns tool_arguments as is.
        """
        if isinstance(tool_arguments, str):
            return json.loads(tool_arguments)
        return tool_arguments

    def get_tool_calls(self, response: BaseMessage):
        """
        Extracts tool calls list from a fallback response.

        Args:
            response: The response object containing tool call data.

        Returns:
            The tool calls list if present, otherwise None.
        """
        tool_calls = None
        if hasattr(response, "additional_kwargs") and response.additional_kwargs.get(
            "tool_calls"
        ):
            tool_calls = response.additional_kwargs.get("tool_calls")
        elif hasattr(response, "tool_calls"):
            tool_calls = response.tool_calls
        elif isinstance(response, dict) and response.get("additional_kwargs", {}).get(
            "tool_calls"
        ):
            tool_calls = response.get("additional_kwargs").get("tool_calls")
        return tool_calls