
"""simple docstring"""

import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


UpperCamelCase     :    Dict       =			logging.get_logger(__name__)

class __lowerCAmelCase		:
     lowercase						       =						None






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

 return _map_with_joblib(snake_case    , snake_case    , snake_case    , snake_case    , snake_case    , snake_case    , snake_case		)






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

 if len(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(snake_case		)}, '
      f'length: {sum(len(i[1]		) for i in split_kwds		)}'		)

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

 return mapped






def 							A						(    snake_case				:Optional[int]    , snake_case				:Dict    , snake_case				:Dict    , snake_case				:Tuple    , snake_case				:Tuple    , snake_case				:Optional[int]    , snake_case				:Any		)				->     Any:
 # 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=snake_case		):
  return joblib.Parallel()(
      joblib.delayed(snake_case		)((function, obj, types, None, True, None)		) for obj in iterable		)






@experimental
@contextlib.contextmanager
def 							A						(    snake_case				:str		)				->     List[Any]:
 __UpperCamelCase      =	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:
  __UpperCamelCase      =	None





"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


UpperCamelCase     :    Optional[int]       =			logging.get_logger(__name__)

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'     ,					__UpperCAmelCase     ,					)
      super().__init__(*__UpperCAmelCase     ,					**__UpperCAmelCase				)



