


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A   :					str	=      logging.get_logger(__name__)
A   :					Tuple	=      {"vocab_file": "spiece.model"}

A   :					Union[str, Any]	=      {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

A   :					Optional[Any]	=      {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

A   :					List[Any]	=      "▁"







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    VOCAB_FILES_NAMES
       A__      		=    PRETRAINED_VOCAB_FILES_MAP
       A__      		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





       def __init__(     self							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Union[str, Any]=False      ,   __lowerCamelCase							:    Union[str, Any]="[CLS]"      ,   __lowerCamelCase							:    List[str]="[SEP]"      ,   __lowerCamelCase							:    Optional[int]="<unk>"      ,   __lowerCamelCase							:    Optional[Any]="[SEP]"      ,   __lowerCamelCase							:    str="<pad>"      ,   __lowerCamelCase							:    Optional[Any]="[CLS]"      ,   __lowerCamelCase							:    List[str]="[MASK]"      ,   __lowerCamelCase							:    Optional[Dict[str, Any]] = None      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						(
                 AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase      ,   normalized=__lowerCamelCase )
                 if isinstance(__lowerCamelCase      ,   __lowerCamelCase )
                 else mask_token
             )

             lowerCamelCase__	:       List[str]					=						{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 do_lower_case=__lowerCamelCase      ,   remove_space=__lowerCamelCase      ,   keep_accents=__lowerCamelCase      ,   bos_token=__lowerCamelCase      ,   eos_token=__lowerCamelCase      ,   unk_token=__lowerCamelCase      ,   sep_token=__lowerCamelCase      ,   pad_token=__lowerCamelCase      ,   cls_token=__lowerCamelCase      ,   mask_token=__lowerCamelCase      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **__lowerCamelCase      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						do_lower_case
             lowerCamelCase__	:       Optional[Any]					=						remove_space
             lowerCamelCase__	:       int					=						keep_accents
             lowerCamelCase__	:       Dict					=						vocab_file

             lowerCamelCase__	:       Union[str, Any]					=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
             self.sp_model.Load(__lowerCamelCase )





       @property
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             return len(self.sp_model )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						{self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
             vocab.update(self.added_tokens_encoder )
             return vocab





       def __getstate__(     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__dict__.copy()
             lowerCamelCase__	:       Optional[int]					=						None
             return state





       def __setstate__(     self							:    Tuple      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						d

             # for backward compatibility
             if not hasattr(self      ,   "sp_model_kwargs" ):
                   lowerCamelCase__	:       Dict					=						{}

             lowerCamelCase__	:       Tuple					=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
             self.sp_model.Load(self.vocab_file )





       def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             if self.remove_space:
                   lowerCamelCase__	:       List[Any]					=						" ".join(inputs.strip().split() )
             else:
                   lowerCamelCase__	:       str					=						inputs
             lowerCamelCase__	:       List[str]					=						outputs.replace("``"      ,   "\"" ).replace("''"      ,   "\"" )

             if not self.keep_accents:
                   lowerCamelCase__	:       Tuple					=						unicodedata.normalize("NFKD"      ,   __lowerCamelCase )
                   lowerCamelCase__	:       str					=						"".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase )] )
             if self.do_lower_case:
                   lowerCamelCase__	:       Any					=						outputs.lower()

             return outputs





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.preprocess_text(__lowerCamelCase )
             lowerCamelCase__	:       str					=						self.sp_model.encode(__lowerCamelCase      ,   out_type=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						[]
             for piece in pieces:
                   if len(__lowerCamelCase ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
                         lowerCamelCase__	:       int					=						self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase      ,   "" ) )
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                               if len(cur_pieces[0] ) == 1:
                                     lowerCamelCase__	:       List[Any]					=						cur_pieces[1:]
                               else:
                                     lowerCamelCase__	:       Tuple					=						cur_pieces[0][1:]
                         cur_pieces.append(piece[-1] )
                         new_pieces.extend(__lowerCamelCase )
                   else:
                         new_pieces.append(__lowerCamelCase )

             return new_pieces





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             return self.sp_model.PieceToId(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             return self.sp_model.IdToPiece(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						[]
             lowerCamelCase__	:       Dict					=						""
             lowerCamelCase__	:       int					=						False
             for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                         if not prev_is_special:
                               out_string += " "
                         out_string += self.sp_model.decode(__lowerCamelCase ) + token
                         lowerCamelCase__	:       Dict					=						True
                         lowerCamelCase__	:       Optional[Any]					=						[]
                   else:
                         current_sub_tokens.append(__lowerCamelCase )
                         lowerCamelCase__	:       Optional[Any]					=						False
             out_string += self.sp_model.decode(__lowerCamelCase )
             return out_string.strip()





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						[self.sep_token_id]
             lowerCamelCase__	:       List[str]					=						[self.cls_token_id]
             if token_ids_a is None:
                   return cls + token_ids_a + sep
             return cls + token_ids_a + sep + token_ids_a + sep





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None      ,   __lowerCamelCase							:    bool = False ):


             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=__lowerCamelCase      ,   token_ids_a=__lowerCamelCase      ,   already_has_special_tokens=__lowerCamelCase )

             if token_ids_a is not None:
                   return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]
             return [1] + ([0] * len(__lowerCamelCase )) + [1]





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						[self.sep_token_id]
             lowerCamelCase__	:       List[str]					=						[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 ) * [0] + len(token_ids_a + sep ) * [1]





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[str] = None ):


             '''simple docstring'''



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

             if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
                   copyfile(self.vocab_file      ,   __lowerCamelCase )
             elif not os.path.isfile(self.vocab_file ):
                   with open(__lowerCamelCase      ,   "wb" ) as fi:
                         lowerCamelCase__	:       Tuple					=						self.sp_model.serialized_model_proto()
                         fi.write(__lowerCamelCase )

             return (out_vocab_file,)






from __future__ import annotations

def    lowercase_	(	_A    :					str					,      _A    :					list[str] | None = None					,      _A    :					dict[str, float] | None = None					,      _A    :					bool = False					,      ):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						cipher_alphabet or [chr(_A		) for i in range(97					,      123		)]

      # If the argument is None or the user provided an empty dictionary
      if not frequencies_dict:
            # Frequencies of letters in the english language (how much they show up)
            lowerCamelCase__	:       Dict					=						{
                "a": 0.08_497,
                "b": 0.01_492,
                "c": 0.02_202,
                "d": 0.04_253,
                "e": 0.11_162,
                "f": 0.02_228,
                "g": 0.02_015,
                "h": 0.06_094,
                "i": 0.07_546,
                "j": 0.00_153,
                "k": 0.01_292,
                "l": 0.04_025,
                "m": 0.02_406,
                "n": 0.06_749,
                "o": 0.07_507,
                "p": 0.01_929,
                "q": 0.00_095,
                "r": 0.07_587,
                "s": 0.06_327,
                "t": 0.09_356,
                "u": 0.02_758,
                "v": 0.00_978,
                "w": 0.02_560,
                "x": 0.00_150,
                "y": 0.01_994,
                "z": 0.00_077,
            }
      else:
            # Custom frequencies dictionary
            lowerCamelCase__	:       Optional[int]					=						frequencies_dict

      if not case_sensitive:
            lowerCamelCase__	:       str					=						ciphertext.lower()

      # Chi squared statistic values
      lowerCamelCase__	:       dict[int, tuple[float, str]]					=						{}

      # cycle through all of the shifts
      for shift in range(len(_A		)		):
            lowerCamelCase__	:       Optional[Any]					=						""

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        lowerCamelCase__	:       Dict					=						(alphabet_letters.index(letter.lower()		) - shift) % len(
                            _A		)
                        decrypted_with_shift += (
                            alphabet_letters[new_key].upper()
                            if case_sensitive and letter.isupper()
                            else alphabet_letters[new_key]
                        )
                  except ValueError:
                        # Append the character if it isn't in the alphabet
                        decrypted_with_shift += letter

            lowerCamelCase__	:       str					=						0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        lowerCamelCase__	:       List[str]					=						letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              lowerCamelCase__	:       List[str]					=						decrypted_with_shift.lower().count(_A		)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              lowerCamelCase__	:       List[Any]					=						frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       str					=						((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value
                  else:
                        if letter.lower() in frequencies:
                              # Get the amount of times the letter occurs in the message
                              lowerCamelCase__	:       Any					=						decrypted_with_shift.count(_A		)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              lowerCamelCase__	:       str					=						frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       int					=						((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
            lowerCamelCase__	:       Optional[int]					=						(
                chi_squared_statistic,
                decrypted_with_shift,
            )

      # Get the most likely cipher by finding the cipher with the smallest chi squared
      # statistic
      def chi_squared_statistic_values_sorting_key(_A    :					int		) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      lowerCamelCase__	:       int					=						min(
          _A					,      key=_A					,      )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          lowerCamelCase__
      )					,     (
          lowerCamelCase__
      )					,     
      )	:       int					=						chi_squared_statistic_values[most_likely_cipher]

      # Return the data on the most likely shift
      return (
          most_likely_cipher,
          most_likely_cipher_chi_squared_value,
          decoded_most_likely_cipher,
      )


