

import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


lowerCamelCase           =					logging.get_logger(__name__)







class 						__magic_name__			:

   '''simple docstring'''




   lowerCamelCase__ :	Tuple    =   None





@experimental
def 			UpperCAmelCase__				(			_A      : int	,      _A      : Optional[Any]	,      _A      : str	,      _A      : Optional[int]	,      _A      : Optional[Any]	,      _A      : int	,      _A      : List[str]							):





  '''simple docstring'''







  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 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : str	,      _A      : int	,      _A      : Optional[Any]	,      _A      : List[Any]	,      _A      : Dict	,      _A      : Optional[int]							):





  '''simple docstring'''







  a__  =num_proc if num_proc <= len(_lowerCamelCase							) else len(_lowerCamelCase							)
  a__  =[]  # We organize the splits ourselve (contiguous splits)
  for index in range(_lowerCamelCase							):
    a__  =len(_lowerCamelCase							) // num_proc
    a__  =len(_lowerCamelCase							) % num_proc
    a__  =div * index + min(_lowerCamelCase	,      _lowerCamelCase							)
    a__  =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__  =None, None
  if not disable_tqdm:
    a__  =(RLock(),), tqdm.set_lock
  with Pool(_lowerCamelCase	,      initargs=_lowerCamelCase	,      initializer=_lowerCamelCase							) as pool:
    a__  =pool.map(_lowerCamelCase	,      _lowerCamelCase							)
  logger.info(F"""Finished {num_proc} processes"""							)
  a__  =[obj for proc_res in mapped for obj in proc_res]
  logger.info(F"""Unpacked {len(_lowerCamelCase							)} objects"""							)

  return mapped




def 			UpperCAmelCase__				(			_A      : Tuple	,      _A      : str	,      _A      : Union[str, Any]	,      _A      : Union[str, Any]	,      _A      : List[Any]	,      _A      : Union[str, Any]	,      _A      : List[Any]							):





  '''simple docstring'''







  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 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







  a__  =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__  =None





'''simple docstring'''




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)