



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'''




      UpperCamelCase__      :			int      =  None







@experimental
def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__							,       a__							,       a__       )    ->			Optional[int]:
       if ParallelBackendConfig.backend_name is None:
              return _map_with_multiprocessing_pool(
                  a__							,       a__							,       a__							,       a__							,       a__							,       a__							,       a__       )

       return _map_with_joblib(a__							,       a__							,       a__							,       a__							,       a__							,       a__							,       a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__							,       a__							,       a__       )    ->			str:
       __SCREAMING_SNAKE_CASE									= num_proc if num_proc <= len(a__       ) else len(a__       )
       __SCREAMING_SNAKE_CASE									= []  # We organize the splits ourselve (contiguous splits)
       for index in range(a__       ):
              __SCREAMING_SNAKE_CASE									= len(a__       ) // num_proc
              __SCREAMING_SNAKE_CASE									= len(a__       ) % num_proc
              __SCREAMING_SNAKE_CASE									= div * index + min(a__							,       a__       )
              __SCREAMING_SNAKE_CASE									= start + div + (1 if index < mod else 0)
              split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)       )

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

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

       return mapped






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__							,       a__							,       a__       )    ->			Dict:
       # 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=a__       ):
              return joblib.Parallel()(
                  joblib.delayed(a__       )((function, obj, types, None, True, None)       ) for obj in iterable       )






@experimental
@contextlib.contextmanager
def      __lowercase       (			a__       )    ->			str:
       __SCREAMING_SNAKE_CASE									= 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:
              __SCREAMING_SNAKE_CASE									= None











def      __lowercase       (			a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= [[0 for _ in range(a__       )] for _ in range(m + 1       )]
							for i in range(m + 1       ):
														__SCREAMING_SNAKE_CASE									= 1

							for n in range(m + 1       ):
														for k in range(1							,       a__       ):
																					memo[n][k] += memo[n][k - 1]
																					if n - k > 0:
																												memo[n][k] += memo[n - k - 1][k]

							return memo[m][m - 1]


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						try:
									lowerCAmelCase__		:					Optional[Any]							=int(input('''Enter a number: ''').strip())
									print(partition(n))
						except ValueError:
									print('''Please enter a number.''')
			else:
						try:
									lowerCAmelCase__		:					str							=int(sys.argv[1])
									print(partition(n))
						except ValueError:
									print('''Please pass a number.''')






