





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCAmelCase				:   int						       =						logging.get_logger(__name__)


class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  None




@experimental
def   __snake_case    (       _lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Optional[int]					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			Dict					,					_lowerCAmelCase			:			Dict					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			List[str]						)			->    int:
   if ParallelBackendConfig.backend_name is None:
      return _map_with_multiprocessing_pool(
          _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)

   return _map_with_joblib(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)

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

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

   logger.info(
       f"Spawning {num_proc} processes for {len(_lowerCAmelCase						)} objects in slices of {[len(i[1]						) for i in split_kwds]}"						)
   A_   ,    A_       :					Dict        =      None, None
   if not disable_tqdm:
      A_   ,    A_       :					Any        =      (RLock(),), tqdm.set_lock
   with Pool(_lowerCAmelCase					,					initargs=_lowerCAmelCase					,					initializer=_lowerCAmelCase						) as pool:
      A_       :					str        =      pool.map(_lowerCAmelCase					,					_lowerCAmelCase						)
   logger.info(f"Finished {num_proc} processes"						)
   A_       :					int        =      [obj for proc_res in mapped for obj in proc_res]
   logger.info(f"Unpacked {len(_lowerCAmelCase						)} objects"						)

   return mapped

def   __snake_case    (       _lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Union[str, Any]						)			->    List[str]:
   # 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=_lowerCAmelCase						):
      return joblib.Parallel()(
          joblib.delayed(_lowerCAmelCase						)((function, obj, types, None, True, None)						) for obj in iterable						)





@experimental
@contextlib.contextmanager
def   __snake_case    (       _lowerCAmelCase			:			str						)			->    int:
   A_       :					int        =      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:
      A_       :					Union[str, Any]        =      None







def   __snake_case    (       _lowerCAmelCase			:			int = 1					,					_lowerCAmelCase			:			int = 1000						)			->    int:
   A_       :					Optional[int]        =      1
   A_       :					int        =      0

   for divide_by_number in range(_lowerCAmelCase					,					digit + 1						):
      A_       :					list[int]        =      []
      A_       :					Union[str, Any]        =      numerator
      for _ in range(1					,					digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(_lowerCAmelCase						):
               A_       :					Optional[Any]        =      len(_lowerCAmelCase						)
               A_       :					Union[str, Any]        =      divide_by_number
         else:
            has_been_divided.append(_lowerCAmelCase						)
            A_       :					Dict        =      now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()
