





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_UpperCAmelCase             =							logging.get_logger(__name__)






class        snake_case_    :
  A_      							=   None






@experimental
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 if ParallelBackendConfig.backend_name is None:
  return _map_with_multiprocessing_pool(
      SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 return _map_with_joblib(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






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

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

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

 return mapped






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   ,   SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:str   )      ->   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=SCREAMING_SNAKE_CASE   ):
  return joblib.Parallel()(
      joblib.delayed(SCREAMING_SNAKE_CASE   )((function, obj, types, None, True, None)   ) for obj in iterable   )







@experimental
@contextlib.contextmanager
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   )      ->   Union[str, Any]:
 __lowerCAmelCase					:	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:
  __lowerCAmelCase					:	Tuple      =       None






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()