""" Configuration module. Uses backend_config to load system configuration. """
import logging
import os
from os.path import expandvars, expanduser

from ..backend_api import load_config
from ..backend_config.bucket_config import S3BucketConfigurations

from .defs import *
from .remote import running_remotely_task_id as _running_remotely_task_id

config_obj = load_config(Path(__file__).parent)
config_obj.initialize_logging()
config = config_obj.get("sdk")
""" Configuration object reflecting the merged SDK section of all available configuration files """


def get_cache_dir():
    cache_base_dir = Path(
        expandvars(
            expanduser(
                TRAINS_CACHE_DIR.get() or config.get("storage.cache.default_base_dir") or DEFAULT_CACHE_DIR
            )
        )
    )
    return cache_base_dir


def get_config_for_bucket(base_url, extra_configurations=None):
    config_list = S3BucketConfigurations.from_config(config.get("aws.s3"))

    for configuration in extra_configurations or []:
        config_list.add_config(configuration)

    return config_list.get_config_by_uri(base_url)


def get_remote_task_id():
    return _running_remotely_task_id


def running_remotely():
    return bool(_running_remotely_task_id)


def get_log_to_backend(default=None):
    return LOG_TO_BACKEND_ENV_VAR.get(default=default)


def get_node_id(default=0):
    node_id = NODE_ID_ENV_VAR.get()

    try:
        mpi_world_rank = int(os.environ.get('OMPI_COMM_WORLD_NODE_RANK', os.environ.get('PMI_RANK')))
    except:
        mpi_world_rank = None

    try:
        mpi_rank = int(os.environ.get('OMPI_COMM_WORLD_RANK', os.environ.get('SLURM_PROCID')))
    except:
        mpi_rank = None

    # if we have no node_id, use the mpi rank
    if node_id is None and (mpi_world_rank is not None or mpi_rank is not None):
        node_id = mpi_world_rank if mpi_world_rank is not None else mpi_rank

    # if node is is till None, use the default
    if node_id is None:
        node_id = default

    # check if we have pyTorch node/worker ID
    try:
        from torch.utils.data.dataloader import get_worker_info
        worker_info = get_worker_info()
        if not worker_info:
            torch_rank = None
        else:
            w_id = worker_info.id
            try:
                torch_rank = int(w_id)
            except Exception:
                # guess a number based on wid hopefully unique value
                import hashlib
                h = hashlib.md5()
                h.update(str(w_id).encode('utf-8'))
                torch_rank = int(h.hexdigest(), 16)
    except Exception:
        torch_rank = None

    # if we also have a torch rank add it to the node rank
    if torch_rank is not None:
        # Since we dont know the world rank, we assume it is not bigger than 10k
        node_id = (10000 * node_id) + torch_rank

    return node_id


def get_is_master_node():
    global __force_master_node
    if __force_master_node:
        return True

    return get_node_id(default=0) == 0


def get_log_redirect_level():
    """ Returns which log level (and up) should be redirected to stderr. None means no redirection. """
    value = LOG_STDERR_REDIRECT_LEVEL.get()
    try:
        if value:
            return logging._checkLevel(value)
    except (ValueError, TypeError):
        pass


def dev_worker_name():
    return DEV_WORKER_NAME.get()


def __set_is_master_node():
    try:
        force_master_node = os.environ.pop('TRAINS_FORCE_MASTER_NODE', None)
    except:
        force_master_node = None

    if force_master_node is not None:
        try:
            force_master_node = bool(int(force_master_node))
        except:
            force_master_node = None

    return force_master_node


__force_master_node = __set_is_master_node()