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

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

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

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, ABC):
    """Base class for scripted and RL agents."""

    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)

    class AgentSettingsSchema(BaseModel, ABC):
        """Schema for the 'agent_settings' key."""

        model_config = ConfigDict(extra="forbid")

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

        model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
        type: str
        ref: str = ""
        """name of the agent."""
        team: Optional[Literal["BLUE", "GREEN", "RED"]] = None
        agent_settings: AbstractAgent.AgentSettingsSchema = Field(default=lambda: AbstractAgent.AgentSettingsSchema())
        action_space: ActionManager.ConfigSchema = Field(default_factory=lambda: ActionManager.ConfigSchema())
        observation_space: ObservationManager.ConfigSchema = Field(
            default_factory=lambda: ObservationManager.ConfigSchema()
        )
        reward_function: RewardFunction.ConfigSchema = Field(default_factory=lambda: RewardFunction.ConfigSchema())

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.logger: AgentLog = AgentLog(agent_name=self.config.ref)

    config: "AbstractAgent.ConfigSchema" = Field(default_factory=lambda: AbstractAgent.ConfigSchema())

    logger: AgentLog = AgentLog(agent_name="Abstract_Agent")
    history: List[AgentHistoryItem] = []

    action_manager: ActionManager = Field(default_factory=lambda: ActionManager())
    observation_manager: ObservationManager = Field(default_factory=lambda: ObservationManager())
    reward_function: RewardFunction = Field(default_factory=lambda: RewardFunction())

    _registry: ClassVar[Dict[str, Type[AbstractAgent]]] = {}

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

    def model_post_init(self, __context: Any) -> None:
        """Overwrite the default empty action, observation, and rewards with ones defined through the config."""
        self.action_manager = ActionManager(config=self.config.action_space)
        self.observation_manager = ObservationManager(config=self.config.observation_space)
        self.reward_function = RewardFunction(config=self.config.reward_function)
        return super().model_post_init(__context)

    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.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]) -> RequestFormat:
        # 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

    @classmethod
    def from_config(cls, config: Dict) -> AbstractAgent:
        """Grab the relevant agent class and construct an instance from a config dict."""
        agent_type = config["type"]
        print("agent_type:", agent_type)
        print("cls._registry:", cls._registry)
        agent_class = cls._registry[agent_type]
        return agent_class(config=config)


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

    config: "AbstractScriptedAgent.ConfigSchema" = Field(default_factory=lambda: AbstractScriptedAgent.ConfigSchema())

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

        type: str = "AbstractScriptedAgent"

    @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" = Field(default_factory=lambda: ProxyAgent.ConfigSchema())
    most_recent_action: ActType = None

    class AgentSettingsSchema(AbstractAgent.AgentSettingsSchema):
        """Schema for the `agent_settings` part of the agent config."""

        flatten_obs: bool = False
        action_masking: bool = False

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

        type: str = "Proxy_Agent"
        agent_settings: ProxyAgent.AgentSettingsSchema = Field(default_factory=lambda: ProxyAgent.AgentSettingsSchema())

    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

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