from copy import deepcopy
from typing import Dict, Union

import numpy as np
from gym import spaces

from viqs.sb3.common.preprocessing import is_image_space, is_image_space_channels_first
from viqs.sb3.common.vec_env.base_vec_env import VecEnv, VecEnvStepReturn, VecEnvWrapper


class VecTransposeImage(VecEnvWrapper):
    """
    Re-order channels, from HxWxC to CxHxW.
    It is required for PyTorch convolution layers.

    :param venv:
    """
    def __init__(self, venv: VecEnv):
        assert is_image_space(venv.observation_space) or isinstance(
            venv.observation_space, spaces.dict.Dict
        ), "The observation space must be an image or dictionary observation space"

        if isinstance(venv.observation_space, spaces.dict.Dict):
            self.image_space_keys = []
            observation_space = deepcopy(venv.observation_space)
            for key, space in observation_space.spaces.items():
                if is_image_space(space):
                    # Keep track of which keys should be transposed later
                    self.image_space_keys.append(key)
                    observation_space.spaces[key] = self.transpose_space(space, key)
        else:
            observation_space = self.transpose_space(venv.observation_space)
        super(VecTransposeImage, self).__init__(venv, observation_space=observation_space)

    @staticmethod
    def transpose_space(observation_space: spaces.Box, key: str = "") -> spaces.Box:
        """
        Transpose an observation space (re-order channels).

        :param observation_space:
        :param key: In case of dictionary space, the key of the observation space.
        :return:
        """
        # Sanity checks
        assert is_image_space(observation_space), "The observation space must be an image"
        assert not is_image_space_channels_first(
            observation_space
        ), f"The observation space {key} must follow the channel last convention"
        height, width, channels = observation_space.shape
        new_shape = (channels, height, width)
        return spaces.Box(low=0, high=255, shape=new_shape, dtype=observation_space.dtype)

    @staticmethod
    def transpose_image(image: np.ndarray) -> np.ndarray:
        """
        Transpose an image or batch of images (re-order channels).

        :param image:
        :return:
        """
        if len(image.shape) == 3:
            return np.transpose(image, (2, 0, 1))
        return np.transpose(image, (0, 3, 1, 2))

    def transpose_observations(self, observations: Union[np.ndarray, Dict]) -> Union[np.ndarray, Dict]:
        """
        Transpose (if needed) and return new observations.

        :param observations:
        :return: Transposed observations
        """
        if isinstance(observations, dict):
            # Avoid modifying the original object in place
            observations = deepcopy(observations)
            for k in self.image_space_keys:
                observations[k] = self.transpose_image(observations[k])
        else:
            observations = self.transpose_image(observations)
        return observations

    def step_wait(self) -> VecEnvStepReturn:
        observations, rewards, dones, infos = self.venv.step_wait()

        # Transpose the terminal observations
        for idx, done in enumerate(dones):
            if not done:
                continue
            if "terminal_observation" in infos[idx]:
                infos[idx]["terminal_observation"] = self.transpose_observations(infos[idx]["terminal_observation"])

        return self.transpose_observations(observations), rewards, dones, infos

    def reset(self) -> Union[np.ndarray, Dict]:
        """
        Reset all environments
        """
        return self.transpose_observations(self.venv.reset())

    def close(self) -> None:
        self.venv.close()
