from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional, Union

import torch
from coati.experience_maker import Experience, ExperienceMaker
from coati.replay_buffer import ReplayBuffer
from torch import Tensor
from torch.utils.data import DistributedSampler
from tqdm import tqdm

from .callbacks import Callback
from .strategies import Strategy
from .utils import is_rank_0


class Trainer(ABC):
    """
        Base class for rlhf trainers.

    Args:
        strategy (Strategy):the strategy to use for training
        experience_maker (ExperienceMaker): the experience maker to use for produce experience to fullfill replay buffer
        replay_buffer (ReplayBuffer): the replay buffer to use for training
        experience_batch_size (int, defaults to 8): the batch size to use for experience generation
        max_epochs (int, defaults to 1): the number of epochs of training process
        tokenizer (Callable, optional): the tokenizer to use for tokenizing the input
        sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer
        data_loader_pin_memory (bool, defaults to True): whether to pin memory for data loader
        callbacks (List[Callback], defaults to []): the callbacks to call during training process
        generate_kwargs (dict, optional): the kwargs to use while model generating
    """

    def __init__(self,
                 strategy: Strategy,
                 experience_maker: ExperienceMaker,
                 replay_buffer: ReplayBuffer,
                 experience_batch_size: int = 8,
                 max_epochs: int = 1,
                 tokenizer: Optional[Callable[[Any], dict]] = None,
                 sample_replay_buffer: bool = False,
                 dataloader_pin_memory: bool = True,
                 callbacks: List[Callback] = [],
                 **generate_kwargs) -> None:
        super().__init__()
        self.strategy = strategy
        self.experience_maker = experience_maker
        self.replay_buffer = replay_buffer
        self.experience_batch_size = experience_batch_size
        self.max_epochs = max_epochs
        self.tokenizer = tokenizer
        self.generate_kwargs = generate_kwargs
        self.sample_replay_buffer = sample_replay_buffer
        self.dataloader_pin_memory = dataloader_pin_memory
        self.callbacks = callbacks

    @abstractmethod
    def training_step(self, experience: Experience) -> Dict[str, Any]:
        pass

    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)}"')

    def _sample_prompts(self, prompts) -> list:
        indices = list(range(len(prompts)))
        sampled_indices = self.strategy.experience_sampler.choice(indices, self.experience_batch_size, replace=False)
        return [prompts[i] for i in sampled_indices]

    def _learn(self):
        # replay buffer may be empty at first, we should rebuild at each training
        if not self.sample_replay_buffer:
            dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory)
            device = torch.cuda.current_device()
        if self.sample_replay_buffer:
            pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0())
            for _ in pbar:
                experience = self.replay_buffer.sample()
                metrics = self.training_step(experience)
                pbar.set_postfix(metrics)
        else:
            for epoch in range(self.max_epochs):
                self._on_learn_epoch_start(epoch)
                if isinstance(dataloader.sampler, DistributedSampler):
                    dataloader.sampler.set_epoch(epoch)
                pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0())
                for experience in pbar:
                    self._on_learn_batch_start()
                    experience.to_device(device)
                    metrics = self.training_step(experience)
                    self._on_learn_batch_end(metrics, experience)
                    pbar.set_postfix(metrics)
                self._on_learn_epoch_end(epoch)

    def fit(self,
            prompt_dataloader,
            pretrain_dataloader,
            num_episodes: int = 50000,
            max_timesteps: int = 500,
            update_timesteps: int = 5000) -> None:
        time = 0
        self.pretrain_dataloader = pretrain_dataloader
        self.prompt_dataloader = prompt_dataloader
        self._on_fit_start()
        for episode in range(num_episodes):
            self._on_episode_start(episode)
            for timestep in tqdm(range(max_timesteps),
                                 desc=f'Episode [{episode+1}/{num_episodes}]',
                                 disable=not is_rank_0()):
                time += 1
                prompts = next(iter(self.prompt_dataloader))
                self._on_make_experience_start()
                self.experience_maker.initial_model.to(torch.cuda.current_device())
                self.experience_maker.reward_model.to(torch.cuda.current_device())
                experience = self._make_experience(prompts)
                self._on_make_experience_end(experience)
                self.replay_buffer.append(experience)
                if time % update_timesteps == 0:
                    self.experience_maker.initial_model.to('cpu')
                    self.experience_maker.reward_model.to('cpu')
                    self._learn()
                    self.replay_buffer.clear()
            self._on_episode_end(episode)
        self._on_fit_end()

    # TODO(ver217): maybe simplify these code using context
    def _on_fit_start(self) -> None:
        for callback in self.callbacks:
            callback.on_fit_start()

    def _on_fit_end(self) -> None:
        for callback in self.callbacks:
            callback.on_fit_end()

    def _on_episode_start(self, episode: int) -> None:
        for callback in self.callbacks:
            callback.on_episode_start(episode)

    def _on_episode_end(self, episode: int) -> None:
        for callback in self.callbacks:
            callback.on_episode_end(episode)

    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_learn_epoch_start(self, epoch: int) -> None:
        for callback in self.callbacks:
            callback.on_learn_epoch_start(epoch)

    def _on_learn_epoch_end(self, epoch: int) -> None:
        for callback in self.callbacks:
            callback.on_learn_epoch_end(epoch)

    def _on_learn_batch_start(self) -> None:
        for callback in self.callbacks:
            callback.on_learn_batch_start()

    def _on_learn_batch_end(self, metrics: dict, experience: Experience) -> None:
        for callback in self.callbacks:
            callback.on_learn_batch_end(metrics, experience)
