from abc import ABC, abstractmethod
from collections.abc import Callable, Sequence
from enum import Enum
from typing import Any, TypeAlias, cast

import gymnasium as gym
from sensai.util.string import ToStringMixin

from tianshou.env import (
    BaseVectorEnv,
    DummyVectorEnv,
    RayVectorEnv,
    ShmemVectorEnv,
    SubprocVectorEnv,
)
from tianshou.highlevel.persistence import Persistence
from tianshou.utils.net.common import TActionShape

TObservationShape: TypeAlias = int | Sequence[int]


class EnvType(Enum):
    """Enumeration of environment types."""

    CONTINUOUS = "continuous"
    DISCRETE = "discrete"

    def is_discrete(self) -> bool:
        return self == EnvType.DISCRETE

    def is_continuous(self) -> bool:
        return self == EnvType.CONTINUOUS

    def assert_continuous(self, requiring_entity: Any) -> None:
        if not self.is_continuous():
            raise AssertionError(f"{requiring_entity} requires continuous environments")

    def assert_discrete(self, requiring_entity: Any) -> None:
        if not self.is_discrete():
            raise AssertionError(f"{requiring_entity} requires discrete environments")


class VectorEnvType(Enum):
    DUMMY = "dummy"
    """Vectorized environment without parallelization; environments are processed sequentially"""
    SUBPROC = "subproc"
    """Parallelization based on `subprocess`"""
    SUBPROC_SHARED_MEM = "shmem"
    """Parallelization based on `subprocess` with shared memory"""
    RAY = "ray"
    """Parallelization based on the `ray` library"""

    def create_venv(self, factories: list[Callable[[], gym.Env]]) -> BaseVectorEnv:
        match self:
            case VectorEnvType.DUMMY:
                return DummyVectorEnv(factories)
            case VectorEnvType.SUBPROC:
                return SubprocVectorEnv(factories)
            case VectorEnvType.SUBPROC_SHARED_MEM:
                return ShmemVectorEnv(factories)
            case VectorEnvType.RAY:
                return RayVectorEnv(factories)
            case _:
                raise NotImplementedError(self)


class Environments(ToStringMixin, ABC):
    """Represents (vectorized) environments."""

    def __init__(self, env: gym.Env, train_envs: BaseVectorEnv, test_envs: BaseVectorEnv):
        self.env = env
        self.train_envs = train_envs
        self.test_envs = test_envs
        self.persistence: Sequence[Persistence] = []

    @staticmethod
    def from_factory_and_type(
        factory_fn: Callable[[], gym.Env],
        env_type: EnvType,
        venv_type: VectorEnvType,
        num_training_envs: int,
        num_test_envs: int,
    ) -> "Environments":
        """Creates a suitable subtype instance from a factory function that creates a single instance and
        the type of environment (continuous/discrete).

        :param factory_fn: the factory for a single environment instance
        :param env_type: the type of environments created by `factory_fn`
        :param venv_type: the vector environment type to use for parallelization
        :param num_training_envs: the number of training environments to create
        :param num_test_envs: the number of test environments to create
        :return: the instance
        """
        train_envs = venv_type.create_venv([factory_fn] * num_training_envs)
        test_envs = venv_type.create_venv([factory_fn] * num_test_envs)
        env = factory_fn()
        match env_type:
            case EnvType.CONTINUOUS:
                return ContinuousEnvironments(env, train_envs, test_envs)
            case EnvType.DISCRETE:
                return DiscreteEnvironments(env, train_envs, test_envs)
            case _:
                raise ValueError(f"Environment type {env_type} not handled")

    def _tostring_includes(self) -> list[str]:
        return []

    def _tostring_additional_entries(self) -> dict[str, Any]:
        return self.info()

    def info(self) -> dict[str, Any]:
        return {
            "action_shape": self.get_action_shape(),
            "state_shape": self.get_observation_shape(),
        }

    def set_persistence(self, *p: Persistence) -> None:
        """Associates the given persistence handlers which may persist and restore
        environment-specific information.

        :param p: persistence handlers
        """
        self.persistence = p

    @abstractmethod
    def get_action_shape(self) -> TActionShape:
        pass

    @abstractmethod
    def get_observation_shape(self) -> TObservationShape:
        pass

    def get_action_space(self) -> gym.Space:
        return self.env.action_space

    def get_observation_space(self) -> gym.Space:
        return self.env.observation_space

    @abstractmethod
    def get_type(self) -> EnvType:
        pass


class ContinuousEnvironments(Environments):
    """Represents (vectorized) continuous environments."""

    def __init__(self, env: gym.Env, train_envs: BaseVectorEnv, test_envs: BaseVectorEnv):
        super().__init__(env, train_envs, test_envs)
        self.state_shape, self.action_shape, self.max_action = self._get_continuous_env_info(env)

    @staticmethod
    def from_factory(
        factory_fn: Callable[[], gym.Env],
        venv_type: VectorEnvType,
        num_training_envs: int,
        num_test_envs: int,
    ) -> "ContinuousEnvironments":
        """Creates an instance from a factory function that creates a single instance.

        :param factory_fn: the factory for a single environment instance
        :param venv_type: the vector environment type to use for parallelization
        :param num_training_envs: the number of training environments to create
        :param num_test_envs: the number of test environments to create
        :return: the instance
        """
        return cast(
            ContinuousEnvironments,
            Environments.from_factory_and_type(
                factory_fn,
                EnvType.CONTINUOUS,
                venv_type,
                num_training_envs,
                num_test_envs,
            ),
        )

    def info(self) -> dict[str, Any]:
        d = super().info()
        d["max_action"] = self.max_action
        return d

    @staticmethod
    def _get_continuous_env_info(
        env: gym.Env,
    ) -> tuple[tuple[int, ...], tuple[int, ...], float]:
        if not isinstance(env.action_space, gym.spaces.Box):
            raise ValueError(
                "Only environments with continuous action space are supported here. "
                f"But got env with action space: {env.action_space.__class__}.",
            )
        state_shape = env.observation_space.shape or env.observation_space.n  # type: ignore
        if not state_shape:
            raise ValueError("Observation space shape is not defined")
        action_shape = env.action_space.shape
        max_action = env.action_space.high[0]
        return state_shape, action_shape, max_action

    def get_action_shape(self) -> TActionShape:
        return self.action_shape

    def get_observation_shape(self) -> TObservationShape:
        return self.state_shape

    def get_type(self) -> EnvType:
        return EnvType.CONTINUOUS


class DiscreteEnvironments(Environments):
    """Represents (vectorized) discrete environments."""

    def __init__(self, env: gym.Env, train_envs: BaseVectorEnv, test_envs: BaseVectorEnv):
        super().__init__(env, train_envs, test_envs)
        self.observation_shape = env.observation_space.shape or env.observation_space.n  # type: ignore
        self.action_shape = env.action_space.shape or env.action_space.n  # type: ignore

    @staticmethod
    def from_factory(
        factory_fn: Callable[[], gym.Env],
        venv_type: VectorEnvType,
        num_training_envs: int,
        num_test_envs: int,
    ) -> "DiscreteEnvironments":
        """Creates an instance from a factory function that creates a single instance.

        :param factory_fn: the factory for a single environment instance
        :param venv_type: the vector environment type to use for parallelization
        :param num_training_envs: the number of training environments to create
        :param num_test_envs: the number of test environments to create
        :return: the instance
        """
        return cast(
            DiscreteEnvironments,
            Environments.from_factory_and_type(
                factory_fn,
                EnvType.CONTINUOUS,
                venv_type,
                num_training_envs,
                num_test_envs,
            ),
        )

    def get_action_shape(self) -> TActionShape:
        return self.action_shape

    def get_observation_shape(self) -> TObservationShape:
        return self.observation_shape

    def get_type(self) -> EnvType:
        return EnvType.DISCRETE


class EnvFactory(ToStringMixin, ABC):
    @abstractmethod
    def create_envs(self, num_training_envs: int, num_test_envs: int) -> Environments:
        pass