from typing import Any, Dict, Generic, Optional, Tuple, TypeVar, Union

import gym
import numpy as np
from gym import spaces

from stable_baselines3.common.type_aliases import GymStepReturn

T = TypeVar("T", int, np.ndarray)


class IdentityEnv(gym.Env, Generic[T]):
    def __init__(self, dim: Optional[int] = None, space: Optional[spaces.Space] = None, ep_length: int = 100):
        """
        Identity environment for testing purposes

        :param dim: the size of the action and observation dimension you want
            to learn. Provide at most one of ``dim`` and ``space``. If both are
            None, then initialization proceeds with ``dim=1`` and ``space=None``.
        :param space: the action and observation space. Provide at most one of
            ``dim`` and ``space``.
        :param ep_length: the length of each episode in timesteps
        """
        if space is None:
            if dim is None:
                dim = 1
            space = spaces.Discrete(dim)
        else:
            assert dim is None, "arguments for both 'dim' and 'space' provided: at most one allowed"

        self.action_space = self.observation_space = space
        self.ep_length = ep_length
        self.current_step = 0
        self.num_resets = -1  # Becomes 0 after __init__ exits.
        self.reset()

    def reset(self) -> T:
        self.current_step = 0
        self.num_resets += 1
        self._choose_next_state()
        return self.state

    def step(self, action: T) -> Tuple[T, float, bool, Dict[str, Any]]:
        reward = self._get_reward(action)
        self._choose_next_state()
        self.current_step += 1
        done = self.current_step >= self.ep_length
        return self.state, reward, done, {}

    def _choose_next_state(self) -> None:
        self.state = self.action_space.sample()

    def _get_reward(self, action: T) -> float:
        return 1.0 if np.all(self.state == action) else 0.0

    def render(self, mode: str = "human") -> None:
        pass


class IdentityEnvBox(IdentityEnv[np.ndarray]):
    def __init__(self, low: float = -1.0, high: float = 1.0, eps: float = 0.05, ep_length: int = 100):
        """
        Identity environment for testing purposes

        :param low: the lower bound of the box dim
        :param high: the upper bound of the box dim
        :param eps: the epsilon bound for correct value
        :param ep_length: the length of each episode in timesteps
        """
        space = spaces.Box(low=low, high=high, shape=(1,), dtype=np.float32)
        super().__init__(ep_length=ep_length, space=space)
        self.eps = eps

    def step(self, action: np.ndarray) -> GymStepReturn:
        reward = self._get_reward(action)
        self._choose_next_state()
        self.current_step += 1
        done = self.current_step >= self.ep_length
        return self.state, reward, done, {}

    def _get_reward(self, action: np.ndarray) -> float:
        return 1.0 if (self.state - self.eps) <= action <= (self.state + self.eps) else 0.0


class IdentityEnvMultiDiscrete(IdentityEnv[np.ndarray]):
    def __init__(self, dim: int = 1, ep_length: int = 100):
        """
        Identity environment for testing purposes

        :param dim: the size of the dimensions you want to learn
        :param ep_length: the length of each episode in timesteps
        """
        space = spaces.MultiDiscrete([dim, dim])
        super().__init__(ep_length=ep_length, space=space)


class IdentityEnvMultiBinary(IdentityEnv[np.ndarray]):
    def __init__(self, dim: int = 1, ep_length: int = 100):
        """
        Identity environment for testing purposes

        :param dim: the size of the dimensions you want to learn
        :param ep_length: the length of each episode in timesteps
        """
        space = spaces.MultiBinary(dim)
        super().__init__(ep_length=ep_length, space=space)


class FakeImageEnv(gym.Env):
    """
    Fake image environment for testing purposes, it mimics Atari games.

    :param action_dim: Number of discrete actions
    :param screen_height: Height of the image
    :param screen_width: Width of the image
    :param n_channels: Number of color channels
    :param discrete: Create discrete action space instead of continuous
    :param channel_first: Put channels on first axis instead of last
    """

    def __init__(
        self,
        action_dim: int = 6,
        screen_height: int = 84,
        screen_width: int = 84,
        n_channels: int = 1,
        discrete: bool = True,
        channel_first: bool = False,
    ):
        self.observation_shape = (screen_height, screen_width, n_channels)
        if channel_first:
            self.observation_shape = (n_channels, screen_height, screen_width)
        self.observation_space = spaces.Box(low=0, high=255, shape=self.observation_shape, dtype=np.uint8)
        if discrete:
            self.action_space = spaces.Discrete(action_dim)
        else:
            self.action_space = spaces.Box(low=-1, high=1, shape=(5,), dtype=np.float32)
        self.ep_length = 10
        self.current_step = 0

    def reset(self) -> np.ndarray:
        self.current_step = 0
        return self.observation_space.sample()

    def step(self, action: Union[np.ndarray, int]) -> GymStepReturn:
        reward = 0.0
        self.current_step += 1
        done = self.current_step >= self.ep_length
        return self.observation_space.sample(), reward, done, {}

    def render(self, mode: str = "human") -> None:
        pass
