


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				None:
  snake_case__				,				snake_case__						= analyze_text(__lowerCAmelCase				)
  snake_case__						= list(''' ''' + ascii_lowercase				)
  # what is our total sum of probabilities.
  snake_case__						= sum(single_char_strings.values()				)

  # one length string
  snake_case__						= 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:
      snake_case__						= single_char_strings[ch]
      snake_case__						= my_str / all_sum
      my_fir_sum += prob * math.loga(__lowerCAmelCase				)  # entropy formula.

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

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

    # 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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				tuple[dict, dict]:
  snake_case__						= Counter()  # type: ignore
  snake_case__						= 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(__lowerCAmelCase				) - 1				):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings







def SCREAMING_SNAKE_CASE	(			)    ->				int:
  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()







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[float]     =      field(
     default=0.0     ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''whether to use adafactor'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''linear'''     ,      metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}     ,      )

