import os
import time
import tracemalloc
from copy import deepcopy
from threading import Lock
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union

import ray
import torch
import torch.nn as nn
from coati.experience_buffer.utils import BufferItem, make_experience_batch, split_experience_batch
from coati.experience_maker import Experience, ExperienceMaker, NaiveExperienceMaker
from coati.models.base import Actor, Critic, RewardModel
from coati.trainer.callbacks import Callback
from coati.trainer.strategies import Strategy
from coati.trainer.strategies.sampler import DistributedSampler
from ray.exceptions import GetTimeoutError
from torch import Tensor
from tqdm import tqdm

from .callbacks import ExperienceMakerPerformanceEvaluator, MakerCallback
from .lora_constructor import LoRAConstructor
from .utils import get_model_numel, get_rank, get_world_size, is_rank_0, set_dist_env, state_dict_to


@ray.remote(concurrency_groups={"experience_io": 1, "model_io": 1, "compute": 1})
class ExperienceMakerHolder:
    '''
    Args:
        detached_trainer_name_list: str list to get ray actor handles
        strategy:
        kl_coef: the coefficient of kl divergence loss
        sync_models_from_trainers: whether to sync models from trainers. If True, you must call sync_models_to_remote_makers() in trainers to sync models.
    '''

    def __init__(
            self,
            detached_trainer_name_list: List[str],
            strategy_fn: Callable[[], Strategy],
        # a function returns (actor, critic, reward_model, initial_model)
            model_fn: Callable[[], Tuple[Actor, Critic, RewardModel, Actor]],
            env_info: Dict[str, str] = None,
            sync_models_from_trainers: bool = False,
            buffer_cpu_offload: bool = True,
            kl_coef: float = 0.1,
            callbacks: List[MakerCallback] = [],
            eval_performance: bool = False,
            debug: bool = False,
            update_lora_weights: bool = False,
            **generate_kwargs):
        # set environment variables
        if env_info:
            set_dist_env(env_info=env_info)
        self.target_trainer_list = []
        assert len(detached_trainer_name_list) > 0
        self._detached_trainer_name_list = detached_trainer_name_list
        self.strategy = strategy_fn()
        self.buffer_cpu_offload = buffer_cpu_offload
        self.kl_coef = kl_coef
        # init models
        with self.strategy.model_init_context():
            actor, critic, reward_model, initial_model = model_fn()
        self.generate_kwargs = _set_default_generate_kwargs(generate_kwargs, actor)
        if eval_performance:
            actor_numel = get_model_numel(actor)
            critic_numel = get_model_numel(critic)
            initial_model_numel = get_model_numel(initial_model)
            reward_model_numel = get_model_numel(reward_model)
            evaluator = ExperienceMakerPerformanceEvaluator(actor_numel, critic_numel, initial_model_numel,
                                                            reward_model_numel)
            callbacks = callbacks + [evaluator]

        actor, critic, reward_model, initial_model = self.strategy.prepare(actor, critic, reward_model, initial_model)
        self.experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, self.kl_coef)
        self.callbacks = callbacks

        self._model_visit_lock = Lock()

        self._is_fully_initialized = not sync_models_from_trainers

        self._debug = debug
        self._update_lora_weights = update_lora_weights
        if self._update_lora_weights:
            self.actor_lora_constructor = LoRAConstructor()
            self.critic_lora_constructor = LoRAConstructor()

        self.target_auto_balance = False

        self._target_idx = 0

        if self._debug:
            print(f'[maker{get_rank()}] will send items to {self._detached_trainer_name_list}')
            if not self._is_fully_initialized:
                print(f'[maker{get_rank()}] Waiting for INIT')

    def _get_ready(self):
        while not self._fully_initialized():
            time.sleep(1.0)

    def _fully_initialized(self):
        return self._is_fully_initialized

    def _init_target_trainer_list(self):
        if len(self.target_trainer_list) > 0:
            return
        for name in self._detached_trainer_name_list:
            self.target_trainer_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"]))

    # copy from ../trainer/base.py
    @ray.method(concurrency_group="compute")
    def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience:
        if isinstance(inputs, Tensor):
            return self.experience_maker.make_experience(inputs, **self.generate_kwargs)
        elif isinstance(inputs, dict):
            return self.experience_maker.make_experience(**inputs, **self.generate_kwargs)
        else:
            raise ValueError(f'Unsupported input type "{type(inputs)}"')

    @ray.method(concurrency_group="experience_io")
    def _send_items(self, experience: Experience) -> None:
        self._init_target_trainer_list()
        items = split_experience_batch(experience)
        items_per_trainer = [[] for _ in range(len(self.target_trainer_list))]
        for item in items:
            items_per_trainer[self._target_idx].append(item)
            self._target_idx = (self._target_idx + 1) % len(self.target_trainer_list)
        for i, target_trainer in enumerate(self.target_trainer_list):
            if len(items_per_trainer[i]) > 0:
                target_trainer.buffer_extend.remote(items_per_trainer[i])

    def _inference_step(self, batch) -> None:
        self._on_batch_start()
        with self._model_visit_lock:
            self._on_make_experience_start()
            experience = self._make_experience(batch)
            self._on_make_experience_end(experience)
        self._on_send_start()
        if self.buffer_cpu_offload:
            experience.to_device('cpu')
        self._send_items(experience)
        self._on_send_end()
        self._on_batch_end()

    def workingloop(self, dataloader_fn: Callable[[], Iterable], num_epochs: int = 1, num_steps: int = 0):
        """Working loop of the experience maker.

        Args:
            dataloader_fn (Callable[[], Iterable]): A function that returns a dataloader.
            num_epochs (int, optional): Iterate the dataloader for number of epochs. Defaults to 1.
            num_steps (int, optional): Iterate the dataloader for number if steps. If this value > 0, num_epochs will be ignored. Defaults to 0.
        """
        self._get_ready()
        self._on_loop_start()
        dataloader = dataloader_fn()
        if num_steps > 0:
            # ignore num epochs
            it = iter(dataloader)
            for _ in tqdm(range(num_steps), desc='ExperienceMaker', disable=not is_rank_0()):
                try:
                    batch = next(it)
                except StopIteration:
                    it = iter(dataloader)
                    batch = next(it)
                self._inference_step(batch)
        else:
            with tqdm(total=num_epochs * len(dataloader), desc='ExperienceMaker', disable=not is_rank_0()) as pbar:
                for _ in range(num_epochs):
                    for batch in dataloader:
                        self._inference_step(batch)
                        pbar.update()
        self._on_loop_end()

    @ray.method(concurrency_group="model_io")
    def update_experience_maker(self,
                                new_actor_state_dict: Dict[str, Any] = None,
                                new_actor_lora_config_dict: Dict[str, Any] = None,
                                new_critic_state_dict: Dict[str, Any] = None,
                                new_critic_lora_config_dict: Dict[str, Any] = None,
                                fully_update: bool = False,
                                chunk_start: bool = None,
                                chunk_end: bool = None):
        '''
            called by trainer
            chunk_start: Set True at the first call. Before sending state_dict calls
            chunk_end: Set True at the last call. After sending state_dict calls.
            fully_update: Set True if you want to sync models when initializing

            TODO: load_state_dict integrate with model-sharding strategy
        '''
        _watch_memory = self._debug
        if chunk_start:
            if self._debug:
                print("[maker] UPDATE ")
            if _watch_memory:
                tracemalloc.start()
            self._model_visit_lock.acquire()

        with torch.no_grad():
            if new_actor_state_dict is not None:
                if not self._update_lora_weights or fully_update:
                    self.experience_maker.actor.model.load_state_dict(new_actor_state_dict, strict=False)
                else:
                    new_actor_state_dict = state_dict_to(new_actor_state_dict, device=torch.cuda.current_device())
                    state_dict_increase = self.actor_lora_constructor.reconstruct_increase(
                        new_actor_state_dict, new_actor_lora_config_dict)
                    self.actor_lora_constructor.load_state_dict_increase(
                        self.experience_maker.actor.model, state_dict_increase)
            if new_critic_state_dict is not None:
                if not self._update_lora_weights or fully_update:
                    self.experience_maker.critic.load_state_dict(new_critic_state_dict, strict=False)
                else:
                    new_critic_state_dict = state_dict_to(new_critic_state_dict, device=torch.cuda.current_device())
                    state_dict_increase = self.critic_lora_constructor.reconstruct_increase(
                        new_critic_state_dict, new_critic_lora_config_dict)
                    self.critic_lora_constructor.load_state_dict_increase(
                        self.experience_maker.critic, state_dict_increase)

        # the lock must be released after both actor and critic being updated
        if chunk_end:
            self._model_visit_lock.release()
            if _watch_memory:
                current, peak = tracemalloc.get_traced_memory()
                print(f"Current memory usage is {current / 10**6}MB; Peak was {peak / 10**6}MB")
                tracemalloc.stop()
            if fully_update:
                self._is_fully_initialized = True

    def _on_make_experience_start(self) -> None:
        for callback in self.callbacks:
            callback.on_make_experience_start()

    def _on_make_experience_end(self, experience: Experience) -> None:
        for callback in self.callbacks:
            callback.on_make_experience_end(experience)

    def _on_loop_start(self) -> None:
        for callback in self.callbacks:
            callback.on_loop_start()

    def _on_loop_end(self) -> None:
        for callback in self.callbacks:
            callback.on_loop_end()

    def _on_send_start(self) -> None:
        for callback in self.callbacks:
            callback.on_send_start()

    def _on_send_end(self) -> None:
        for callback in self.callbacks:
            callback.on_send_end()

    def _on_batch_start(self) -> None:
        for callback in self.callbacks:
            callback.on_batch_start()

    def _on_batch_end(self) -> None:
        for callback in self.callbacks:
            callback.on_batch_end()


def _set_default_generate_kwargs(generate_kwargs: dict, actor: Actor) -> None:
    origin_model = actor.model
    new_kwargs = {**generate_kwargs}
    # use huggingface models method directly
    if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'):
        new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation

    if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(origin_model, '_update_model_kwargs_for_generation'):
        new_kwargs['update_model_kwargs_fn'] = origin_model._update_model_kwargs_for_generation

    return new_kwargs
