






"""simple docstring"""



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							None




@experimental
def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					str      )		->str:
		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__       :					Dict,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					str      )		->Tuple:
		A__						:  Dict		  =  num_proc if num_proc <= len(__UpperCamelCase      ) else len(__UpperCamelCase      )
		A__						:  List[Any]		  =  []  # We organize the splits ourselve (contiguous splits)
		for index in range(__UpperCamelCase      ):
				A__						:  str		  =  len(__UpperCamelCase      ) // num_proc
				A__						:  Any		  =  len(__UpperCamelCase      ) % num_proc
				A__						:  Any		  =  div * index + min(__UpperCamelCase,						__UpperCamelCase      )
				A__						:  List[str]		  =  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]}'      )
		A__  ,  A__						:  Union[str, Any]		  =  None, None
		if not disable_tqdm:
				A__  ,  A__						:  str		  =  (RLock(),), tqdm.set_lock
		with Pool(__UpperCamelCase,						initargs=__UpperCamelCase,						initializer=__UpperCamelCase      ) as pool:
				A__						:  List[Any]		  =  pool.map(__UpperCamelCase,						__UpperCamelCase      )
		logger.info(f'Finished {num_proc} processes'      )
		A__						:  Optional[Any]		  =  [obj for proc_res in mapped for obj in proc_res]
		logger.info(f'Unpacked {len(__UpperCamelCase      )} objects'      )

		return mapped




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					int      )		->Tuple:
		# 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__       :					List[str]      )		->List[str]:
		A__						:  Optional[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__						:  Optional[Any]		  =  None














"""simple docstring"""



import baseaa




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->bytes:
						return baseaa.baaencode(string.encode("""utf-8"""      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					bytes      )		->str:
						return baseaa.baadecode(UpperCAmelCase__      ).decode("""utf-8"""      )


if __name__ == "__main__":
						A_    =							'''Hello World!'''
						A_    =							baseaa_encode(test)
						print(encoded)

						A_    =							baseaa_decode(encoded)
						print(decoded)






