# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""
FSDP PPO Trainer with Ray-based single controller.
This trainer supports model-agonistic model initialization with huggingface
"""

import os
from dataclasses import dataclass, field
from enum import Enum
from pprint import pprint
from typing import Callable, Type, Tuple, Union

from omegaconf import OmegaConf, open_dict
import numpy as np
from codetiming import Timer

from single_controller.base import Worker
from single_controller.ray import RayResourcePool, RayWorkerGroup, RayClassWithInitArgs
from single_controller.ray.base import create_colocated_worker_cls
from verl import DataProto
from verl.trainer.ppo import core_algos

WorkerType = Type[Worker]


class Role(Enum):
    """
    To create more roles dynamically, you can subclass Role and add new members
    """
    Actor = 0
    Rollout = 1
    ActorRollout = 2
    Critic = 3
    RefPolicy = 4
    RewardModel = 5
    ActorRolloutRef = 6


@dataclass
class ResourcePoolManager:
    """
    Define a resource pool specification. Resource pool will be initialized first.
    Mapping
    """
    resource_pool_spec: dict[str, list[int]]
    mapping: dict[Role, str]
    resource_pool_dict: dict[str, RayResourcePool] = field(default_factory=dict)

    def create_resource_pool(self):
        for resource_pool_name, process_on_nodes in self.resource_pool_spec.items():
            # Due to the Ray issue, we can only support max_colocate_count=1 for now.
            # This means that each GPU can only have one process.
            # We can support max_colocate > 1 when applying this pull request: https://github.com/ray-project/ray/pull/44385
            resource_pool = RayResourcePool(process_on_nodes=process_on_nodes, use_gpu=True, max_colocate_count=1)
            self.resource_pool_dict[resource_pool_name] = resource_pool

    def get_resource_pool(self, role: Role) -> RayResourcePool:
        """Get the resource pool of the worker_cls"""
        return self.resource_pool_dict[self.mapping[role]]


import torch
from verl.utils.torch_functional import masked_mean


def apply_kl_penalty(data: DataProto, kl_ctrl: core_algos.AdaptiveKLController, kl_penalty='kl'):
    responses = data.batch['responses']
    response_length = responses.size(1)
    token_level_scores = data.batch['token_level_scores']
    batch_size = data.batch.batch_size[0]
    attention_mask = data.batch['attention_mask']
    response_mask = attention_mask[:, -response_length:]

    # compute kl between ref_policy and current policy
    if 'ref_log_prob' in data.batch.keys():
        kld = core_algos.kl_penalty(data.batch['old_log_probs'], data.batch['ref_log_prob'],
                                    kl_penalty=kl_penalty)  # (batch_size, response_length)
        kld = kld * response_mask
        beta = kl_ctrl.value
    else:
        beta = 0
        kld = torch.zeros_like(response_mask, dtype=torch.float32)

    token_level_rewards = token_level_scores - beta * kld

    current_kl = masked_mean(kld, mask=response_mask, axis=-1)  # average over sequence
    current_kl = torch.mean(current_kl, dim=0).item()

    # according to https://github.com/huggingface/trl/blob/951ca1841f29114b969b57b26c7d3e80a39f75a0/trl/trainer/ppo_trainer.py#L837
    kl_ctrl.update(current_kl=current_kl, n_steps=batch_size)
    data.batch['token_level_rewards'] = token_level_rewards

    metrics = {'critic/kl': current_kl, 'critic/kl_coeff': beta}

    return data, metrics


def compute_advantage(data: DataProto, gamma, lam, adv_estimator):
    values = data.batch['values']
    responses = data.batch['responses']
    response_length = responses.size(1)
    attention_mask = data.batch['attention_mask']
    response_mask = attention_mask[:, -response_length:]
    token_level_rewards = data.batch['token_level_rewards']

    # TODO: add other ways to estimate advantages
    if adv_estimator == 'gae':
        advantages, returns = core_algos.compute_gae_advantage_return(token_level_rewards=token_level_rewards,
                                                                      values=values,
                                                                      eos_mask=response_mask,
                                                                      gamma=gamma,
                                                                      lam=lam)
        data.batch['advantages'] = advantages
        data.batch['returns'] = returns
    else:
        raise NotImplementedError
    return data


def reduce_metrics(metrics: dict):
    for key, val in metrics.items():
        metrics[key] = np.mean(val)
    return metrics


def compute_data_metrics(batch):
    # TODO: add response length
    sequence_score = batch.batch['token_level_scores'].sum(-1)
    sequence_reward = batch.batch['token_level_rewards'].sum(-1)

    response_length = batch.batch['responses'].shape[-1]

    advantages = batch.batch['advantages']
    prompt_mask = batch.batch['attention_mask'][:, :-response_length]
    response_mask = batch.batch['attention_mask'][:, -response_length:]

    prompt_length = prompt_mask.sum(-1).float()
    response_length = response_mask.sum(-1).float()  # (batch_size,)

    returns = batch.batch['returns']
    values = batch.batch['values']

    metrics = {
        # score
        'critic/score/mean': torch.mean(sequence_score).detach().item(),
        'critic/score/max': torch.max(sequence_score).detach().item(),
        'critic/score/min': torch.min(sequence_score).detach().item(),
        # reward
        'critic/rewards/mean': torch.mean(sequence_reward).detach().item(),
        'critic/rewards/max': torch.max(sequence_reward).detach().item(),
        'critic/rewards/min': torch.min(sequence_reward).detach().item(),
        # adv
        'critic/advantages/mean': masked_mean(advantages, response_mask).detach().item(),
        'critic/advantages/max': torch.max(advantages[response_mask]).detach().item(),
        'critic/advantages/min': torch.min(advantages[response_mask]).detach().item(),
        # returns
        'critic/returns/mean': masked_mean(returns, response_mask).detach().item(),
        'critic/returns/max': torch.max(returns[response_mask]).detach().item(),
        'critic/returns/min': torch.min(returns[response_mask]).detach().item(),
        # values
        'critic/values/mean': masked_mean(values, response_mask).detach().item(),
        'critic/values/max': torch.max(values[response_mask]).detach().item(),
        'critic/values/min': torch.min(values[response_mask]).detach().item(),
        # response length
        'response_length/mean': torch.mean(response_length).detach().item(),
        'response_length/max': torch.max(response_length).detach().item(),
        'response_length/min': torch.min(response_length).detach().item(),
        # prompt length
        'prompt_length/mean': torch.mean(prompt_length).detach().item(),
        'prompt_length/max': torch.max(prompt_length).detach().item(),
        'prompt_length/min': torch.min(prompt_length).detach().item(),
    }
    return metrics


class RayPPOTrainer(object):
    """
    Note that this trainer runs on the driver process on a single CPU/GPU node.
    """

    # TODO: support each role have individual ray_worker_group_cls,
    # i.e., support different backend of different role
    def __init__(self,
                 config,
                 tokenizer,
                 role_worker_mapping: dict[Role, WorkerType],
                 resource_pool_manager: ResourcePoolManager,
                 ray_worker_group_cls: RayWorkerGroup = RayWorkerGroup,
                 reward_fn=None,
                 val_reward_fn=None):

        # assert torch.cuda.is_available(), 'cuda must be available on driver'

        self.tokenizer = tokenizer
        self.config = config
        self.reward_fn = reward_fn
        self.val_reward_fn = val_reward_fn

        self.hybrid_engine = config.actor_rollout_ref.hybrid_engine
        assert self.hybrid_engine, 'Currently, only support hybrid engine'

        if self.hybrid_engine:
            assert Role.ActorRollout in role_worker_mapping, f'{role_worker_mapping.keys()=}'

        self.role_worker_mapping = role_worker_mapping
        self.resource_pool_manager = resource_pool_manager
        self.use_reference_policy = Role.RefPolicy in role_worker_mapping
        self.use_rm = Role.RewardModel in role_worker_mapping
        self.ray_worker_group_cls = ray_worker_group_cls

        # define KL control
        if self.use_reference_policy:
            if config.algorithm.kl_ctrl.type == 'fixed':
                self.kl_ctrl = core_algos.FixedKLController(kl_coef=config.algorithm.kl_ctrl.kl_coef)
            elif config.algorithm.kl_ctrl.type == 'adaptive':
                assert config.algorithm.kl_ctrl.horizon > 0, f'horizon must be larger than 0. Got {config.critic.kl_ctrl.horizon}'
                self.kl_ctrl = core_algos.AdaptiveKLController(init_kl_coef=config.algorithm.kl_ctrl.kl_coef,
                                                               target_kl=config.algorithm.kl_ctrl.target_kl,
                                                               horizon=config.algorithm.kl_ctrl.horizon)
            else:
                raise NotImplementedError
        else:
            self.kl_ctrl = core_algos.FixedKLController(kl_coef=0.)

        self._create_dataloader()

    def _create_dataloader(self):
        from torch.utils.data import DataLoader
        # TODO: we have to make sure the batch size is divisible by the dp size
        from verl.utils.dataset.rl_dataset import RLHFDataset, collate_fn
        self.train_dataset = RLHFDataset(parquet_files=self.config.data.train_files,
                                         tokenizer=self.tokenizer,
                                         prompt_key=self.config.data.prompt_key,
                                         max_prompt_length=self.config.data.max_prompt_length,
                                         filter_prompts=True,
                                         return_raw_chat=self.config.data.get('return_raw_chat', False),
                                         truncation='error')
        self.train_dataloader = DataLoader(dataset=self.train_dataset,
                                           batch_size=self.config.data.train_batch_size,
                                           shuffle=True,
                                           drop_last=True,
                                           collate_fn=collate_fn)

        self.val_dataset = RLHFDataset(parquet_files=self.config.data.val_files,
                                       tokenizer=self.tokenizer,
                                       prompt_key=self.config.data.prompt_key,
                                       max_prompt_length=self.config.data.max_prompt_length,
                                       filter_prompts=True,
                                       return_raw_chat=self.config.data.get('return_raw_chat', False),
                                       truncation='error')
        self.val_dataloader = DataLoader(dataset=self.val_dataset,
                                         batch_size=self.config.data.val_batch_size,
                                         shuffle=True,
                                         drop_last=True,
                                         collate_fn=collate_fn)

        assert len(self.train_dataloader) >= 1
        assert len(self.val_dataloader) >= 1

        print(f'Size of train dataloader: {len(self.train_dataloader)}')
        print(f'Size of val dataloader: {len(self.val_dataloader)}')

        # inject total_training_steps to actor/critic optim_config. This is hacky.
        total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs

        OmegaConf.set_struct(self.config, True)
        with open_dict(self.config):
            self.config.actor_rollout_ref.actor.optim.total_training_steps = total_training_steps
            self.config.critic.optim.total_training_steps = total_training_steps

    def _validate(self):
        reward_tensor_lst = []
        data_source_lst = []
        for test_data in self.val_dataloader:
            test_batch = DataProto.from_single_dict(test_data)
            # test_batch = test_batch.to('cuda')

            # we only do validation on rule-based rm
            if test_batch[0].non_tensor_batch['reward_model']['style'] == 'model':
                return {}

            test_gen_batch = test_batch.pop(['input_ids', 'attention_mask', 'position_ids'])
            test_gen_batch.meta_info = {
                'eos_token_id': self.tokenizer.eos_token_id,
                'pad_token_id': self.tokenizer.pad_token_id,
                'recompute_log_prob': False,
                'do_sample': False,
                'validate': True,
            }

            test_output_gen_batch = self.actor_rollout_wg.generate_sequences(test_gen_batch)
            print('validation generation end')

            test_batch = test_batch.union(test_output_gen_batch)

            # evaluate using reward_function
            # for certain reward function (e.g. sandbox), the generation can overlap with reward
            reward_tensor = self.val_reward_fn(test_batch)

            reward_tensor_lst.append(reward_tensor)
            data_source_lst.append(test_batch.non_tensor_batch.get('data_source', ['unknown'] * reward_tensor.shape[0]))

        reward_tensor = torch.cat(reward_tensor_lst, dim=0).sum(-1).cpu()  # (batch_size,)
        data_sources = np.concatenate(data_source_lst, axis=0)
        # evaluate test_score based on data source
        data_source_reward = {}
        for i in range(reward_tensor.shape[0]):
            data_source = data_sources[i]
            if data_source not in data_source_reward:
                data_source_reward[data_source] = []
            data_source_reward[data_source].append(reward_tensor[i].item())

        metric_dict = {}
        for data_source, rewards in data_source_reward.items():
            metric_dict[f'test_score/{data_source}'] = np.mean(rewards)

        return metric_dict

    def init_workers(self):
        """Init resource pool and worker group"""
        self.resource_pool_manager.create_resource_pool()

        self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()}

        # create actor and rollout
        if self.hybrid_engine:
            resource_pool = self.resource_pool_manager.get_resource_pool(Role.ActorRollout)
            actor_rollout_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.ActorRollout],
                                                     config=self.config.actor_rollout_ref,
                                                     role='actor_rollout')
            self.resource_pool_to_cls[resource_pool]['actor_rollout'] = actor_rollout_cls
        else:
            raise NotImplementedError

        # create critic
        if self.config.algorithm.adv_estimator == 'gae':
            resource_pool = self.resource_pool_manager.get_resource_pool(Role.Critic)
            critic_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.Critic], config=self.config.critic)
            self.resource_pool_to_cls[resource_pool]['critic'] = critic_cls
            self.use_critic = True
        else:
            # support GRPO and ReMax
            raise NotImplementedError

        # create reference policy if needed
        if self.use_reference_policy:
            resource_pool = self.resource_pool_manager.get_resource_pool(Role.RefPolicy)
            ref_policy_cls = RayClassWithInitArgs(self.role_worker_mapping[Role.RefPolicy],
                                                  config=self.config.actor_rollout_ref,
                                                  role='ref')
            self.resource_pool_to_cls[resource_pool]['ref'] = ref_policy_cls

        # create a reward model if reward_fn is None
        if self.use_rm:
            # we create a RM here
            resource_pool = self.resource_pool_manager.get_resource_pool(Role.RewardModel)
            rm_cls = RayClassWithInitArgs(self.role_worker_mapping[Role.RewardModel], config=self.config.reward_model)
            self.resource_pool_to_cls[resource_pool]['rm'] = rm_cls

        # initialize WorkerGroup
        # NOTE: if you want to use a different resource pool for each role, which can support different parallel size,
        # you should not use `create_colocated_worker_cls`. Instead, directly pass different resource pool to different worker groups.
        # See https://github.com/volcengine/verl/blob/master/examples/ray/tutorial.ipynb for more information.
        all_wg = {}
        for resource_pool, class_dict in self.resource_pool_to_cls.items():
            worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict)
            wg_dict = self.ray_worker_group_cls(resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls)
            spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys())
            all_wg.update(spawn_wg)

        if self.use_critic:
            self.critic_wg = all_wg['critic']
            self.critic_wg.init_model()

        if self.use_reference_policy:
            self.ref_policy_wg = all_wg['ref']
            self.ref_policy_wg.init_model()

        if self.use_rm:
            self.rm_wg = all_wg['rm']
            self.rm_wg.init_model()

        # we should create rollout at the end so that vllm can have a better estimation of kv cache memory
        self.actor_rollout_wg = all_wg['actor_rollout']
        self.actor_rollout_wg.init_model()

    def fit(self):
        """
        The training loop of PPO.
        The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow.
        The light-weight advantage computation is done on the driver process.
        """
        from verl.utils.tracking import Tracking
        from omegaconf import OmegaConf

        logger = Tracking(project_name=self.config.trainer.project_name,
                          experiment_name=self.config.trainer.experiment_name,
                          default_backend=self.config.trainer.logger,
                          config=OmegaConf.to_container(self.config, resolve=True))

        global_steps = 0

        # perform validation before training
        # currently, we only support validation using the reward_function.
        if self.val_reward_fn is not None:
            val_metrics = self._validate()
            pprint(f'Initial validation metrics: {val_metrics}')

        for epoch in range(self.config.trainer.total_epochs):
            for batch_dict in self.train_dataloader:
                metrics = {}

                batch: DataProto = DataProto.from_single_dict(batch_dict)
                # batch = batch.to('cuda')

                # pop those keys for generation
                gen_batch = batch.pop(batch_keys=['input_ids', 'attention_mask', 'position_ids'])

                # generate a batch
                with Timer(name='gen', logger=None) as timer:
                    gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch)
                metrics['timing/gen'] = timer.last

                batch = batch.union(gen_batch_output)

                if self.use_reference_policy:
                    # compute reference log_prob
                    with Timer(name='ref', logger=None) as timer:
                        ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch)
                        batch = batch.union(ref_log_prob)
                    metrics['timing/ref'] = timer.last

                # compute values
                with Timer(name='values', logger=None) as timer:
                    values = self.critic_wg.compute_values(batch)
                    batch = batch.union(values)
                metrics['timing/values'] = timer.last

                with Timer(name='adv', logger=None) as timer:
                    # compute scores. Support both model and function-based.
                    # We first compute the scores using reward model. Then, we call reward_fn to combine
                    # the results from reward model and rule-based results.
                    if self.use_rm:
                        # we first compute reward model score
                        reward_tensor = self.rm_wg.compute_rm_score(batch)
                        batch = batch.union(reward_tensor)

                    # we combine with rule-based rm
                    reward_tensor = self.reward_fn(batch)
                    batch.batch['token_level_scores'] = reward_tensor

                    # compute rewards. apply_kl_penalty if available
                    batch, kl_metrics = apply_kl_penalty(batch,
                                                         kl_ctrl=self.kl_ctrl,
                                                         kl_penalty=self.config.algorithm.kl_penalty)
                    metrics.update(kl_metrics)

                    # compute advantages, executed on the driver process
                    batch = compute_advantage(batch,
                                              self.config.algorithm.gamma,
                                              self.config.algorithm.lam,
                                              adv_estimator=self.config.algorithm.adv_estimator)
                metrics['timing/adv'] = timer.last

                # update critic
                if self.use_critic:
                    with Timer(name='update_critic', logger=None) as timer:
                        critic_output = self.critic_wg.update_critic(batch)
                    metrics['timing/update_critic'] = timer.last
                    critic_output_metrics = reduce_metrics(critic_output.meta_info['metrics'])
                    metrics.update(critic_output_metrics)

                # implement critic warmup
                if self.config.trainer.critic_warmup <= global_steps:
                    # update actor
                    with Timer(name='update_actor', logger=None) as timer:
                        actor_output = self.actor_rollout_wg.update_actor(batch)
                    metrics['timing/update_actor'] = timer.last
                    actor_output_metrics = reduce_metrics(actor_output.meta_info['metrics'])
                    metrics.update(actor_output_metrics)

                # validate
                if self.val_reward_fn is not None and (global_steps + 1) % self.config.trainer.test_freq == 0:
                    with Timer(name='testing', logger=None) as timer:
                        val_metrics: dict = self._validate()
                        val_metrics = {f'val/{key}': val for key, val in val_metrics.items()}
                    metrics['timing/testing'] = timer.last
                    metrics.update(val_metrics)

                # collect metrics
                data_metrics = compute_data_metrics(batch=batch)
                metrics.update(data_metrics)

                # TODO: make a canonical logger that supports various backend
                logger.log(data=metrics, step=global_steps)

                if self.config.trainer.save_freq > 0 and (global_steps + 1) % self.config.trainer.save_freq == 0:
                    actor_local_path = os.path.join(self.config.trainer.default_local_dir, 'actor',
                                                    f'global_step_{global_steps}')
                    actor_remote_path = os.path.join(self.config.trainer.default_hdfs_dir, 'actor')
                    self.actor_rollout_wg.save_checkpoint(actor_local_path, actor_remote_path)

                    if self.use_critic:
                        critic_local_path = os.path.join(self.config.trainer.default_local_dir, 'critic',
                                                         f'global_step_{global_steps}')
                        critic_remote_path = os.path.join(self.config.trainer.default_hdfs_dir, 'critic')
                        self.critic_wg.save_checkpoint(critic_local_path, critic_remote_path)

                global_steps += 1

        # perform validation after training
        if self.val_reward_fn is not None:
            val_metrics = self._validate()
            pprint(f'Final validation metrics: {val_metrics}')
