"""simple docstring"""




import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_a				:		Dict	   =       {
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
_a				:		List[str]	   =       'ETAOINSHRDLCUMWFGYPBVKJXQZ'
_a				:		Any	   =       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				dict[str, int]:
  _lowerCAmelCase						:	Optional[Any]        =					{letter: 0 for letter in string.ascii_uppercase}
  for letter in message.upper():
    if letter in LETTERS:
      letter_count[letter] += 1

  return letter_count





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	tuple     )	->				str:
  return x[0]





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Optional[int]        =					get_letter_count(_lowerCamelCase     )
  _lowerCAmelCase						:	dict[int, list[str]]        =					{
      freq: [] for letter, freq in letter_to_freq.items()
  }
  for letter in LETTERS:
    freq_to_letter[letter_to_freq[letter]].append(_lowerCamelCase     )

  _lowerCAmelCase						:	dict[int, str]        =					{}

  for freq in freq_to_letter:
    freq_to_letter[freq].sort(key=ETAOIN.find      ,reverse=_lowerCamelCase     )
    _lowerCAmelCase						:	str        =					"""""".join(freq_to_letter[freq]     )

  _lowerCAmelCase						:	Tuple        =					list(freq_to_letter_str.items()     )
  freq_pairs.sort(key=_lowerCamelCase      ,reverse=_lowerCamelCase     )

  _lowerCAmelCase						:	list[str]        =					[freq_pair[1] for freq_pair in freq_pairs]

  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				int:
  _lowerCAmelCase						:	List[str]        =					get_frequency_order(_lowerCamelCase     )
  _lowerCAmelCase						:	Tuple        =					0
  for common_letter in ETAOIN[:6]:
    if common_letter in freq_order[:6]:
      match_score += 1

  for uncommon_letter in ETAOIN[-6:]:
    if uncommon_letter in freq_order[-6:]:
      match_score += 1

  return match_score


if __name__ == "__main__":
     import doctest

     doctest.testmod()





"""simple docstring"""




import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							:
     _UpperCamelCase							:      str		 =		None







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

  return mapped





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






@experimental
@contextlib.contextmanager
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				Any:
  _lowerCAmelCase						:	List[Any]        =					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						:	Optional[Any]        =					None




