
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					analyze_text(_lowerCamelCase)
							__UpperCamelCase	:   Optional[int]			   =					list(" " + ascii_lowercase)
							# what is our total sum of probabilities.
							__UpperCamelCase	:   int			   =					sum(single_char_strings.values())

							# one length string
							__UpperCamelCase	:   Optional[Any]			   =					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	:   List[str]			   =					single_char_strings[ch]
																					__UpperCamelCase	:   str			   =					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
							__UpperCamelCase	:   Optional[int]			   =					sum(two_char_strings.values())
							__UpperCamelCase	:   List[str]			   =					0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					__UpperCamelCase	:   Union[str, Any]			   =					cha + cha
																					if sequence in two_char_strings:
																												__UpperCamelCase	:   List[Any]			   =					two_char_strings[sequence]
																												__UpperCamelCase	:   Any			   =					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 :			str)					->    Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					Counter()  # type: ignore
							__UpperCamelCase	:   Tuple			   =					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:






							'''simple docstring'''





							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()
from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



