# Copyright 2024 Bytedance Ltd. and/or its affiliates
# Copyright 2025 Meituan 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
# limitations under the License.
"""
Note that we don't combine the main with ray_trainer as ray_trainer is used by other main.
"""

import os
import socket

import hydra
import ray
from omegaconf import OmegaConf

from recipe.one_step_off_policy.utils import need_critic
from verl.trainer.constants_ppo import get_ppo_ray_runtime_env
from verl.trainer.main_ppo import create_rl_dataset, create_rl_sampler
from verl.trainer.ppo.reward import load_reward_manager
from verl.trainer.ppo.utils import need_reference_policy
from verl.utils.config import validate_config

from .ray_trainer import OneStepOffRayTrainer


@hydra.main(config_path="config", config_name="one_step_off_ppo_trainer", version_base=None)
def main(config):
    run_ppo(config)


# Define a function to run the PPO-like training process
def run_ppo(config) -> None:
    # Check if Ray is not initialized
    if not ray.is_initialized():
        # Initialize Ray with a local cluster configuration
        # Set environment variables in the runtime environment to control tokenizer parallelism,
        # NCCL debug level, VLLM logging level, and allow runtime LoRA updating
        # `num_cpus` specifies the number of CPU cores Ray can use, obtained from the configuration
        default_runtime_env = get_ppo_ray_runtime_env()
        ray_init_kwargs = config.ray_kwargs.get("ray_init", {})
        runtime_env_kwargs = ray_init_kwargs.get("runtime_env", {})
        runtime_env = OmegaConf.merge(default_runtime_env, runtime_env_kwargs)
        ray_init_kwargs = OmegaConf.create({**ray_init_kwargs, "runtime_env": runtime_env})
        print(f"ray init kwargs: {ray_init_kwargs}")
        ray.init(**OmegaConf.to_container(ray_init_kwargs))

    # Create a remote instance of the TaskRunner class, and
    # Execute the `run` method of the TaskRunner instance remotely and wait for it to complete
    if (
        config.global_profiler.tool == "nsys"
        and OmegaConf.select(config.global_profiler, "steps") is not None
        and len(OmegaConf.select(config.global_profiler, "steps")) > 0
    ):
        nsight_options = OmegaConf.to_container(config.global_profiler.tool_config.nsys.controller_nsight_options)
        runner = TaskRunner.options(runtime_env={"nsight": nsight_options}).remote()
    else:
        runner = TaskRunner.remote()
    ray.get(runner.run.remote(config))

    # [Optional] get the path of the timeline trace file from the configuration, default to None
    # This file is used for performance analysis
    timeline_json_file = config.ray_kwargs.get("timeline_json_file", None)
    if timeline_json_file:
        ray.timeline(filename=timeline_json_file)


@ray.remote(num_cpus=1)  # please make sure main_task is not scheduled on head
class TaskRunner:
    def run(self, config):
        # Print the initial configuration. `resolve=True` will evaluate symbolic values.
        from pprint import pprint

        from omegaconf import OmegaConf

        from verl.utils.fs import copy_to_local

        print(f"TaskRunner hostname: {socket.gethostname()}, PID: {os.getpid()}")

        pprint(OmegaConf.to_container(config, resolve=True))

        OmegaConf.resolve(config)

        # Define worker classes based on the actor strategy.
        if config.actor_rollout_ref.actor.strategy == "fsdp2":
            assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
            from verl.single_controller.ray import RayWorkerGroup

            from .fsdp_workers import (
                ActorRolloutRefWorker,
                AsyncActorRolloutRefWorker,
                CriticWorker,
                RolloutWorker,
            )

            actor_rollout_cls = (
                AsyncActorRolloutRefWorker
                if config.actor_rollout_ref.rollout.mode == "async"
                else ActorRolloutRefWorker
            )
            ray_worker_group_cls = RayWorkerGroup

        elif config.actor_rollout_ref.actor.strategy == "megatron":
            assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
            from verl.single_controller.ray import RayWorkerGroup

            from .megatron_workers import (
                ActorRolloutRefWorker,
                AsyncActorRolloutRefWorker,
                CriticWorker,
                RolloutWorker,
            )

            actor_rollout_cls = (
                AsyncActorRolloutRefWorker
                if config.actor_rollout_ref.rollout.mode == "async"
                else ActorRolloutRefWorker
            )
            ray_worker_group_cls = RayWorkerGroup

        else:
            raise NotImplementedError

        from .ray_trainer import ResourcePoolManager, Role

        role_worker_mapping = {
            Role.Actor: ray.remote(actor_rollout_cls),
            Role.Rollout: ray.remote(RolloutWorker),
            Role.Critic: ray.remote(CriticWorker),
        }

        global_pool_id = "actor_pool"

        assert config.trainer.n_gpus_per_node > 0, "config.trainer.n_gpus_per_node must be greater than 0"
        assert config.trainer.nnodes > 0, "config.trainer.nnodes must be greater than 0"
        assert config.rollout.n_gpus_per_node > 0, "config.rollout.n_gpus_per_node must be greater than 0"
        assert config.rollout.nnodes > 0, "config.rollout.nnodes must be greater than 0"

        actor_pool = [config.trainer.n_gpus_per_node] * config.trainer.nnodes
        rollout_pool = [config.rollout.n_gpus_per_node] * config.rollout.nnodes

        resource_pool_spec = {
            "actor_pool": actor_pool,
            "rollout_pool": rollout_pool,
        }
        mapping = {
            Role.Actor: "actor_pool",
            Role.Rollout: "rollout_pool",
            Role.Critic: "actor_pool",
        }
        print(f"resource_pool_spec: {resource_pool_spec}")
        # We should adopt a multi-source reward function here:
        # - for rule-based rm, we directly call a reward score
        # - for model-based rm, we call a model
        # - for code related prompt, we send to a sandbox if there are test cases
        # finally, we combine all the rewards together
        # The reward type depends on the tag of the data
        if config.reward_model.enable:
            if config.reward_model.strategy in ["fsdp2"]:
                from verl.workers.fsdp_workers import RewardModelWorker
            elif config.reward_model.strategy == "megatron":
                from verl.workers.megatron_workers import RewardModelWorker
            else:
                raise NotImplementedError
            role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker)
            mapping[Role.RewardModel] = global_pool_id

        # Add a reference policy worker if KL loss or KL reward is used.
        if config.algorithm.use_kl_in_reward or config.actor_rollout_ref.actor.use_kl_loss:
            role_worker_mapping[Role.RefPolicy] = ray.remote(ActorRolloutRefWorker)
            mapping[Role.RefPolicy] = global_pool_id

        # validate config
        validate_config(
            config=config,
            use_reference_policy=need_reference_policy(role_worker_mapping),
            use_critic=need_critic(config),
        )

        # Download the checkpoint from HDFS to the local machine.
        # `use_shm` determines whether to use shared memory, which could lead to faster model loading if turned on
        local_path = copy_to_local(
            config.actor_rollout_ref.model.path, use_shm=config.actor_rollout_ref.model.get("use_shm", False)
        )

        # Instantiate the tokenizer and processor.
        from verl.utils import hf_processor, hf_tokenizer

        trust_remote_code = config.data.get("trust_remote_code", False)
        tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code)
        # Used for multimodal LLM, could be None
        processor = hf_processor(local_path, trust_remote_code=trust_remote_code, use_fast=True)

        # Load the reward manager for training and validation.
        reward_fn = load_reward_manager(
            config, tokenizer, num_examine=0, **config.reward_model.get("reward_kwargs", {})
        )
        val_reward_fn = load_reward_manager(
            config, tokenizer, num_examine=1, **config.reward_model.get("reward_kwargs", {})
        )
        resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)

        from verl.utils.dataset.rl_dataset import collate_fn

        # Create training and validation datasets.
        train_dataset = create_rl_dataset(config.data.train_files, config.data, tokenizer, processor)
        val_dataset = create_rl_dataset(config.data.val_files, config.data, tokenizer, processor)
        train_sampler = create_rl_sampler(config.data, train_dataset)

        # Initialize the PPO trainer.
        trainer = OneStepOffRayTrainer(
            config=config,
            tokenizer=tokenizer,
            processor=processor,
            role_worker_mapping=role_worker_mapping,
            resource_pool_manager=resource_pool_manager,
            ray_worker_group_cls=ray_worker_group_cls,
            reward_fn=reward_fn,
            val_reward_fn=val_reward_fn,
            train_dataset=train_dataset,
            val_dataset=val_dataset,
            collate_fn=collate_fn,
            train_sampler=train_sampler,
            device_name=config.trainer.device,
        )
        # Initialize the workers of the trainer.
        trainer.init_workers()
        # Start the training process.
        trainer.fit()


if __name__ == "__main__":
    main()
