



"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       UpperCAmelCase						:		Any			= None


UpperCAmelCase						:		Any			= logging.get_logger(__name__)

UpperCAmelCase						:		Dict			= {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase						:		Optional[Any]			= {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase						:		Optional[int]			= {
    "camembert-base": 512,
}

UpperCAmelCase						:		Dict			= "▁"


class    SCREAMING_SNAKE_CASE__ (							__SCREAMING_SNAKE_CASE   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   ['input_ids', 'attention_mask']
    lowercase__      =   CamembertTokenizer

    def __init__(				self			:				Any ,  lowerCAmelCase_			:				List[str]=None ,  lowerCAmelCase_			:				int=None ,  lowerCAmelCase_			:				Tuple="<s>" ,  lowerCAmelCase_			:				Optional[int]="</s>" ,  lowerCAmelCase_			:				str="</s>" ,  lowerCAmelCase_			:				Union[str, Any]="<s>" ,  lowerCAmelCase_			:				Tuple="<unk>" ,  lowerCAmelCase_			:				List[str]="<pad>" ,  lowerCAmelCase_			:				int="<mask>" ,  lowerCAmelCase_			:				Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"] ,  **lowerCAmelCase_			:				List[Any] ,  ):







      """simple docstring"""







      lowercase_								= AddedToken(_a ,  lstrip=_a ,  rstrip=_a) if isinstance(_a ,  _a) else mask_token

      super().__init__(
          _a ,  tokenizer_file=_a ,  bos_token=_a ,  eos_token=_a ,  sep_token=_a ,  cls_token=_a ,  unk_token=_a ,  pad_token=_a ,  mask_token=_a ,  additional_special_tokens=_a ,  **_a ,  )

      lowercase_								= vocab_file
      lowercase_								= False if not self.vocab_file else True

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Any = None):







      """simple docstring"""







      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowercase_								= [self.cls_token_id]
      lowercase_								= [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[int] = None):







      """simple docstring"""







      lowercase_								= [self.sep_token_id]
      lowercase_								= [self.cls_token_id]

      if token_ids_a is None:
        return len(cls + token_ids_a + sep) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str = None):







      """simple docstring"""







      if not self.can_save_slow_tokenizer:
        raise ValueError(
            """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
            """tokenizer.""")

      if not os.path.isdir(_a):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
        return
      lowercase_								= os.path.join(
          _a ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

      if os.path.abspath(self.vocab_file) != os.path.abspath(_a):
        copyfile(self.vocab_file ,  _a)

      return (out_vocab_file,)






"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


  lowercase_					,    lowercase_								= analyze_text(__lowerCAmelCase							)
  lowercase_								= list(""" """ + ascii_lowercase							)
  # what is our total sum of probabilities.
  lowercase_								= sum(single_char_strings.values()							)

  # one length string
  lowercase_								= 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:
      lowercase_								= single_char_strings[ch]
      lowercase_								= 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
  lowercase_								= sum(two_char_strings.values()							)
  lowercase_								= 0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowercase_								= cha + cha
      if sequence in two_char_strings:
        lowercase_								= two_char_strings[sequence]
        lowercase_								= 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]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= 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						() ->						str:







  '''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()

