# © Crown-owned copyright 2025, Defence Science and Technology Laboratory UK
"""Interface for agents."""
from __future__ import annotations

from abc import abstractmethod
from typing import Any, ClassVar, Dict, List, Optional, Tuple, Type, TYPE_CHECKING

from gymnasium.core import ActType, ObsType
from pydantic import BaseModel, ConfigDict, model_validator

from primaite.game.agent.actions import ActionManager
from primaite.game.agent.agent_log import AgentLog
from primaite.game.agent.observations.observation_manager import ObservationManager
from primaite.game.agent.rewards import RewardFunction
from primaite.interface.request import RequestFormat, RequestResponse

if TYPE_CHECKING:
    pass

__all__ = ("AgentHistoryItem", "AbstractAgent", "AbstractScriptedAgent", "ProxyAgent")


class AgentHistoryItem(BaseModel):
    """One entry of an agent's action log - what the agent did and how the simulator responded in 1 step."""

    timestep: int
    """Timestep of this action."""

    action: str
    """CAOS Action name."""

    parameters: Dict[str, Any]
    """CAOS parameters for the given action."""

    request: RequestFormat
    """The request that was sent to the simulation based on the CAOS action chosen."""

    response: RequestResponse
    """The response sent back by the simulator for this action."""

    reward: Optional[float] = None

    reward_info: Dict[str, Any] = {}


class AbstractAgent(BaseModel):
    """Base class for scripted and RL agents."""

    _registry: ClassVar[Dict[str, Type[AbstractAgent]]] = {}
    _logger: AgentLog = AgentLog(agent_name="Abstract_Agent")

    history: List[AgentHistoryItem] = []
    config: "AbstractAgent.ConfigSchema"
    action_manager: "ActionManager"
    observation_manager: "ObservationManager"
    reward_function: "RewardFunction"

    class ConfigSchema(BaseModel):
        """
        Configuration Schema for AbstractAgents.

        :param type: Type of agent being generated.
        :type type: str
        :param agent_name: Unique string identifier for the agent, for reporting and multi-agent purposes.
        :type agent_name: str
        :param observation_space: Observation space for the agent.
        :type observation_space: Optional[ObservationSpace]
        :param reward_function: Reward function for the agent.
        :type reward_function: Optional[RewardFunction]
        :param agent_settings: Configurable Options for Abstracted Agents.
        :type agent_settings: Optional[AgentSettings]
        """

        model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
        agent_name: str = "Abstract_Agent"
        flatten_obs: bool = True
        "Whether to flatten the observation space before passing it to the agent. True by default."
        action_masking: bool = False
        "Whether to return action masks at each step."
        start_step: int = 5
        "The timestep at which an agent begins performing it's actions"
        frequency: int = 5
        "The number of timesteps to wait between performing actions"
        variance: int = 0
        "The amount the frequency can randomly change to"

        @model_validator(mode="after")
        def check_variance_lt_frequency(self) -> "AbstractAgent.ConfigSchema":
            """
            Make sure variance is equal to or lower than frequency.

            This is because the calculation for the next execution time is now + (frequency +- variance).
            If variance were greater than frequency, sometimes the bracketed term would be negative
            and the attack would never happen again.
            """
            if self.variance > self.frequency:
                raise ValueError(
                    f"Agent start settings error: variance must be lower than frequency "
                    f"{self.variance=}, {self.frequency=}"
                )
            return self

    def __init_subclass__(cls, identifier: str, **kwargs: Any) -> None:
        if identifier in cls._registry:
            raise ValueError(f"Cannot create a new agent under reserved name {identifier}")
        cls._registry[identifier] = cls
        super().__init_subclass__(**kwargs)

    @property
    def flatten_obs(self) -> bool:
        """Return agent flatten_obs param."""
        return self.config.flatten_obs

    @classmethod
    def from_config(cls, config: Dict) -> "AbstractAgent":
        """Creates an agent component from a configuration dictionary."""
        obj = cls(
            config=cls.ConfigSchema(**config["agent_settings"]),
            action_manager=ActionManager.from_config(**config["action_manager"]),
            observation_manager=ObservationManager.from_config(**config["observation_space"]),
            reward_function=RewardFunction.from_config(**config["reward_function"]),
        )
        return obj

    def update_observation(self, state: Dict) -> ObsType:
        """
        Convert a state from the simulator into an observation for the agent using the observation space.

        state : dict state directly from simulation.describe_state
        output : dict state according to CAOS.
        """
        return self.observation_manager.update(state)

    def update_reward(self, state: Dict) -> float:
        """
        Use the reward function to calculate a reward from the state.

        :param state: State of the environment.
        :type state: Dict
        :return: Reward from the state.
        :rtype: float
        """
        return self.reward_function.update(state=state, last_action_response=self.config.history[-1])

    @abstractmethod
    def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
        """
        Return an action to be taken in the environment.

        Subclasses should implement agent logic here. It should use the observation as input to decide best next action.

        :param obs: Observation of the environment.
        :type obs: ObsType
        :param timestep: The current timestep in the simulation, used for non-RL agents. Optional
        :type timestep: int
        :return: Action to be taken in the environment.
        :rtype: Tuple[str, Dict]
        """
        # in RL agent, this method will send CAOS observation to RL agent, then receive a int 0-39,
        # then use a bespoke conversion to take 1-40 int back into CAOS action
        return ("do_nothing", {})

    def format_request(self, action: Tuple[str, Dict], options: Dict[str, int]) -> List[str]:
        # this will take something like APPLICATION.EXECUTE and add things like target_ip_address in simulator.
        # therefore the execution definition needs to be a mapping from CAOS into SIMULATOR
        """Format action into format expected by the simulator, and apply execution definition if applicable."""
        request = self.action_manager.form_request(action_identifier=action, action_options=options)
        return request

    def process_action_response(
        self, timestep: int, action: str, parameters: Dict[str, Any], request: RequestFormat, response: RequestResponse
    ) -> None:
        """Process the response from the most recent action."""
        self.history.append(
            AgentHistoryItem(
                timestep=timestep, action=action, parameters=parameters, request=request, response=response
            )
        )

    def save_reward_to_history(self) -> None:
        """Update the most recent history item with the reward value."""
        self.history[-1].reward = self.reward_function.current_reward


class AbstractScriptedAgent(AbstractAgent, identifier="Abstract_Scripted_Agent"):
    """Base class for actors which generate their own behaviour."""

    config: "AbstractScriptedAgent.ConfigSchema"

    class ConfigSchema(AbstractAgent.ConfigSchema):
        """Configuration Schema for AbstractScriptedAgents."""

        agent_name: str = "Abstract_Scripted_Agent"

    @abstractmethod
    def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
        """Return an action to be taken in the environment."""
        return super().get_action(obs=obs, timestep=timestep)


class ProxyAgent(AbstractAgent, identifier="ProxyAgent"):
    """Agent that sends observations to an RL model and receives actions from that model."""

    config: "ProxyAgent.ConfigSchema"
    most_recent_action: ActType = None

    class ConfigSchema(AbstractAgent.ConfigSchema):
        """Configuration Schema for Proxy Agent."""

        agent_name: str = "Proxy_Agent"
        flatten_obs: bool = False
        action_masking: bool = False

    def get_action(self, obs: ObsType, timestep: int = 0) -> Tuple[str, Dict]:
        """
        Return the agent's most recent action, formatted in CAOS format.

        :param obs: Observation for the agent. Not used by ProxyAgents, but required by the interface.
        :type obs: ObsType
        :param timestep: Current simulation timestep. Not used by ProxyAgents, bur required for the interface.
        :type timestep: int
        :return: Action to be taken in CAOS format.
        :rtype: Tuple[str, Dict]
        """
        return self.action_manager.get_action(self.most_recent_action)

    def store_action(self, action: ActType):
        """
        Store the most recent action taken by the agent.

        The environment is responsible for calling this method when it receives an action from the agent policy.
        """
        self.most_recent_action = action