

import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
snake_case_        =			{
    '''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,
}
snake_case_        =			'''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
snake_case_        =			'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	{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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   tuple      ):



     '''simple docstring'''





     return x[0]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       str				=	get_letter_count(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       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(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       dict[int, str]				=	{}

     for freq in freq_to_letter:
          freq_to_letter[freq].sort(key=ETAOIN.find     ,					reverse=SCREAMING_SNAKE_CASE_      )
          lowercase__   :       str				=	''.join(freq_to_letter[freq]      )

     lowercase__   :       Tuple				=	list(freq_to_letter_str.items()      )
     freq_pairs.sort(key=SCREAMING_SNAKE_CASE_     ,					reverse=SCREAMING_SNAKE_CASE_      )

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

     return "".join(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	get_frequency_order(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[int]				=	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()




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a = None						,       a = None						,       a = True						,       a = None						,       a = False						,       a = None						,       a = True						,       a = "arrow"						,       **a						,       ):
         super().__init__(
             split=a						,       features=a						,       cache_dir=a						,       keep_in_memory=a						,       streaming=a						,       **a						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       Dict				=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
         self.builder.download_and_prepare(
             download_mode=a						,       file_format=self._file_format						,       )
         return self.builder.as_dataset(split=self.split)

