# Copyright 2019-2023 Huawei Technologies Co., Ltd
#
# 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.
# ==============================================================================
"""
The configuration module provides various functions to set and get the supported
configuration parameters, and read a configuration file.

Common imported modules in corresponding API examples are as follows:

.. code-block::

    import mindspore.dataset as ds
"""
from __future__ import absolute_import
from enum import IntEnum
import os
import platform
import random
import numpy
import mindspore._c_dataengine as cde
from mindspore import log as logger
from mindspore.dataset.core.validator_helpers import replace_none, type_check
from mindspore.dataset.debug import DebugHook, PrintMetaDataHook

__all__ = ['set_sending_batches', 'load', '_init_device_info',
           'set_seed', 'get_seed',
           'set_prefetch_size', 'get_prefetch_size',
           'set_num_parallel_workers', 'get_num_parallel_workers',
           'set_numa_enable', 'get_numa_enable',
           'set_monitor_sampling_interval', 'get_monitor_sampling_interval',
           'set_callback_timeout', 'get_callback_timeout',
           'set_auto_num_workers', 'get_auto_num_workers',
           'set_enable_shared_mem', 'get_enable_shared_mem',
           'set_enable_autotune', 'get_enable_autotune',
           'set_autotune_interval', 'get_autotune_interval',
           'set_auto_offload', 'get_auto_offload',
           'set_enable_watchdog', 'get_enable_watchdog',
           'set_fast_recovery', 'get_fast_recovery',
           'set_debug_mode', 'get_debug_mode',
           'set_error_samples_mode', 'get_error_samples_mode', 'ErrorSamplesMode',
           'set_multiprocessing_timeout_interval', 'get_multiprocessing_timeout_interval']

INT32_MAX = 2147483647
UINT32_MAX = 4294967295

_config = cde.GlobalContext.config_manager()
_debug_context = {}


def _init_device_info():
    """
    INTERNAL USE ONLY!
    As rank_id need to pass into deep layer for numa and device_queue.
    One process work with only one rank_id, In standalone scenario,
    rank_id may come from env 'CUDA_VISIBLE_DEVICES', For distribute
    scenario, rank_id come from _get_global_rank().
    """
    from mindspore import context
    from mindspore.parallel._auto_parallel_context import auto_parallel_context
    from mindspore.parallel._utils import _get_global_rank
    numa_enable = False
    numa_enable_env = os.getenv("DATASET_ENABLE_NUMA", None)
    if numa_enable_env and numa_enable_env.strip() == 'True':
        numa_enable = True
    numa_enable_env = os.getenv("MS_ENABLE_NUMA", None)
    if numa_enable_env and numa_enable_env.strip() == 'True':
        numa_enable = True
    if context.get_context("device_target") == "GPU":
        rank_id = _get_global_rank()
        parallel_mode = auto_parallel_context().get_parallel_mode()
        if parallel_mode == "stand_alone":
            rank_id = context.get_context("device_id")
        if numa_enable:
            _config.set_numa_enable(True)
        _config.set_rank_id(rank_id)
    elif context.get_context("device_target") == "Ascend":
        # Ascend is a special scenario, we'd better get rank info from env
        env_rank_size = os.getenv("RANK_SIZE", None)
        env_rank_id = os.getenv("RANK_ID", None)
        rank_size = 0
        rank_id = 0
        if env_rank_size and env_rank_id:
            try:
                rank_size = int(env_rank_size.strip())
                rank_id = int(env_rank_id.strip())
            except ValueError:
                raise ValueError("rank_size or rank_id is not int.")
        if rank_size > 1:
            if numa_enable:
                _config.set_numa_enable(True)
            _config.set_rank_id(rank_id)


def set_seed(seed):
    """
    Set the seed for the random number generator in data pipeline.

    The seed can be set to control the initial state of the random generator
    for the purpose of fixing the result of random number generation.

    Note:
        This interface will set the random seed of the `random`, `numpy.random`
        and `mindspore.dataset` modules to the specified value at the same time.

    Args:
        seed (int): The desired seed. Must be non-negative.

    Raises:
        TypeError: If `seed` is not of type int.
        ValueError: If `seed` is a negative value.

    Examples:
        >>> # Set a new global configuration value for the seed value.
        >>> # Operations with randomness will use the seed value to generate random values.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_seed(1000)
    """
    if not isinstance(seed, int) or isinstance(seed, bool):
        raise TypeError("seed isn't of type int.")
    if seed < 0 or seed > UINT32_MAX:
        raise ValueError(
            "seed given is not within the required range [0, UINT32_MAX(4294967295)].")
    _config.set_seed(seed)
    random.seed(seed)
    # numpy.random isn't thread safe
    numpy.random.seed(seed)


def get_seed():
    """
    Get random number seed. If the seed has been set, then will
    return the set value, otherwise it will return the default seed value
    which equals to `std::mt19937::default_seed <http://www.cplusplus.com/reference/random/mt19937/>`_ .

    Returns:
        int, random number seed.

    Examples:
        >>> # Get the global configuration of seed.
        >>> # If set_seed() is never called before, the default value(std::mt19937::default_seed) will be returned.
        >>> import mindspore.dataset as ds
        >>> seed = ds.config.get_seed()
    """
    return _config.get_seed()


def set_prefetch_size(size):
    """
    Set the buffer queue size between dataset operations in the pipeline.

    The presence of a buffer queue allows the current operation to start
    processing subsequent data before the next operation fetches it, so the
    operations can execute asynchronously and concurrently.

    A larger buffer queue size reduces the overall processing latency when
    neighboring operations have unbalanced throughput rates, but also consumes
    more system memory.

    Args:
        size (int): The size of the buffer queue, must be greater than 0.

    Raises:
        TypeError: If `size` is not of type int.
        ValueError: If `size` is not a positive number.

    Note:
        The total memory consumed by the buffer queue is proportional to the number
        of worker threads. To avoid overuse of memory, when the number of worker
        threads is greater than 4, the actual buffer queue size used will be adjusted
        to the greater of (`size` * 4 / number of worker threads) and 1.

    Examples:
        >>> # Set a new global configuration value for the prefetch size.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_prefetch_size(1000)
    """
    if not isinstance(size, int) or isinstance(size, bool):
        raise TypeError("size isn't of type int.")
    if size <= 0 or size > INT32_MAX:
        raise ValueError(
            "size is not within the required range (0, INT32_MAX(2147483647)].")
    _config.set_op_connector_size(size)


def get_prefetch_size():
    """
    Get the prefetch size as for number of rows.
    If `set_prefetch_size` is never called before, the default value 16 will be returned.

    Returns:
        int, total number of rows to be prefetched.

    Examples:
        >>> # Get the global configuration of prefetch size.
        >>> # If set_prefetch_size() is never called before, the default value(16) will be returned.
        >>> import mindspore.dataset as ds
        >>> prefetch_size = ds.config.get_prefetch_size()
    """
    return _config.get_op_connector_size()


def set_num_parallel_workers(num):
    """
    Set a new global configuration default value for the number of parallel workers.
    This setting will affect the parallelism of all dataset operation.

    Args:
        num (int): Number of parallel workers to be used as a default for each operation.

    Raises:
        TypeError: If `num` is not of type int.
        ValueError: If `num` <= 0 or `num` > INT32_MAX(2147483647).

    Examples:
        >>> # Set a new global configuration value for the number of parallel workers.
        >>> # Now parallel dataset operations will run with 8 workers.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_num_parallel_workers(8)
    """
    if not isinstance(num, int) or isinstance(num, bool):
        raise TypeError("num isn't of type int.")
    if num <= 0 or num > INT32_MAX:
        raise ValueError("Number of parallel workers given is not within the required range"
                         " (0, INT32_MAX(2147483647)].")
    _config.set_num_parallel_workers(num)


def get_num_parallel_workers():
    """
    Get the global configuration of number of parallel workers.
    This is the DEFAULT num_parallel_workers value used for each operation.
    If `set_num_parallel_workers` is never called before, the default value(8) will be returned.

    Returns:
        int, number of parallel workers to be used as a default for each operation.

    Examples:
        >>> # Get the global configuration of parallel workers.
        >>> # If set_num_parallel_workers() is never called before, the default value(8) will be returned.
        >>> import mindspore.dataset as ds
        >>> num_parallel_workers = ds.config.get_num_parallel_workers()
    """
    return _config.get_num_parallel_workers()


def set_numa_enable(numa_enable):
    """
    Set the default state of numa enabled. If `numa_enable` is ``True``, need to
    ensure `numa library <http://rpmfind.net/linux/rpm2html/search.php?query=libnuma-devel>`_ is installed.

    Args:
        numa_enable (bool): Whether to use numa bind feature.

    Raises:
        TypeError: If `numa_enable` is not a boolean data type.

    Examples:
        >>> # Set a new global configuration value for the state of numa enabled.
        >>> # Now parallel dataset operations will run with numa bind function
        >>> import mindspore.dataset as ds
        >>> ds.config.set_numa_enable(True)
    """
    if not isinstance(numa_enable, bool):
        raise TypeError("numa_enable must be a boolean dtype.")
    _config.set_numa_enable(numa_enable)


def get_numa_enable():
    """
    Get the state of numa to indicate enabled/disabled.
    This is the numa config used for all processes, it is disabled by default.

    Returns:
        bool, the default state of numa enabled.

    Examples:
        >>> # Get the global configuration of numa.
        >>> import mindspore.dataset as ds
        >>> numa_state = ds.config.get_numa_enable()
    """
    return _config.get_numa_enable()


def set_monitor_sampling_interval(interval):
    """
    Set the default interval (in milliseconds) for monitor sampling.

    Args:
        interval (int): Interval (in milliseconds) to be used for performance monitor sampling.

    Raises:
        TypeError: If `interval` is not type int.
        ValueError: If `interval` <= 0 or `interval` > INT32_MAX(2147483647).

    Examples:
        >>> # Set a new global configuration value for the monitor sampling interval.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_monitor_sampling_interval(100)
    """
    if not isinstance(interval, int) or isinstance(interval, bool):
        raise TypeError("interval isn't of type int.")
    if interval <= 0 or interval > INT32_MAX:
        raise ValueError(
            "Interval given is not within the required range (0, INT32_MAX(2147483647)].")
    _config.set_monitor_sampling_interval(interval)


def get_monitor_sampling_interval():
    """
    Get the global configuration of sampling interval of performance monitor.
    If `set_monitor_sampling_interval` is never called before, the default value(1000) will be returned.

    Returns:
        int, interval (in milliseconds) for performance monitor sampling.

    Examples:
        >>> # Get the global configuration of monitor sampling interval.
        >>> # If set_monitor_sampling_interval() is never called before, the default value(1000) will be returned.
        >>> import mindspore.dataset as ds
        >>> sampling_interval = ds.config.get_monitor_sampling_interval()
    """
    return _config.get_monitor_sampling_interval()


def set_auto_num_workers(enable):
    """
    Set num_parallel_workers for each op automatically(This feature is turned off by default).

    If turned on, the num_parallel_workers in each op will be adjusted automatically, possibly overwriting the
    num_parallel_workers passed in by user or the default value (if user doesn't pass anything) set by
    :func:`mindspore.dataset.config.set_num_parallel_workers`.

    For now, this function is only optimized for YoloV3 dataset with per_batch_map (running map in batch).
    This feature aims to provide a baseline for optimized num_workers assignment for each operation.
    Operation whose num_parallel_workers is adjusted to a new value will be logged.

    Args:
        enable (bool): Whether to enable auto num_workers feature or not.

    Raises:
        TypeError: If `enable` is not of boolean type.

    Examples:
        >>> # Enable auto_num_worker feature, this might override the num_parallel_workers passed in by user
        >>> import mindspore.dataset as ds
        >>> ds.config.set_auto_num_workers(True)
    """
    if not isinstance(enable, bool):
        raise TypeError("enable must be of type bool.")
    _config.set_auto_num_workers(enable)


def _set_auto_workers_config(option):
    """
    INTERNAL USE ONLY!
    Select the weight profile of auto_num_workers. currently these 7 options are supported.
    Option #0 leaf_num_workers:batch_num_workers:map_num_workers=1:1:1
    Option #1 leaf_num_workers:batch_num_workers:map_num_workers=2:1:1
    Option #2 leaf_num_workers:batch_num_workers:map_num_workers=1:2:1
    Option #3 leaf_num_workers:batch_num_workers:map_num_workers=1:1:2
    Option #4 leaf_num_workers:batch_num_workers:map_num_workers=2:2:1
    Option #5 leaf_num_workers:batch_num_workers:map_num_workers=2:1:2
    Option #6 leaf_num_workers:batch_num_workers:map_num_workers=1:2:2

    Args:
        option (int): The id of the profile to use.

    Raises:
        TypeError: If `option` is not of type int.
        ValueError: If `option` is not within the range of [0, 6].
    """
    if not isinstance(option, int) or isinstance(option, bool):
        raise TypeError("option isn't of type int.")
    if option < 0 or option > 6:
        raise ValueError("option isn't within the required range of [0, 6].")
    _config.set_auto_worker_config(option)


def get_auto_num_workers():
    """
    Get the setting (turned on or off) automatic number of workers, it is disabled by default.

    Returns:
        bool, whether auto number worker feature is turned on.

    Examples:
        >>> # Get the global configuration of auto number worker feature.
        >>> import mindspore.dataset as ds
        >>> flag = ds.config.get_auto_num_workers()
    """
    return _config.get_auto_num_workers()


def set_callback_timeout(timeout):
    """
    Set the default timeout (in seconds) for :class:`mindspore.dataset.WaitedDSCallback` .

    Args:
        timeout (int): Timeout (in seconds) to be used to end the wait in :class:`mindspore.dataset.WaitedDSCallback`
            in case of a deadlock. The `timeout` must be greater than 0.

    Raises:
        TypeError: If `timeout` is not type int.
        ValueError: If `timeout` is not a positive number.

    Examples:
        >>> # Set a new global configuration value for the timeout value.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_callback_timeout(100)
    """
    if not isinstance(timeout, int) or isinstance(timeout, bool):
        raise TypeError("timeout isn't of type int.")
    if timeout <= 0 or timeout > INT32_MAX:
        raise ValueError("Timeout given is not within the required range.")
    _config.set_callback_timeout(timeout)


def get_callback_timeout():
    """
    Get the default timeout (in seconds) for :class:`mindspore.dataset.WaitedDSCallback` .
    If `set_callback_timeout` is never called before, the default value(60) will be returned.

    Returns:
        int, Timeout (in seconds) to be used to end the wait in :class:`mindspore.dataset.WaitedDSCallback` in case of
        a deadlock.

    Examples:
        >>> # Get the global configuration of callback timeout.
        >>> # If set_callback_timeout() is never called before, the default value(60) will be returned.
        >>> import mindspore.dataset as ds
        >>> callback_timeout = ds.config.get_callback_timeout()
    """
    return _config.get_callback_timeout()


def __str__():
    """
    String representation of the configurations.

    Returns:
        str, configurations.
    """
    return str(_config)


def load(file):
    """
    Load the project configuration from the file.

    Args:
        file (str): Path of the configuration file to be loaded.

    Raises:
        RuntimeError: If `file` is invalid and parsing fails.

    Examples:
        >>> # Set new default configuration according to values in the configuration file.
        >>> # example config file:
        >>> # {
        >>> #     "logFilePath": "/tmp",
        >>> #     "numParallelWorkers": 4,
        >>> #     "seed": 5489,
        >>> #     "monitorSamplingInterval": 30
        >>> # }
        >>> import mindspore.dataset as ds
        >>> config_file = "/path/to/config/file"
        >>> ds.config.load(config_file)
    """
    _config.load(file)


def set_enable_autotune(enable, filepath_prefix=None):
    """
    Set whether to enable AutoTune for data pipeline parameters.

    It can be used to automatically adjust the parameter configurations
    of each operation in the data processing pipeline, such as parallelism
    and buffer queue size, according to the load of the environment resources
    during training, so as to improve the overall processing speed.

    AutoTune is not enabled by default.

    Args:
        enable (bool): Whether to enable AutoTune.
        filepath_prefix (str, optional): The path where the optimized parameter configuration will be saved.
            Effective only if `enable` is `True`. The parameter configuration file on each Device will be
            saved separately, and the final file name will be `filepath_prefix + RANK_ID + ".json"`,
            where RANK_ID is the Device ID corresponding to the file. Default: ``None`` , no configuration
            file is saved.

    Raises:
        TypeError: If `enable` is not of type boolean.
        TypeError: If `filepath_prefix` is not of type str.
        RuntimeError: If `filepath_prefix` is an empty string.
        RuntimeError: If `filepath_prefix` is a directory.
        RuntimeError: If `filepath_prefix` does not exist.
        RuntimeError: If `filepath_prefix` does not have write permission.

    Note:
        - Saved parameter profiles can be loaded via the `mindspore.dataset.deserialize` interface to
          directly obtain a data processing pipeline object configured with optimal parameters.
        - The parameter tuning process can be viewed by turning on INFO level logging.

    An example of the generated configuration file is as follows, the "remark" field describes whether or not data
    processing parameter tuning has been performed, the "summary" field briefly shows each operation in the data
    processing pipeline and its corresponding optimal configuration, and the "tree" field provides complete
    information about the structure of the data processing pipeline.

    .. code-block::

        {
            "remark": "The following file has been auto-generated by the Dataset AutoTune.",
            "summary": [
                "CifarOp(ID:5)       (num_parallel_workers: 2, prefetch_size:64)",
                "MapOp(ID:4)         (num_parallel_workers: 2, prefetch_size:64)",
                "MapOp(ID:3)         (num_parallel_workers: 2, prefetch_size:64)",
                "BatchOp(ID:2)       (num_parallel_workers: 8, prefetch_size:64)"
            ],
            "tree": {
                ...
            }
        }

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> # enable AutoTune and save optimized data pipeline configuration
        >>> ds.config.set_enable_autotune(True, "/path/to/autotune_out.json")
        >>>
        >>> # enable AutoTune
        >>> ds.config.set_enable_autotune(True)
    """
    if not isinstance(enable, bool):
        raise TypeError("enable must be of type bool.")

    save_autoconfig = bool(enable and filepath_prefix is not None)

    if filepath_prefix and not isinstance(filepath_prefix, str):
        raise TypeError(
            "json_filepath must be a str value but was: {}.".format(filepath_prefix))

    if enable and filepath_prefix == "":
        raise RuntimeError(
            "The value of json_filepath cannot be the empty string.")

    if not enable and filepath_prefix is not None:
        logger.warning(
            "The value of json_filepath is ignored when enable is False.")

    if enable and filepath_prefix is None:
        logger.warning(
            "Dataset AutoTune is enabled but no json path is specified, check INFO log for tuned result.")

    json_filepath = replace_none(filepath_prefix, "")
    _config.set_enable_autotune(enable, save_autoconfig, json_filepath)


def get_enable_autotune():
    """
    Get whether AutoTune is currently enabled, it is disabled by default.

    Returns:
        bool, whether AutoTune is currently enabled.

    Examples:
        >>> # get the state of AutoTune
        >>> import mindspore.dataset as ds
        >>> autotune_flag = ds.config.get_enable_autotune()
    """
    return _config.get_enable_autotune()


def set_autotune_interval(interval):
    """
    Set the configuration adjustment interval (in steps) for AutoTune.

    The default setting is ``0``, which will adjust the configuration after each epoch.
    Otherwise, the configuration will be adjusted every `interval` steps.

    Args:
        interval (int): Interval (in steps) to adjust the configuration of the data pipeline.

    Raises:
        TypeError: If `interval` is not of type int.
        ValueError: If `interval` is not non-negative.

    Examples:
        >>> # set a new interval for AutoTune
        >>> import mindspore.dataset as ds
        >>> ds.config.set_autotune_interval(30)
    """
    if not isinstance(interval, int) or isinstance(interval, bool):
        raise TypeError("interval must be of type int.")
    if interval < 0 or interval > INT32_MAX:
        raise ValueError(
            "Interval given is not within the required range [0, INT32_MAX(2147483647)].")
    _config.set_autotune_interval(interval)


def get_autotune_interval():
    """
    Get the current configuration adjustment interval (in steps) for AutoTune.
    If `set_autotune_interval` is never called before, the default value(0) will be returned.

    Returns:
        int, the configuration adjustment interval (in steps) for AutoTune.

    Examples:
        >>> # get the global configuration of the autotuning interval
        >>> import mindspore.dataset as ds
        >>> autotune_interval = ds.config.get_autotune_interval()
    """
    return _config.get_autotune_interval()


def get_enable_shared_mem():
    """
    Get the default state of shared mem enabled variable.

    Note:
        `get_enable_shared_mem` is not supported on Windows and MacOS platforms yet.

    Returns:
        bool, the state of shared mem enabled variable.

    Examples:
        >>> # Get the flag of shared memory feature.
        >>> import mindspore.dataset as ds
        >>> shared_mem_flag = ds.config.get_enable_shared_mem()
    """
    # For Windows and MacOS we forbid shared mem function temporarily
    enable_shared_mem = _config.get_enable_shared_mem()
    if enable_shared_mem and platform.system().lower() in {"windows", "darwin"}:
        _config.set_enable_shared_mem(False)
        return False
    return enable_shared_mem


def set_enable_shared_mem(enable):
    """
    Set whether to use shared memory for interprocess communication when data processing multiprocessing is turned on.

    Using shared memory can speed up the efficiency of data transfer between processes.

    Shared memory is used by default.

    Note:
        Windows and MacOS systems are not supported yet.

    Args:
        enable (bool): Whether to use shared memory for interprocess communication.

    Raises:
        TypeError: If `enable` is not of type bool.

    Examples:
        >>> # Enable shared memory feature to improve the performance of Python multiprocessing.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_enable_shared_mem(True)
    """
    if not isinstance(enable, bool):
        raise TypeError("enable must be of type bool.")
    if enable:
        # For Windows and MacOS we forbid shared mem function temporarily
        if platform.system().lower() in {"windows", "darwin"}:
            logger.warning("For Windows and MacOS we forbid shared mem function temporarily.")
            return
        logger.warning("The shared memory is on, multiprocessing performance will be improved. "
                       "Note: the required shared memory can't exceeds 80% of the available shared memory.")
    _config.set_enable_shared_mem(enable)


def set_sending_batches(batch_num):
    """
    Set the upper limit on the number of batches of data that the Host can send to the Device.

    Can be used to implement customized data sending control logic to solve the problem of
    Device out of memory. In each epoch, when the actual number of batches sent to the Device
    reaches this value, the Host will stop continuing to send until the user increases this
    upper limit again through this interface.

    Currently, it is only supported when training in sink mode with Ascend backend, which can
    be enabled via the :class:`mindspore.train.Model.train` interface.

    Args:
        batch_num (int): The upper limit on the number of batches of data that the Host can
            send to the Device. ``0`` indicates that there is no upper limit for sending.

    Raises:
        TypeError: If `batch_num` is not of type int.

    Examples:
        >>> # Set a new global configuration value for the sending batches
        >>> import mindspore.dataset as ds
        >>> ds.config.set_sending_batches(10)
    """
    if not isinstance(batch_num, int) or isinstance(batch_num, bool):
        raise TypeError("batch_num must be an int dtype.")
    _config.set_sending_batches(batch_num)


def set_auto_offload(offload):
    """
    Set the automatic offload flag of the dataset. If set_auto_offload is True,
    automatically offload as many dataset operations from the CPU to the Device (GPU or Ascend).

    Args:
        offload (bool): Whether to use the automatic offload feature.

    Raises:
        TypeError: If offload is not a boolean data type.

    Examples:
        >>> # Enable automatic offload feature
        >>> import mindspore.dataset as ds
        >>> ds.config.set_auto_offload(True)
    """
    if not isinstance(offload, bool):
        raise TypeError("offload must be a bool dtype")
    _config.set_auto_offload(offload)


def get_auto_offload():
    """
    Get the state of the automatic offload flag (True or False), it is disabled by default.

    Returns:
        bool, Whether the automatic offload feature is enabled.

    Examples:
        >>> # Get the global configuration of the automatic offload feature.
        >>> import mindspore.dataset as ds
        >>> auto_offload = ds.config.get_auto_offload()
    """
    return _config.get_auto_offload()


def set_enable_watchdog(enable):
    """
    Set the default state of watchdog Python thread as enabled, the default state of watchdog Python thread is enabled.
    Watchdog is a thread which cleans up hanging subprocesses.

    Args:
        enable (bool): Whether to launch a watchdog Python thread.

    Raises:
        TypeError: If `enable` is not a boolean data type.

    Examples:
        >>> # Set a new global configuration value for the state of watchdog Python thread as enabled.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_enable_watchdog(True)
    """
    if not isinstance(enable, bool):
        raise TypeError("enable must be a boolean dtype.")
    _config.set_enable_watchdog(enable)


def get_enable_watchdog():
    """
    Get the state of watchdog Python thread to indicate enabled or disabled state.
    This is a Python watchdog thread used for all processes, it is enabled by default.

    Returns:
        bool, the default state of watchdog Python thread enabled.

    Examples:
        >>> # Get the global configuration of watchdog Python thread.
        >>> import mindspore.dataset as ds
        >>> watchdog_state = ds.config.get_enable_watchdog()
    """
    return _config.get_enable_watchdog()


def set_multiprocessing_timeout_interval(interval):
    """
    Set the default interval (in seconds) for multiprocessing/multithreading timeout when main process/thread gets
    data from subprocesses/child threads.

    Args:
        interval (int): Interval (in seconds) to be used for multiprocessing/multithreading timeout when main
            process/thread gets data from subprocess/child threads.

    Raises:
        TypeError: If `interval` is not of type int.
        ValueError: If `interval` <= 0 or `interval` > INT32_MAX(2147483647).

    Examples:
        >>> # Set a new global configuration value for multiprocessing/multithreading timeout when getting data.
        >>> import mindspore.dataset as ds
        >>> ds.config.set_multiprocessing_timeout_interval(300)
    """
    if not isinstance(interval, int) or isinstance(interval, bool):
        raise TypeError("interval isn't of type int.")
    if interval <= 0 or interval > INT32_MAX:
        raise ValueError(
            "Interval given is not within the required range (0, INT32_MAX(2147483647)).")
    _config.set_multiprocessing_timeout_interval(interval)


def get_multiprocessing_timeout_interval():
    """
    Get the global configuration of multiprocessing/multithreading timeout when main process/thread gets data from
    subprocesses/child threads.

    Returns:
        int, interval (in seconds) for multiprocessing/multithreading timeout when main process/thread gets data from
        subprocesses/child threads. If `set_multiprocessing_timeout_interval` is never called before,
        the default value(300) will be returned.

    Examples:
        >>> # Get the global configuration of multiprocessing/multithreading timeout when main process/thread gets data
        >>> # from subprocesses/child threads. If set_multiprocessing_timeout_interval() is never called before, the
        >>> # default value(300) will be returned.
        >>> import mindspore.dataset as ds
        >>> multiprocessing_timeout_interval = ds.config.get_multiprocessing_timeout_interval()
    """
    return _config.get_multiprocessing_timeout_interval()


def set_dynamic_shape(is_dynamic):
    """
    Set the dynamic shape flag of the dataset.

    Args:
        is_dynamic (bool): Whether the dataset is dynamic shape. Default: False

    Raises:
        TypeError: If `is_dynamic` is not a boolean data type.

    Examples:
        >>> import mindspore.dataset as ds
        >>> ds.config.set_dynamic_shape(True)
    """
    if not isinstance(is_dynamic, bool):
        raise TypeError("is_dynamic must be a boolean dtype.")
    _config.set_dynamic_shape(is_dynamic)


def get_dynamic_shape():
    """
    Get the dynamic shape flag of the dataset, it is set to False by default.

    Returns:
        bool, whether the dataset is dynamic shape.

    Examples:
        >>> import mindspore.dataset as ds
        >>> is_dynamic_shape = ds.config.get_dynamic_shape()
    """
    return _config.get_dynamic_shape()


def set_fast_recovery(fast_recovery):
    """
    Set whether dataset pipeline should recover in fast mode during failover
    (In fast mode, random augmentations may not get same results as before the failure occurred).

    Args:
        fast_recovery (bool): Whether the dataset pipeline recovers in fast mode.

    Raises:
        TypeError: If `fast_recovery` is not a boolean data type.

    Examples:
        >>> import mindspore.dataset as ds
        >>> ds.config.set_fast_recovery(False)
    """
    if not isinstance(fast_recovery, bool):
        raise TypeError("fast_recovery must be a boolean dtype.")
    _config.set_fast_recovery(fast_recovery)


def get_fast_recovery():
    """
    Get whether the fast recovery mode is enabled for the current dataset pipeline.
    It is set to True by default.

    Returns:
        bool, whether the dataset recovers fast in failover reset.

    Examples:
        >>> import mindspore.dataset as ds
        >>> is_fast_recovery = ds.config.get_fast_recovery()
    """
    return _config.get_fast_recovery()


def set_debug_mode(debug_mode_flag: bool, debug_hook_list: list = None):
    """
    Set the debug_mode flag of the dataset pipeline. When enabled, the dataset pipeline is run synchronously and
    sequentially with a single thread.

    Note:
        When debug_mode is enabled,

        - If random seed has not been set, will internally set the seed to 1.
          So that debug mode execution of the dataset pipeline can produce deterministic results.

        - The following configuration settings are ignored:

          - auto_offload (False is used.)
          - enable_autotune (False is used.)
          - error_samples_mode (ErrorSamplesMode.RETURN is used.)
          - num_parallel_workers (Value 1 is used.)

        - The `offload` parameter in `map` operation will be ignored.
        - The `python_multiprocessing` parameter in `GeneratorDataset`, `map`/`batch` operation will be ignored.
        - The `cache` parameter in Dataset loading API will be ignored.

    Args:
        debug_mode_flag (bool): Whether dataset pipeline debug mode is enabled, which forces the pipeline
            to run synchronously and sequentially.
        debug_hook_list (list[DebugHook]): a list of debug hook objects to be inserted before and after each
            transform operation in map operation. Default: ``None``, which means to use basic print hook,
            which prints shape/size/type of each input/output data of each transformation.

    Raises:
        TypeError: If `debug_mode_flag` is not a boolean data type.
        TypeError: If `debug_hook_list` is not a list type.
        TypeError: If any item in `debug_hook_list` is not DebugHook type.

    Examples:
        >>> import mindspore.dataset as ds
        >>> import mindspore.dataset.vision as vision
        >>> import mindspore.dataset.debug as debug
        >>>
        >>> # 1. Enable dataset pipeline debug mode and use default debug hook.
        >>> # Print shape and type of input/output data of each transform op in map operator.
        >>> ds.config.set_debug_mode(True)
        >>>
        >>> # 2. Enable dataset pipeline debug mode and use pre-defined debug hook provided by MindData.
        >>> ds.config.set_debug_mode(True, debug_hook_list=[debug.PrintDataHook()])
        >>>
        >>> # 3. Enable dataset pipeline debug mode and use user-defined debug hook. It must define a
        >>> # class inherited from DebugHook.
        >>> class CustomizedHook(debug.DebugHook):
        ...     def __init__(self):
        ...         super().__init__()
        ...
        ...     def compute(self, *args):
        ...         # Add your debugging code here.
        ...         return args
        >>>
        >>> ds.config.set_debug_mode(True, debug_hook_list=[CustomizedHook()])
        >>>
        >>> # 4. Enable dataset pipeline debug mode and use user-defined debug hook and insert by users manually.
        >>> ds.config.set_debug_mode(True)
        >>> dataset = ds.ImageFolderDataset(dataset_dir="/path/to/image_folder_dataset_directory")
        >>>
        >>> # The debug hook is added after Decode operation.
        >>> dataset = dataset.map([vision.Decode(), CustomizedHook(), vision.CenterCrop(100)])
    """
    if not isinstance(debug_mode_flag, bool):
        raise TypeError("debug_mode_flag isn't of type boolean.")
    if debug_hook_list is None:
        debug_hook_list = [PrintMetaDataHook()]
    if not isinstance(debug_hook_list, list):
        raise TypeError("debug_hook_list is not a list.")
    for debug_func in debug_hook_list:
        if not isinstance(debug_func, DebugHook):
            raise TypeError("All items in debug_hook_list must be of type DebugHook.")
    if debug_mode_flag:
        logger.warning("Dataset pipeline debug mode is enabled. Performance will be impacted because the pipeline"
                       " will be running in a single thread.")
    if debug_hook_list:
        _debug_context["debug_hook_list"] = debug_hook_list

    _config.set_debug_mode(debug_mode_flag)


def get_debug_mode():
    """
    Get whether debug mode is currently enabled for the data pipeline.

    Returns:
        bool, whether data pipeline debug mode is enabled.

    Examples:
        >>> import mindspore.dataset as ds
        >>> debug_mode = ds.config.get_debug_mode()
    """
    return _config.get_debug_mode()


def _get_debug_hook_list():
    """
    INTERNAL USE ONLY!
    Get value of debug_hook_list.

    Returns:
        list, the debug hook objects to be inserted in map operation to debug inputs/outputs of each transform.
    """
    return _debug_context.get("debug_hook_list")


class ErrorSamplesMode(IntEnum):
    """
    An enumeration for `error_samples_mode` .

    Possible enumeration values are: ErrorSamplesMode.RETURN, ErrorSamplesMode.REPLACE, ErrorSamplesMode.SKIP.

    - ErrorSamplesMode.RETURN: means erroneous sample results in error raised and returned.
    - ErrorSamplesMode.REPLACE: means erroneous sample is replaced with an internally determined sample.
    - ErrorSamplesMode.SKIP: means erroneous sample is skipped.
    """

    RETURN = 0
    REPLACE = 1
    SKIP = 2


# Convert ErrorSamplesMode from Python enum format to CDE enum format
_PYTHON_TO_CDE_ERROR_SAMPLES_MODE = {
    ErrorSamplesMode.RETURN: cde.ErrorSamplesMode.DE_ERROR_SAMPLES_MODE_RETURN,
    ErrorSamplesMode.REPLACE: cde.ErrorSamplesMode.DE_ERROR_SAMPLES_MODE_REPLACE,
    ErrorSamplesMode.SKIP: cde.ErrorSamplesMode.DE_ERROR_SAMPLES_MODE_SKIP
}

# Convert ErrorSamplesMode from CDE int format to Python enum format
_CDE_TO_PYTHON_ERROR_SAMPLES_MODE = {
    0: ErrorSamplesMode.RETURN,
    1: ErrorSamplesMode.REPLACE,
    2: ErrorSamplesMode.SKIP
}


def set_error_samples_mode(error_samples_mode):
    """
    Set the method in which erroneous samples should be processed in a dataset pipeline.

    Note:
        - This error samples feature is only applicable to the Map operation in a dataset pipeline.
        - For ``ErrorSamplesMode.REPLACE`` mode, a cache of other samples will be used.
        - If ``ErrorSamplesMode.SKIP`` mode is used in a distributed setting, beware to manually ensure the
          number of valid samples are the same for each shard (otherwise one may encounter hangs).
          One technique is to manually concat a dataset of all valid samples plus a
          take operation for the number of skipped erroneous samples.

    Args:
        error_samples_mode (ErrorSamplesMode): The method in which erroneous samples should be processed in a dataset
            pipeline. It can be any of [ErrorSamplesMode.RETURN, ErrorSamplesMode.REPLACE, ErrorSamplesMode.SKIP].

            - ``ErrorSamplesMode.RETURN``: means erroneous sample results in error raised and returned.

            - ``ErrorSamplesMode.REPLACE``: means erroneous sample is replaced with a correct sample.

            - ``ErrorSamplesMode.SKIP``: means erroneous sample is skipped.

    Raises:
        TypeError: If `error_samples_mode` is not of type ErrorSamplesMode.

    Examples:
        >>> import mindspore.dataset as ds
        >>> ds.config.set_error_samples_mode(ds.config.ErrorSamplesMode.SKIP)
    """
    type_check(error_samples_mode, (ErrorSamplesMode,), "error_samples_mode")
    _config.set_error_samples_mode(_PYTHON_TO_CDE_ERROR_SAMPLES_MODE.get(error_samples_mode))


def get_error_samples_mode():
    """
    Get the current configuration for strategy for processing erroneous samples in a dataset pipeline.
    If `set_error_samples_mode` is never called before, the default setting is ErrorSamplesMode.RETURN.

    Returns:
        ErrorSamplesMode, The method in which erroneous samples should be processed in a dataset pipeline.

        - ErrorSamplesMode.RETURN: means erroneous sample results in error raised and returned.
        - ErrorSamplesMode.REPLACE: means erroneous sample is replaced with an internally determined sample.
        - ErrorSamplesMode.SKIP: means erroneous sample is skipped.

    Examples:
        >>> import mindspore.dataset as ds
        >>> error_samples_mode = ds.config.get_error_samples_mode()
    """
    return _CDE_TO_PYTHON_ERROR_SAMPLES_MODE.get(_config.get_error_samples_mode())