""" Wrappers for use with jaxmarl baselines. """
import os
import jax
import jax.numpy as jnp
import chex
import numpy as np
from flax import struct
from functools import partial

# from gymnax.environments import environment, spaces
from gymnax.environments.spaces import Box as BoxGymnax, Discrete as DiscreteGymnax
from typing import Dict, Optional, List, Tuple, Union
from jaxmarl.environments.overcooked_v2.common import DynamicObject
from jaxmarl.environments.spaces import Box, Discrete, MultiDiscrete
from jaxmarl.environments.multi_agent_env import MultiAgentEnv, State

from safetensors.flax import save_file, load_file
from flax.traverse_util import flatten_dict, unflatten_dict

def save_params(params: Dict, filename: Union[str, os.PathLike]) -> None:
    flattened_dict = flatten_dict(params, sep=',')
    save_file(flattened_dict, filename)

def load_params(filename:Union[str, os.PathLike]) -> Dict:
    flattened_dict = load_file(filename)
    return unflatten_dict(flattened_dict, sep=",")


class JaxMARLWrapper(object):
    """Base class for all jaxmarl wrappers."""

    def __init__(self, env: MultiAgentEnv):
        self._env = env

    def __getattr__(self, name: str):
        return getattr(self._env, name)

    # def _batchify(self, x: dict):
    #     x = jnp.stack([x[a] for a in self._env.agents])
    #     return x.reshape((self._env.num_agents, -1))

    def _batchify_floats(self, x: dict):
        return jnp.stack([x[a] for a in self._env.agents])


@struct.dataclass
class LogEnvState:
    env_state: State
    episode_returns: float
    episode_lengths: int
    returned_episode_returns: float
    returned_episode_lengths: int


class LogWrapper(JaxMARLWrapper):
    """Log the episode returns and lengths.
    NOTE for now for envs where agents terminate at the same time.
    """

    def __init__(self, env: MultiAgentEnv, replace_info: bool = False):
        super().__init__(env)
        self.replace_info = replace_info

    @partial(jax.jit, static_argnums=(0,))
    def reset(self, key: chex.PRNGKey) -> Tuple[chex.Array, State]:
        obs, env_state = self._env.reset(key)
        state = LogEnvState(
            env_state,
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
        )
        return obs, state

    @partial(jax.jit, static_argnums=(0,))
    def step(
        self,
        key: chex.PRNGKey,
        state: LogEnvState,
        action: Union[int, float],
    ) -> Tuple[chex.Array, LogEnvState, float, bool, dict]:
        obs, env_state, reward, done, info = self._env.step(
            key, state.env_state, action
        )
        ep_done = done["__all__"]
        new_episode_return = state.episode_returns + self._batchify_floats(reward)
        new_episode_length = state.episode_lengths + 1
        state = LogEnvState(
            env_state=env_state,
            episode_returns=new_episode_return * (1 - ep_done),
            episode_lengths=new_episode_length * (1 - ep_done),
            returned_episode_returns=state.returned_episode_returns * (1 - ep_done)
            + new_episode_return * ep_done,
            returned_episode_lengths=state.returned_episode_lengths * (1 - ep_done)
            + new_episode_length * ep_done,
        )
        if self.replace_info:
            info = {}
        info["returned_episode_returns"] = state.returned_episode_returns
        info["returned_episode_lengths"] = state.returned_episode_lengths
        info["returned_episode"] = jnp.full((self._env.num_agents,), ep_done)
        return obs, state, reward, done, info
    
@struct.dataclass
class OvercookedV2LogEnvState:
    env_state: State
    episode_returns: float
    episode_lengths: int
    returned_episode_returns: float
    returned_episode_lengths: int
    returned_episode_recipe_returns: Dict[str, float]


class OvercookedV2LogWrapper(JaxMARLWrapper):
    def __init__(self, env: MultiAgentEnv, replace_info: bool = False):
        super().__init__(env)
        self.replace_info = replace_info

        self.recipe_dict = {
            f"{recipe[0]}_{recipe[1]}_{recipe[2]}": DynamicObject.get_recipe_encoding(
                recipe
            )
            for recipe in env.possible_recipes
        }

    @partial(jax.jit, static_argnums=(0,))
    def reset(self, key: chex.PRNGKey) -> Tuple[chex.Array, State]:
        obs, env_state = self._env.reset(key)

        recipe_returns = {
            r: jnp.zeros((self._env.num_agents,)) for r in self.recipe_dict
        }

        state = OvercookedV2LogEnvState(
            env_state,
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            recipe_returns,
        )
        return obs, state

    @partial(jax.jit, static_argnums=(0,))
    def step(
        self,
        key: chex.PRNGKey,
        state: OvercookedV2LogEnvState,
        action: Union[int, float],
    ) -> Tuple[chex.Array, LogEnvState, float, bool, dict]:
        obs, env_state, reward, done, info = self._env.step(
            key, state.env_state, action
        )
        ep_done = done["__all__"]
        new_episode_return = state.episode_returns + self._batchify_floats(reward)
        new_episode_length = state.episode_lengths + 1

        updated_recipe_returns = {
            id: jax.lax.select(
                (state.env_state.recipe == self.recipe_dict[id]) & ep_done,
                new_episode_return,
                old_episode_return,
            )
            for id, old_episode_return in state.returned_episode_recipe_returns.items()
        }

        state = OvercookedV2LogEnvState(
            env_state=env_state,
            episode_returns=new_episode_return * (1 - ep_done),
            episode_lengths=new_episode_length * (1 - ep_done),
            returned_episode_returns=jax.lax.select(
                ep_done, new_episode_return, state.returned_episode_returns
            ),
            returned_episode_lengths=jax.lax.select(
                ep_done, new_episode_length, state.returned_episode_lengths
            ),
            returned_episode_recipe_returns=updated_recipe_returns,
        )
        if self.replace_info:
            info = {}
        info["returned_episode_returns"] = state.returned_episode_returns
        info["returned_episode_lengths"] = state.returned_episode_lengths
        info["returned_episode"] = jnp.full((self._env.num_agents,), ep_done)
        info["returned_episode_recipe_returns"] = state.returned_episode_recipe_returns
        return obs, state, reward, done, info


class MPELogWrapper(LogWrapper):
    """ Times reward signal by number of agents within the environment,
    to match the on-policy codebase. """
    
    @partial(jax.jit, static_argnums=(0,))
    def step(
        self,
        key: chex.PRNGKey,
        state: LogEnvState,
        action: Union[int, float],
    ) -> Tuple[chex.Array, LogEnvState, float, bool, dict]:
        obs, env_state, reward, done, info = self._env.step(
            key, state.env_state, action
        )
        rewardlog = jax.tree.map(lambda x: x*self._env.num_agents, reward)  # As per on-policy codebase
        ep_done = done["__all__"]
        new_episode_return = state.episode_returns + self._batchify_floats(rewardlog)
        new_episode_length = state.episode_lengths + 1
        state = LogEnvState(
            env_state=env_state,
            episode_returns=new_episode_return * (1 - ep_done),
            episode_lengths=new_episode_length * (1 - ep_done),
            returned_episode_returns=state.returned_episode_returns * (1 - ep_done)
            + new_episode_return * ep_done,
            returned_episode_lengths=state.returned_episode_lengths * (1 - ep_done)
            + new_episode_length * ep_done,
        )
        if self.replace_info:
            info = {}
        info["returned_episode_returns"] = state.returned_episode_returns
        info["returned_episode_lengths"] = state.returned_episode_lengths
        info["returned_episode"] = jnp.full((self._env.num_agents,), ep_done)
        return obs, state, reward, done, info

@struct.dataclass
class SMAXLogEnvState:
    env_state: State
    episode_returns: float
    episode_lengths: int
    won_episode: int
    returned_episode_returns: float
    returned_episode_lengths: int
    returned_won_episode: int


class SMAXLogWrapper(JaxMARLWrapper):
    def __init__(self, env: MultiAgentEnv, replace_info: bool = False):
        super().__init__(env)
        self.replace_info = replace_info

    @partial(jax.jit, static_argnums=(0,))
    def reset(self, key: chex.PRNGKey) -> Tuple[chex.Array, State]:
        obs, env_state = self._env.reset(key)
        state = SMAXLogEnvState(
            env_state,
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
            jnp.zeros((self._env.num_agents,)),
        )
        return obs, state

    @partial(jax.jit, static_argnums=(0,))
    def step(
        self,
        key: chex.PRNGKey,
        state: SMAXLogEnvState,
        action: Union[int, float],
    ) -> Tuple[chex.Array, LogEnvState, float, bool, dict]:
        obs, env_state, reward, done, info = self._env.step(
            key, state.env_state, action
        )
        ep_done = done["__all__"]
        batch_reward = self._batchify_floats(reward)
        new_episode_return = state.episode_returns + self._batchify_floats(reward)
        new_episode_length = state.episode_lengths + 1
        new_won_episode = (batch_reward >= 1.0).astype(jnp.float32)
        state = SMAXLogEnvState(
            env_state=env_state,
            won_episode=new_won_episode * (1 - ep_done),
            episode_returns=new_episode_return * (1 - ep_done),
            episode_lengths=new_episode_length * (1 - ep_done),
            returned_episode_returns=state.returned_episode_returns * (1 - ep_done)
            + new_episode_return * ep_done,
            returned_episode_lengths=state.returned_episode_lengths * (1 - ep_done)
            + new_episode_length * ep_done,
            returned_won_episode=state.returned_won_episode * (1 - ep_done)
            + new_won_episode * ep_done,
        )
        if self.replace_info:
            info = {}
        info["returned_episode_returns"] = state.returned_episode_returns
        info["returned_episode_lengths"] = state.returned_episode_lengths
        info["returned_won_episode"] = state.returned_won_episode
        info["returned_episode"] = jnp.full((self._env.num_agents,), ep_done)
        return obs, state, reward, done, info


def get_space_dim(space):
    # get the proper action/obs space from Discrete-MultiDiscrete-Box spaces
    if isinstance(space, (DiscreteGymnax, Discrete)):
        return space.n
    elif isinstance(space, (BoxGymnax, Box, MultiDiscrete)):
        return np.prod(space.shape)
    else:
        print(space)
        raise NotImplementedError('Current wrapper works only with Discrete/MultiDiscrete/Box action and obs spaces')


class CTRolloutManager(JaxMARLWrapper):
    """
    Rollout Manager for Centralized Training of with Parameters Sharing. Used by JaxMARL Q-Learning Baselines.
    - Batchify multiple environments (the number of parallel envs is defined by batch_size in __init__).
    - Adds a global state (obs["__all__"]) and a global reward (rewards["__all__"]) in the env.step returns.
    - Pads the observations of the agents in order to have all the same length.
    - Adds an agent id (one hot encoded) to the observation vectors.

    By default:
    - global_state is the concatenation of all agents' observations.
    - global_reward is the sum of all agents' rewards.
    """

    def __init__(self, env: MultiAgentEnv, batch_size:int, training_agents:List=None, preprocess_obs:bool=True):
        
        super().__init__(env)
        
        self.batch_size = batch_size

        # the agents to train could differ from the total trainable agents in the env (f.i. if using pretrained agents)
        # it's important to know it in order to compute properly the default global rewards and state
        self.training_agents = self.agents if training_agents is None else training_agents  
        self.preprocess_obs = preprocess_obs  

        # TOREMOVE: this is because overcooked doesn't follow other envs conventions
        if len(env.observation_spaces) == 0:
            self.observation_spaces = {agent:self.observation_space() for agent in self.agents}
        if len(env.action_spaces) == 0:
            self.action_spaces = {agent:env.action_space() for agent in self.agents}
        
        # batched action sampling
        self.batch_samplers = {agent: jax.jit(jax.vmap(self.action_space(agent).sample, in_axes=0)) for agent in self.agents}

        # assumes the observations are flattened vectors
        self.max_obs_length = max(list(map(lambda x: get_space_dim(x), self.observation_spaces.values())))
        self.max_action_space = max(list(map(lambda x: get_space_dim(x), self.action_spaces.values())))
        self.obs_size = self.max_obs_length
        if self.preprocess_obs:
            self.obs_size += len(self.agents)

        # agents ids
        self.agents_one_hot = {a:oh for a, oh in zip(self.agents, jnp.eye(len(self.agents)))}
        # valid actions
        self.valid_actions = {a:jnp.arange(u.n) for a, u in self.action_spaces.items()}
        self.valid_actions_oh ={a:jnp.concatenate((jnp.ones(u.n), jnp.zeros(self.max_action_space - u.n))) for a, u in self.action_spaces.items()}

        # custom global state and rewards for specific envs
        if 'smax' in env.name.lower():
            self.global_state = lambda obs, state: obs['world_state']
            self.global_reward = lambda rewards: rewards[self.training_agents[0]]
            self.get_valid_actions = lambda state: jax.vmap(env.get_avail_actions)(state)
        elif 'overcooked' in env.name.lower():
            self.global_state = lambda obs, state:  jnp.concatenate([obs[agent].flatten() for agent in self.agents], axis=-1)
            self.global_reward = lambda rewards: rewards[self.training_agents[0]]
        elif 'hanabi' in env.name.lower():
            self.global_reward = lambda rewards: rewards[self.training_agents[0]]
            self.get_valid_actions = lambda state: jax.vmap(env.get_legal_moves)(state)

    
    @partial(jax.jit, static_argnums=0)
    def batch_reset(self, key):
        keys = jax.random.split(key, self.batch_size)
        return jax.vmap(self.wrapped_reset, in_axes=0)(keys)

    @partial(jax.jit, static_argnums=0)
    def batch_step(self, key, states, actions):
        keys = jax.random.split(key, self.batch_size)
        return jax.vmap(self.wrapped_step, in_axes=(0, 0, 0))(keys, states, actions)

    @partial(jax.jit, static_argnums=0)
    def wrapped_reset(self, key):
        obs_, state = self._env.reset(key)
        if self.preprocess_obs:
            obs = jax.tree.map(self._preprocess_obs, {agent:obs_[agent] for agent in self.agents}, self.agents_one_hot)
        else:
            obs = obs_
        obs["__all__"] = self.global_state(obs_, state)
        return obs, state

    @partial(jax.jit, static_argnums=0)
    def wrapped_step(self, key, state, actions):
        obs_, state, reward, done, infos = self._env.step(key, state, actions)
        if self.preprocess_obs:
            obs = jax.tree.map(self._preprocess_obs, {agent:obs_[agent] for agent in self.agents}, self.agents_one_hot)
            obs = jax.tree.map(lambda d, o: jnp.where(d, 0., o), {agent:done[agent] for agent in self.agents}, obs) # ensure that the obs are 0s for done agents
        else:
            obs = obs_
        obs["__all__"] = self.global_state(obs_, state)
        reward["__all__"] = self.global_reward(reward)
        return obs, state, reward, done, infos

    @partial(jax.jit, static_argnums=0)
    def global_state(self, obs, state):
        return jnp.concatenate([obs[agent] for agent in self.agents], axis=-1)
    
    @partial(jax.jit, static_argnums=0)
    def global_reward(self, reward):
        return jnp.stack([reward[agent] for agent in self.training_agents]).sum(axis=0) 
    
    def batch_sample(self, key, agent):
        return self.batch_samplers[agent](jax.random.split(key, self.batch_size)).astype(int)
    
    @partial(jax.jit, static_argnums=0)
    def get_valid_actions(self, state):
        # default is to return the same valid actions one hot encoded for each env 
        return {agent:jnp.tile(actions, self.batch_size).reshape(self.batch_size, -1) for agent, actions in self.valid_actions_oh.items()}

    @partial(jax.jit, static_argnums=0)
    def _preprocess_obs(self, arr, extra_features):
        # flatten
        arr = arr.flatten()
        # pad the observation vectors to the maximum length
        pad_width = [(0, 0)] * (arr.ndim - 1) + [(0, max(0, self.max_obs_length - arr.shape[-1]))]
        arr = jnp.pad(arr, pad_width, mode='constant', constant_values=0)
        # concatenate the extra features
        arr = jnp.concatenate((arr, extra_features), axis=-1)
        return arr
