
"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    analyze_text(__lowerCamelCase  )
   UpperCAmelCase_					:					List[Any]			     =    list(" " + ascii_lowercase  )
   # what is our total sum of probabilities.
   UpperCAmelCase_					:					Optional[Any]			     =    sum(single_char_strings.values()  )

   # one length string
   UpperCAmelCase_					:					Optional[int]			     =    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_					:					Dict			     =    single_char_strings[ch]
         UpperCAmelCase_					:					List[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_					:					int			     =    sum(two_char_strings.values()  )
   UpperCAmelCase_					:					Tuple			     =    0
   # for each alpha (two in size) calculate entropy.
   for cha in my_alphas:
      for cha in my_alphas:
         UpperCAmelCase_					:					Tuple			     =    cha + cha
         if sequence in two_char_strings:
            UpperCAmelCase_					:					Dict			     =    two_char_strings[sequence]
            UpperCAmelCase_					:					Optional[int]			     =    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 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    Counter()  # type: ignore
   UpperCAmelCase_					:					List[str]			     =    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 	__a     (		):
   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"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a		       =							{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""albert"""







  def __init__(		self				,   lowercase_=3_0000				,   lowercase_=128				,   lowercase_=4096				,   lowercase_=12				,   lowercase_=1				,   lowercase_=64				,   lowercase_=1_6384				,   lowercase_=1				,   lowercase_="gelu_new"				,   lowercase_=0				,   lowercase_=0				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=0.1				,   lowercase_="absolute"				,   lowercase_=0				,   lowercase_=2				,   lowercase_=3				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_				,   bos_token_id=lowercase_				,   eos_token_id=lowercase_				,   **lowercase_  )

     UpperCAmelCase_					:					int			     =    vocab_size
     UpperCAmelCase_					:					Optional[int]			     =    embedding_size
     UpperCAmelCase_					:					List[str]			     =    hidden_size
     UpperCAmelCase_					:					Optional[int]			     =    num_hidden_layers
     UpperCAmelCase_					:					Union[str, Any]			     =    num_hidden_groups
     UpperCAmelCase_					:					Dict			     =    num_attention_heads
     UpperCAmelCase_					:					Any			     =    inner_group_num
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_act
     UpperCAmelCase_					:					Union[str, Any]			     =    intermediate_size
     UpperCAmelCase_					:					List[str]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Union[str, Any]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    max_position_embeddings
     UpperCAmelCase_					:					Any			     =    type_vocab_size
     UpperCAmelCase_					:					List[str]			     =    initializer_range
     UpperCAmelCase_					:					Optional[int]			     =    layer_norm_eps
     UpperCAmelCase_					:					List[Any]			     =    classifier_dropout_prob
     UpperCAmelCase_					:					Tuple			     =    position_embedding_type




class A_	(lowercase__      ):




  '''simple docstring'''







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_					:					int			     =    {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]  )


