# Copyright 2025 Individual Contributor: Thibaut Barroyer
#
# 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.

import multiprocessing
import os
from functools import partial

import ray

from verl import DataProto
from verl.utils.reward_score import default_compute_score


def _call_with_kwargs(raw_fn, extra_kwargs, *args, **kwargs):
    """Calls `raw_fn` by merging `extra_kwargs` into call-time `kwargs`, with `extra_kwargs` taking precedence.

    This function is used to merge additional keyword arguments with the original function's arguments.
    """
    merged_kwargs = {**kwargs, **extra_kwargs}
    return raw_fn(*args, **merged_kwargs)


def get_custom_reward_fn(config):
    """Load and return a custom reward function from external file.

    Dynamically imports a reward function from a specified file path and wraps
    it with additional keyword arguments from the configuration.

    Args:
        config (dict): Configuration dictionary containing custom_reward_function
                      settings with 'path', 'name', and 'reward_kwargs' fields.

    Returns:
        callable or None: Wrapped reward function with merged kwargs, or None
                         if no custom reward function is configured.

    Raises:
        FileNotFoundError: If the specified reward function file doesn't exist.
        RuntimeError: If there's an error loading the module from file.
        AttributeError: If the specified function name isn't found in the module.
    """
    import importlib.util
    import sys

    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:
        sys.modules["custom_module"] = module
        spec.loader.exec_module(module)
    except Exception as e:
        raise RuntimeError(f"Error loading module from '{file_path}': {e}") from 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}'")
    raw_fn = getattr(module, function_name)

    reward_kwargs = dict(reward_fn_config.get("reward_kwargs", {}))

    return partial(_call_with_kwargs, raw_fn, reward_kwargs)


def load_reward_manager(config, tokenizer, num_examine, **reward_kwargs):
    """
    Load and initialize a reward manager based on the configuration.

    Args:
        config: PPO trainer configuration object containing reward_model fields.
        tokenizer: Tokenizer object used for processing text.
        num_examine: Number of samples to examine.
        **reward_kwargs: Additional keyword arguments for the reward manager.

    Returns:
        An instance of the specified reward manager class.
    """
    from verl.workers.reward_manager import get_reward_manager_cls

    # The list of pre-defined reward managers are defined in `verl/workers/reward_manager/`:
    # naive: NaiveRewardManager
    # prime: PrimeRewardManager
    # batch: BatchRewardManager
    # dapo: DAPORewardManager
    # Note(haibin.lin): For custom reward managers, please make sure they are imported and
    # registered via `verl.workers.reward_manager.register`
    # By default reward_manager is set to naive (NaiveRewardManager)
    reward_manager_name = config.reward_model.get("reward_manager", "naive")
    reward_manager_cls = get_reward_manager_cls(reward_manager_name)

    # Try to get a custom reward function based on the configuration
    compute_score = get_custom_reward_fn(config)
    final_compute_score = compute_score

    if compute_score is None:
        sandbox_config = config.reward_model.get("sandbox_fusion")
        sandbox_url = sandbox_config.get("url") if sandbox_config else None
        memory_limit_mb = sandbox_config.get("memory_limit_mb", 1024)
        if sandbox_url:
            sandbox_manager = multiprocessing.Manager()
            # Create a semaphore to control concurrent access to the sandbox
            _concurrent_semaphore = sandbox_manager.Semaphore(sandbox_config.get("max_concurrent", 64))
            final_compute_score = partial(
                default_compute_score,
                sandbox_fusion_url=sandbox_url,
                concurrent_semaphore=_concurrent_semaphore,
                memory_limit_mb=memory_limit_mb,
            )
        else:
            final_compute_score = default_compute_score

    # Instantiate and return the reward manager with the specified parameters
    return reward_manager_cls(
        tokenizer=tokenizer,
        num_examine=num_examine,
        compute_score=final_compute_score,
        reward_fn_key=config.data.reward_fn_key,
        **reward_kwargs,
    )


def compute_reward(data: DataProto, reward_fn):
    """
    Compute reward for a batch of data.
    Args:
        data: DataProto object containing the input data.
        reward_fn: Reward function to compute the reward.
    Returns:
        Tuple of reward tensor and extra info dictionary.
    """
    try:
        reward_result = reward_fn(data, return_dict=True)
        reward_tensor = reward_result["reward_tensor"]
        reward_extra_infos_dict = reward_result.get("reward_extra_info", {})
    except Exception as e:
        print(f"Error in reward_fn: {e}")
        reward_tensor = reward_fn(data)
        reward_extra_infos_dict = {}

    return reward_tensor, reward_extra_infos_dict


@ray.remote(num_cpus=1)
def compute_reward_async(data: DataProto, config=None, tokenizer=None, reward_fn=None):
    """
    Load the reward manager and compute the reward for a batch of data.
    This is meant to be run in a separate Ray worker.
    """
    if reward_fn is None:
        assert config is not None and tokenizer is not None, (
            "config and tokenizer must not be None when reward_fn is None"
        )
        import warnings

        warnings.warn("using config and tokenizer with compute_reward_async is deprecated", stacklevel=2)
        reward_fn = load_reward_manager(
            config, tokenizer, num_examine=0, **config.reward_model.get("reward_kwargs", {})
        )

    return compute_reward(data, reward_fn)