# 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
# 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 hydra
import ray

from verl.trainer.ppo.ray_trainer import RayPPOTrainer


def get_custom_reward_fn(config):
    import importlib.util
    import os

    reward_fn_config = config.get("custom_reward_function") or {}
    file_path = reward_fn_config.get("path")
    if not file_path:
        return None

    if not os.path.exists(file_path):
        raise FileNotFoundError(f"Reward function file '{file_path}' not found.")

    spec = importlib.util.spec_from_file_location("custom_module", file_path)
    module = importlib.util.module_from_spec(spec)
    try:
        spec.loader.exec_module(module)
    except Exception as e:
        raise RuntimeError(f"Error loading module from '{file_path}': {e}")

    function_name = reward_fn_config.get("name")

    if not hasattr(module, function_name):
        raise AttributeError(
            f"Reward function '{function_name}' not found in '{file_path}'."
        )

    print(f"using customized reward function '{function_name}' from '{file_path}'")

    return getattr(module, function_name)


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


def run_ppo(config) -> None:
    # TODO(linjunrong.ocss884): this ENV is left for resolving SGLang conflict with ray devices
    # isolation, will solve in the future
    os.environ["ENSURE_CUDA_VISIBLE_DEVICES"] = os.environ.get(
        "CUDA_VISIBLE_DEVICES", ""
    )
    if not ray.is_initialized():
        # this is for local ray cluster
        ray.init(
            runtime_env={
                "env_vars": {
                    "TOKENIZERS_PARALLELISM": "true",
                    "NCCL_DEBUG": "WARN",
                    "VLLM_LOGGING_LEVEL": "WARN",
                }
            }
        )

    runner = TaskRunner.remote()
    ray.get(runner.run.remote(config))


@ray.remote(num_cpus=1)  # please make sure main_task is not scheduled on head
class TaskRunner:

    def run(self, config):
        # print initial config
        from pprint import pprint

        from omegaconf import OmegaConf

        from verl.utils.fs import copy_to_local

        pprint(
            OmegaConf.to_container(config, resolve=True)
        )  # resolve=True will eval symbol values
        OmegaConf.resolve(config)

        # download the checkpoint from hdfs
        local_path = copy_to_local(config.actor_rollout_ref.model.path)

        # instantiate tokenizer
        from verl.utils import hf_processor, hf_tokenizer

        tokenizer = hf_tokenizer(local_path)
        processor = hf_processor(
            local_path, use_fast=True
        )  # used for multimodal LLM, could be none

        # define worker classes
        if config.actor_rollout_ref.actor.strategy == "fsdp":
            assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
            from verl.single_controller.ray import RayWorkerGroup
            from verl.workers.fsdp_workers import (ActorRolloutRefWorker,
                                                   CriticWorker)

            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.megatron import \
                NVMegatronRayWorkerGroup
            from verl.workers.megatron_workers import (ActorRolloutRefWorker,
                                                       CriticWorker)

            ray_worker_group_cls = NVMegatronRayWorkerGroup

        else:
            raise NotImplementedError

        from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role

        role_worker_mapping = {
            Role.ActorRollout: ray.remote(ActorRolloutRefWorker),
            Role.Critic: ray.remote(CriticWorker),
            Role.RefPolicy: ray.remote(ActorRolloutRefWorker),
        }

        global_pool_id = "global_pool"
        resource_pool_spec = {
            global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes,
        }
        mapping = {
            Role.ActorRollout: global_pool_id,
            Role.Critic: global_pool_id,
            Role.RefPolicy: global_pool_id,
        }

        # 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 == "fsdp":
                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

        reward_manager_name = config.reward_model.get("reward_manager", "naive")
        if reward_manager_name == "naive":
            from verl.workers.reward_manager import NaiveRewardManager

            reward_manager_cls = NaiveRewardManager
        elif reward_manager_name == "prime":
            from verl.workers.reward_manager import PrimeRewardManager

            reward_manager_cls = PrimeRewardManager
        else:
            raise NotImplementedError

        compute_score = get_custom_reward_fn(config)
        reward_fn = reward_manager_cls(
            tokenizer=tokenizer, num_examine=0, compute_score=compute_score
        )

        # Note that we always use function-based RM for validation
        val_reward_fn = reward_manager_cls(
            tokenizer=tokenizer, num_examine=10, compute_score=compute_score
        )

        resource_pool_manager = ResourcePoolManager(
            resource_pool_spec=resource_pool_spec, mapping=mapping
        )

        trainer = RayPPOTrainer(
            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,
        )
        trainer.init_workers()
        trainer.fit()


if __name__ == "__main__":
    main()
