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     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = "arrow"   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





       super().__init__(
           split=SCREAMING_SNAKE_CASE_   ,				features=SCREAMING_SNAKE_CASE_   ,				cache_dir=SCREAMING_SNAKE_CASE_   ,				keep_in_memory=SCREAMING_SNAKE_CASE_   ,				streaming=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)
       UpperCamelCase__										=      load_from_cache_file
       UpperCamelCase__										=      file_format
       UpperCamelCase__										=      Spark(
           df=SCREAMING_SNAKE_CASE_   ,				features=SCREAMING_SNAKE_CASE_   ,				cache_dir=SCREAMING_SNAKE_CASE_   ,				working_dir=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       if self.streaming:
            return self.builder.as_streaming_dataset(split=self.split	)
       UpperCamelCase__										=      None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
       self.builder.download_and_prepare(
           download_mode=SCREAMING_SNAKE_CASE_   ,				file_format=self._file_format   ,				)
       return self.builder.as_dataset(split=self.split	)




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     UpperCamelCase__										=      list(''' ''' + ascii_lowercase							)
     # what is our total sum of probabilities.
     UpperCamelCase__										=      sum(single_char_strings.values()							)

     # one length string
     UpperCamelCase__										=      0
     # for each alpha we go in our dict and if it is in it we calculate entropy
     for ch in my_alphas:
          if ch in single_char_strings:
               UpperCamelCase__										=      single_char_strings[ch]
               UpperCamelCase__										=      my_str / all_sum
               my_fir_sum += prob * math.loga(A							)  # entropy formula.

    # print entropy
     print(f"{round(-1 * my_fir_sum							):.1f}"							)

     # two len string
     UpperCamelCase__										=      sum(two_char_strings.values()							)
     UpperCamelCase__										=      0
     # for each alpha (two in size) calculate entropy.
     for cha in my_alphas:
          for cha in my_alphas:
               UpperCamelCase__										=      cha + cha
               if sequence in two_char_strings:
                    UpperCamelCase__										=      two_char_strings[sequence]
                    UpperCamelCase__										=      int(A							) / all_sum
                    my_sec_sum += prob * math.loga(A							)

    # print second entropy
     print(f"{round(-1 * my_sec_sum							):.1f}"							)

     # print the difference between them
     print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      Counter()  # type: ignore
     UpperCamelCase__										=      Counter()  # type: ignore
     single_char_strings[text[-1]] += 1

     # first case when we have space at start.
     two_char_strings[" " + text[0]] += 1
     for i in range(0 ,							len(A							) - 1							):
          single_char_strings[text[i]] += 1
          two_char_strings[text[i : i + 2]] += 1
     return single_char_strings, two_char_strings







def  __UpperCamelCase    (						):
     import doctest

     doctest.testmod()
     # text = (
     #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
     #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
     #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
     #     "beloved evident through no service elderly is. Blind there if every no so "
     #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
     #     "of message cottage windows do besides against uncivil.  Delightful "
     #     "unreserved impossible few estimating men favourable see entreaties. She "
     #     "propriety immediate was improving. He or entrance humoured likewise "
     #     "moderate. Much nor game son say feel. Fat make met can must form into "
     #     "gate. Me we offending prevailed discovery. "
     # )

     # calculate_prob(text)


if __name__ == "__main__":
     main()



