# Copyright 2024 Bytedance Ltd. and/or its affiliates
# Copyright 2023-2024 SGLang Team
#
# 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
# limitations under the License.

"""
FSDP PPO Trainer with Ray-based single controller.
This trainer supports model-agonistic model initialization with huggingface
"""

import uuid
from copy import deepcopy
from pprint import pprint
from typing import Optional

import numpy as np
import ray
import torch
from torch.utils.data import Dataset, Sampler
from tqdm import tqdm

from verl import DataProto
from verl.single_controller.ray import RayWorkerGroup
from verl.trainer.ppo import core_algos
from verl.trainer.ppo.core_algos import agg_loss
from verl.trainer.ppo.ray_trainer import (
    AdvantageEstimator,
    RayPPOTrainer,
    ResourcePoolManager,
    apply_kl_penalty,
    compute_response_mask,
)
from verl.trainer.ppo.reward import compute_reward, compute_reward_async
from verl.trainer.ppo.utils import Role, WorkerType, need_reference_policy, need_reward_model
from verl.utils.metric import reduce_metrics
from verl.utils.profiler.performance import simple_timer
from verl.utils.tracking import ValidationGenerationsLogger


def softmean(x: torch.Tensor, beta: float, dim: int = -1, keepdim: bool = False) -> torch.Tensor:
    """
    Compute SoftMean_β(x) = (1/β) * log( (1/n) * Σ exp(β * x_i) )
    Falls back to arithmetic mean when β=0.
    """
    if beta == 0.0:
        return x.mean(dim=dim, keepdim=keepdim)

    # cast beta to tensor on same device/dtype
    beta_t = x.new_tensor(beta)
    # numerically-stable logsumexp(β x)
    lse = torch.logsumexp(x * beta_t, dim=dim, keepdim=keepdim)
    n = x.size(dim)
    log_n = x.new_tensor(n).log()

    return (lse - log_n) / beta_t


def compute_advantage(data: DataProto, beta=1.0):
    rewards = data.batch["token_level_rewards"].sum(axis=-1)  # (bs, )
    s_mean = softmean(rewards, beta, keepdim=True)  # (bs, )
    rewards = rewards - s_mean  # (bs, )
    data.batch["seq_level_rewards"] = rewards  # (bs, )
    return data


class RaySPPOTrainer(RayPPOTrainer):
    """
    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,
        processor=None,
        reward_fn=None,
        val_reward_fn=None,
        train_dataset: Optional[Dataset] = None,
        val_dataset: Optional[Dataset] = None,
        collate_fn=None,
        train_sampler: Optional[Sampler] = None,
        device_name=None,
    ):
        self.tokenizer = tokenizer
        self.processor = processor
        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 = need_reference_policy(role_worker_mapping)
        self.use_rm = need_reward_model(role_worker_mapping)
        self.use_critic = False
        self.ray_worker_group_cls = ray_worker_group_cls
        self.validation_generations_logger = ValidationGenerationsLogger()
        self.device_name = device_name if device_name else self.config.trainer.device

        # define in-reward KL control
        # kl loss control currently not supported
        if config.algorithm.use_kl_in_reward:
            self.kl_ctrl_in_reward = core_algos.get_kl_controller(config.algorithm.kl_ctrl)

        self._create_dataloader(train_dataset, val_dataset, collate_fn, train_sampler)

    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 omegaconf import OmegaConf

        from verl.utils.tracking import Tracking

        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),
        )

        self.global_steps = 0

        # load checkpoint before doing anything
        self._load_checkpoint()

        # perform validation before training
        # currently, we only support validation using the reward_function.
        if self.val_reward_fn is not None and self.config.trainer.get("val_before_train", True):
            val_metrics = self._validate()
            pprint(f"Initial validation metrics: {val_metrics}")
            logger.log(data=val_metrics, step=self.global_steps)
            if self.config.trainer.get("val_only", False):
                return

        # add tqdm
        progress_bar = tqdm(total=self.total_training_steps, initial=self.global_steps, desc="Training Progress")

        # we start from step 1
        self.global_steps += 1
        last_val_metrics = None

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

                # pop those keys for generation
                batch_keys_to_pop = ["input_ids", "attention_mask", "position_ids"]
                non_tensor_batch_keys_to_pop = ["raw_prompt_ids"]
                if "multi_modal_data" in batch.non_tensor_batch:
                    non_tensor_batch_keys_to_pop.append("multi_modal_data")
                if "raw_prompt" in batch.non_tensor_batch:
                    non_tensor_batch_keys_to_pop.append("raw_prompt")
                if "tools_kwargs" in batch.non_tensor_batch:
                    non_tensor_batch_keys_to_pop.append("tools_kwargs")
                gen_batch = batch.pop(
                    batch_keys=batch_keys_to_pop,
                    non_tensor_batch_keys=non_tensor_batch_keys_to_pop,
                )
                gen_batch_output = gen_batch.repeat(
                    repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True
                )

                is_last_step = self.global_steps >= self.total_training_steps

                with simple_timer("step", timing_raw):
                    # generate a batch
                    with simple_timer("gen", timing_raw):
                        if not self.async_rollout_mode:
                            gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch_output)
                        else:
                            gen_batch_output = self.async_rollout_manager.generate_sequences(gen_batch_output)
                        timing_raw.update(gen_batch_output.meta_info["timing"])
                        gen_batch_output.meta_info.pop("timing", None)

                    if self.config.algorithm.adv_estimator == AdvantageEstimator.REMAX:
                        with simple_timer("gen_max", timing_raw):
                            gen_baseline_batch = deepcopy(gen_batch)
                            gen_baseline_batch.meta_info["do_sample"] = False
                            gen_baseline_output = self.actor_rollout_wg.generate_sequences(gen_baseline_batch)

                            batch = batch.union(gen_baseline_output)
                            # compute reward model score on batch
                            rm_scores = None
                            if self.use_rm and "rm_scores" not in batch.batch.keys():
                                rm_scores = self.rm_wg.compute_rm_score(batch)
                                batch = batch.union(rm_scores)
                            reward_baseline_tensor, _ = compute_reward(batch, self.reward_fn)
                            reward_baseline_tensor = reward_baseline_tensor.sum(dim=-1)

                            keys_to_pop = set(gen_baseline_output.batch.keys())
                            if rm_scores is not None:
                                keys_to_pop.update(rm_scores.batch.keys())
                            batch.pop(batch_keys=list(keys_to_pop))

                            batch.batch["reward_baselines"] = reward_baseline_tensor

                            del rm_scores, gen_baseline_batch, gen_baseline_output

                    batch.non_tensor_batch["uid"] = np.array(
                        [str(uuid.uuid4()) for _ in range(len(batch.batch))], dtype=object
                    )
                    # repeat to align with repeated responses in rollout
                    batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True)
                    batch = batch.union(gen_batch_output)

                    batch.batch["response_mask"] = compute_response_mask(batch)
                    # Balance the number of valid tokens across DP ranks.
                    # NOTE: This usually changes the order of data in the `batch`,
                    # which won't affect the advantage calculation (since it's based on uid),
                    # but might affect the loss calculation (due to the change of mini-batching).
                    # TODO: Decouple the DP balancing and mini-batching.
                    if self.config.trainer.balance_batch:
                        self._balance_batch(batch, metrics=metrics)

                    # compute global_valid tokens
                    batch.meta_info["global_token_num"] = torch.sum(batch.batch["attention_mask"], dim=-1).tolist()

                with simple_timer("reward", timing_raw):
                    # compute reward model score
                    if self.use_rm and "rm_scores" not in batch.batch.keys():
                        reward_tensor = self.rm_wg.compute_rm_score(batch)
                        batch = batch.union(reward_tensor)

                    if self.config.reward_model.launch_reward_fn_async:
                        future_reward = compute_reward_async.remote(batch, self.config, self.tokenizer)
                    else:
                        reward_tensor, reward_extra_infos_dict = compute_reward(batch, self.reward_fn)

                # recompute old_log_probs
                with simple_timer("old_log_prob", timing_raw):
                    old_log_prob = self.actor_rollout_wg.compute_log_prob(batch)
                    entropys = old_log_prob.batch["entropys"]
                    response_masks = batch.batch["response_mask"]
                    loss_agg_mode = self.config.actor_rollout_ref.actor.loss_agg_mode
                    entropy_agg = agg_loss(loss_mat=entropys, loss_mask=response_masks, loss_agg_mode=loss_agg_mode)
                    old_log_prob_metrics = {"actor/entropy": entropy_agg.detach().item()}
                    metrics.update(old_log_prob_metrics)
                    old_log_prob.batch.pop("entropys")
                    batch = batch.union(old_log_prob)

                if self.use_reference_policy:
                    # compute reference log_prob
                    with simple_timer("ref", timing_raw):
                        ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch)
                        batch = batch.union(ref_log_prob)

                # compute values
                if self.use_critic:
                    with simple_timer("values", timing_raw):
                        values = self.critic_wg.compute_values(batch)
                        batch = batch.union(values)

                with simple_timer("adv", timing_raw):
                    # we combine with rule-based rm
                    reward_extra_infos_dict: dict[str, list]
                    if self.config.reward_model.launch_reward_fn_async:
                        reward_tensor, reward_extra_infos_dict = ray.get(future_reward)
                    batch.batch["token_level_scores"] = reward_tensor

                    if reward_extra_infos_dict:
                        batch.non_tensor_batch.update({k: np.array(v) for k, v in reward_extra_infos_dict.items()})

                    # compute rewards. apply_kl_penalty if available
                    if self.config.algorithm.use_kl_in_reward:
                        batch, kl_metrics = apply_kl_penalty(
                            batch, kl_ctrl=self.kl_ctrl_in_reward, kl_penalty=self.config.algorithm.kl_penalty
                        )
                        metrics.update(kl_metrics)
                    else:
                        batch.batch["token_level_rewards"] = batch.batch["token_level_scores"]
                        batch.batch["seq_level_rewards"] = batch.batch["token_level_scores"]

                    beta = self.config.algorithm.sppo_eta
                    batch = compute_advantage(batch, beta=beta)

                # update critic
                if self.use_critic:
                    with simple_timer("update_critic", timing_raw):
                        critic_output = self.critic_wg.update_critic(batch)
                    critic_output_metrics = reduce_metrics(critic_output.meta_info["metrics"])
                    metrics.update(critic_output_metrics)

                # implement critic warmup
                if self.config.trainer.critic_warmup <= self.global_steps:
                    # update actor
                    with simple_timer("update_actor", timing_raw):
                        batch.meta_info["multi_turn"] = self.config.actor_rollout_ref.rollout.multi_turn.enable
                        actor_output = self.actor_rollout_wg.update_actor(batch)
                    actor_output_metrics = reduce_metrics(actor_output.meta_info["metrics"])
                    metrics.update(actor_output_metrics)

                # Log rollout generations if enabled
                rollout_data_dir = self.config.trainer.get("rollout_data_dir", None)
                if rollout_data_dir:
                    self._log_rollout_data(batch, reward_extra_infos_dict, timing_raw, rollout_data_dir)

                # validate
                if (
                    self.val_reward_fn is not None
                    and self.config.trainer.test_freq > 0
                    and (is_last_step or self.global_steps % self.config.trainer.test_freq == 0)
                ):
                    with simple_timer("testing", timing_raw):
                        val_metrics: dict = self._validate()
                        if is_last_step:
                            last_val_metrics = val_metrics
                    metrics.update(val_metrics)

                if self.config.trainer.save_freq > 0 and (
                    is_last_step or self.global_steps % self.config.trainer.save_freq == 0
                ):
                    with simple_timer("save_checkpoint", timing_raw):
                        self._save_checkpoint()

            # training metrics
            metrics.update(
                {
                    "training/global_step": self.global_steps,
                    "training/epoch": epoch,
                }
            )

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

            if is_last_step:
                pprint(f"Final validation metrics: {last_val_metrics}")
                progress_bar.close()
                return

            progress_bar.update(1)
            self.global_steps += 1
