






from __future__ import annotations






def     _snake_case    (__lowercase    ,      __lowercase = None    ,      __lowercase = None    ,      __lowercase = False    ,      ):
   UpperCamelCase_									=     cipher_alphabet or [chr(__lowercase) 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)
      UpperCamelCase_									=     {
          '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
      UpperCamelCase_									=     frequencies_dict

   if not case_sensitive:
      UpperCamelCase_									=     ciphertext.lower()

   # Chi squared statistic values
   UpperCamelCase_									=     {}

   # cycle through all of the shifts
   for shift in range(len(__lowercase)):
      UpperCamelCase_									=     ''

      # decrypt the message with the shift
      for letter in ciphertext:
         try:
            # Try to index the letter in the alphabet
            UpperCamelCase_									=     (alphabet_letters.index(letter.lower()) - shift) % len(
                __lowercase)
            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

      UpperCamelCase_									=     0.0

      # Loop through each letter in the decoded message with the shift
      for letter in decrypted_with_shift:
         if case_sensitive:
            UpperCamelCase_									=     letter.lower()
            if letter in frequencies:
               # Get the amount of times the letter occurs in the message
               UpperCamelCase_									=     decrypted_with_shift.lower().count(__lowercase)

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

               # Complete the chi squared statistic formula
               UpperCamelCase_									=     ((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
               UpperCamelCase_									=     decrypted_with_shift.count(__lowercase)

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

               # Complete the chi squared statistic formula
               UpperCamelCase_									=     ((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
      UpperCamelCase_									=     (
          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(__lowercase) -> tuple[float, str]:
      return chi_squared_statistic_values[key]

   UpperCamelCase_									=     min(
       __lowercase    ,      key=__lowercase    ,      )

   # Get all the data from the most likely cipher (key, decoded message)
   (
       (
       UpperCamelCase_
   )		,      (
       UpperCamelCase_
   )		,      
   )									=     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,
   )













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


snake_case__      :	List[str]       	=      logging.get_logger(__name__)

snake_case__      :	Optional[int]       	=      {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case__      :	Optional[Any]       	=      {
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case__      :	List[str]       	=      {
    """google/realm-cc-news-pretrained-embedder""": 5_1_2,
    """google/realm-cc-news-pretrained-encoder""": 5_1_2,
    """google/realm-cc-news-pretrained-scorer""": 5_1_2,
    """google/realm-cc-news-pretrained-openqa""": 5_1_2,
    """google/realm-orqa-nq-openqa""": 5_1_2,
    """google/realm-orqa-nq-reader""": 5_1_2,
    """google/realm-orqa-wq-openqa""": 5_1_2,
    """google/realm-orqa-wq-reader""": 5_1_2,
}

snake_case__      :	Tuple       	=      {
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   VOCAB_FILES_NAMES
  A_						=   PRETRAINED_VOCAB_FILES_MAP
  A_						=   PRETRAINED_INIT_CONFIGURATION
  A_						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_						=   RealmTokenizer




  def __init__(     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=True		,		_UpperCAmelCase="[UNK]"		,		_UpperCAmelCase="[SEP]"		,		_UpperCAmelCase="[PAD]"		,		_UpperCAmelCase="[CLS]"		,		_UpperCAmelCase="[MASK]"		,		_UpperCAmelCase=True		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Optional[Any]:
     super().__init__(
         _UpperCAmelCase		,		tokenizer_file=_UpperCAmelCase		,		do_lower_case=_UpperCAmelCase		,		unk_token=_UpperCAmelCase		,		sep_token=_UpperCAmelCase		,		pad_token=_UpperCAmelCase		,		cls_token=_UpperCAmelCase		,		mask_token=_UpperCAmelCase		,		tokenize_chinese_chars=_UpperCAmelCase		,		strip_accents=_UpperCAmelCase		,		**_UpperCAmelCase		,		)

     UpperCamelCase_									=     json.loads(self.backend_tokenizer.normalizer.__getstate__() )
     if (
         normalizer_state.get('lowercase'		,		_UpperCAmelCase ) != do_lower_case
         or normalizer_state.get('strip_accents'		,		_UpperCAmelCase ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'		,		_UpperCAmelCase ) != tokenize_chinese_chars
     ):
        UpperCamelCase_									=     getattr(_UpperCAmelCase		,		normalizer_state.pop('type' ) )
        UpperCamelCase_									=     do_lower_case
        UpperCamelCase_									=     strip_accents
        UpperCamelCase_									=     tokenize_chinese_chars
        UpperCamelCase_									=     normalizer_class(**_UpperCAmelCase )

     UpperCamelCase_									=     do_lower_case




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     PaddingStrategy.MAX_LENGTH

     UpperCamelCase_									=     text
     UpperCamelCase_									=     kwargs.pop('text_pair'		,		_UpperCAmelCase )
     UpperCamelCase_									=     kwargs.pop('return_tensors'		,		_UpperCAmelCase )

     UpperCamelCase_									=     {
         'input_ids': [],
         'attention_mask': [],
         'token_type_ids': [],
     }

     for idx, candidate_text in enumerate(_UpperCAmelCase ):
        if batch_text_pair is not None:
           UpperCamelCase_									=     batch_text_pair[idx]
        else:
           UpperCamelCase_									=     None

        UpperCamelCase_									=     super().__call__(_UpperCAmelCase		,		_UpperCAmelCase		,		return_tensors=_UpperCAmelCase		,		**_UpperCAmelCase )

        UpperCamelCase_									=     encoded_candidates.get('input_ids' )
        UpperCamelCase_									=     encoded_candidates.get('attention_mask' )
        UpperCamelCase_									=     encoded_candidates.get('token_type_ids' )

        if encoded_input_ids is not None:
           output_data["input_ids"].append(_UpperCAmelCase )
        if encoded_attention_mask is not None:
           output_data["attention_mask"].append(_UpperCAmelCase )
        if encoded_token_type_ids is not None:
           output_data["token_type_ids"].append(_UpperCAmelCase )

     UpperCamelCase_									=     {key: item for key, item in output_data.items() if len(_UpperCAmelCase ) != 0}

     return BatchEncoding(_UpperCAmelCase		,		tensor_type=_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None )			-> Union[str, Any]:
     UpperCamelCase_									=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

     return output




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     UpperCamelCase_									=     [self.sep_token_id]
     UpperCamelCase_									=     [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    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> Tuple[str]:
     UpperCamelCase_									=     self._tokenizer.model.save(_UpperCAmelCase		,		name=_UpperCAmelCase )
     return tuple(_UpperCAmelCase )





