



"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							:





     '''simple docstring'''



     a__									=None





@experimental
def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Optional[int]							):
    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

    return _map_with_joblib(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Any											=    num_proc if num_proc <= len(UpperCamelCase__							) else len(UpperCamelCase__							)
    _UpperCAmelCase							:  Union[str, Any]											=    []  # We organize the splits ourselve (contiguous splits)
    for index in range(UpperCamelCase__							):
        _UpperCAmelCase							:  Optional[Any]											=    len(UpperCamelCase__							) // num_proc
        _UpperCAmelCase							:  Optional[int]											=    len(UpperCamelCase__							) % num_proc
        _UpperCAmelCase							:  int											=    div * index + min(UpperCamelCase__     ,       UpperCamelCase__							)
        _UpperCAmelCase							:  Union[str, Any]											=    start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

    if len(UpperCamelCase__							) != sum(len(i[1]							) for i in split_kwds							):
        raise ValueError(
            F'Error dividing inputs iterable among processes. '
            F'Total number of objects {len(UpperCamelCase__							)}, '
            F'length: {sum(len(i[1]							) for i in split_kwds							)}'							)

    logger.info(
        F'Spawning {num_proc} processes for {len(UpperCamelCase__							)} objects in slices of {[len(i[1]							) for i in split_kwds]}'							)
    _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    None, None
    if not disable_tqdm:
        _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    (RLock(),), tqdm.set_lock
    with Pool(UpperCamelCase__     ,       initargs=UpperCamelCase__     ,       initializer=UpperCamelCase__							) as pool:
        _UpperCAmelCase							:  Optional[Any]											=    pool.map(UpperCamelCase__     ,       UpperCamelCase__							)
    logger.info(F'Finished {num_proc} processes'							)
    _UpperCAmelCase							:  List[str]											=    [obj for proc_res in mapped for obj in proc_res]
    logger.info(F'Unpacked {len(UpperCamelCase__							)} objects'							)

    return mapped





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		int							):
    # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
    # and it requires monkey-patching joblib internal classes which is subject to change
    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name     ,       n_jobs=UpperCamelCase__							):
        return joblib.Parallel()(
            joblib.delayed(UpperCamelCase__							)((function, obj, types, None, True, None)							) for obj in iterable							)





@experimental
@contextlib.contextmanager
def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[Any]											=    backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        _UpperCAmelCase							:  Dict											=    None







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


