# 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.
"""
Contains commonly used utilities for ray
"""

import concurrent.futures
import os
from typing import Any, List, Optional

import ray


def ray_noset_visible_devices(env_vars=os.environ):
    # Refer to
    # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/nvidia_gpu.py#L95-L96
    # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/amd_gpu.py#L102-L103
    # https://github.com/ray-project/ray/blob/3b9e729f6a669ffd85190f901f5e262af79771b0/python/ray/_private/accelerators/amd_gpu.py#L114-L115
    # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/npu.py#L94-L95
    # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/hpu.py#L116-L117
    # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/neuron.py#L108-L109
    # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/tpu.py#L171-L172
    # https://github.com/ray-project/ray/blob/161849364a784442cc659fb9780f1a6adee85fce/python/ray/_private/accelerators/intel_gpu.py#L97-L98
    NOSET_VISIBLE_DEVICES_ENV_VARS_LIST = [
        "RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_HABANA_VISIBLE_MODULES",
        "RAY_EXPERIMENTAL_NOSET_NEURON_RT_VISIBLE_CORES",
        "RAY_EXPERIMENTAL_NOSET_TPU_VISIBLE_CHIPS",
        "RAY_EXPERIMENTAL_NOSET_ONEAPI_DEVICE_SELECTOR",
    ]
    return any(env_vars.get(env_var) for env_var in NOSET_VISIBLE_DEVICES_ENV_VARS_LIST)


def parallel_put(data_list: List[Any], max_workers: Optional[int] = None):
    """
    Puts a list of data into the Ray object store in parallel using a thread pool.

    Args:
        data_list (List[Any]): A list of Python objects to be put into the Ray object store.
        max_workers (int, optional): The maximum number of worker threads to use.
                                     Defaults to min(len(data_list), 16).

    Returns:
        List[ray.ObjectRef]: A list of Ray object references corresponding to the input data_list,
                             maintaining the original order.
    """
    assert len(data_list) > 0, "data_list must not be empty"

    def put_data(index, data):
        return index, ray.put(data)

    if max_workers is None:
        max_workers = min(len(data_list), 16)

    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        data_list_f = [executor.submit(put_data, i, data) for i, data in enumerate(data_list)]
        res_lst = []
        for future in concurrent.futures.as_completed(data_list_f):
            res_lst.append(future.result())

        # reorder based on index
        output = [None for _ in range(len(data_list))]
        for res in res_lst:
            index, data_ref = res
            output[index] = data_ref

    return output
