import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase													=		128
UpperCamelCase													=		256

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: int = DEFAULT_BLOCK_SIZE):
    lowercase__				:     List[str]					      =       message.encode("ascii")

    lowercase__				:     Any					      =       []
    for block_start in range(0	,			len(_lowerCamelCase)	,			_lowerCamelCase):
        lowercase__				:     Dict					      =       0
        for i in range(_lowerCamelCase	,			min(block_start + block_size	,			len(_lowerCamelCase))):
            block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
        block_ints.append(_lowerCamelCase)
    return block_ints

def     lowercase_       (    _lowerCamelCase							: list[int]	,			_lowerCamelCase							: int	,			_lowerCamelCase							: int = DEFAULT_BLOCK_SIZE):
    lowercase__				:     list[str]					      =       []
    for block_int in block_ints:
        lowercase__				:     list[str]					      =       []
        for i in range(block_size - 1	,			-1	,			-1):
            if len(_lowerCamelCase) + i < message_length:
                lowercase__				:     Tuple					      =       block_int // (BYTE_SIZE**i)
                lowercase__				:     Optional[Any]					      =       block_int % (BYTE_SIZE**i)
                block_message.insert(0	,			chr(_lowerCamelCase))
        message.extend(_lowerCamelCase)
    return "".join(_lowerCamelCase)

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: tuple[int, int]	,			_lowerCamelCase							: int = DEFAULT_BLOCK_SIZE):
    lowercase__				:     List[str]					      =       []
    lowercase__      ,     lowercase__				:     Any					      =       key

    for block in get_blocks_from_text(_lowerCamelCase	,			_lowerCamelCase):
        encrypted_blocks.append(pow(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase))
    return encrypted_blocks

def     lowercase_       (    _lowerCamelCase							: list[int]	,			_lowerCamelCase							: int	,			_lowerCamelCase							: tuple[int, int]	,			_lowerCamelCase							: int = DEFAULT_BLOCK_SIZE	,			):
    lowercase__				:     Any					      =       []
    lowercase__      ,     lowercase__				:     str					      =       key
    for block in encrypted_blocks:
        decrypted_blocks.append(pow(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase))
    return get_text_from_blocks(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)

def     lowercase_       (    _lowerCamelCase							: str):
    with open(_lowerCamelCase) as fo:
        lowercase__				:     Optional[Any]					      =       fo.read()
    lowercase__      ,     lowercase__      ,     lowercase__				:     str					      =       content.split(",")
    return (int(_lowerCamelCase), int(_lowerCamelCase), int(_lowerCamelCase))

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: str	,			_lowerCamelCase							: str	,			_lowerCamelCase							: int = DEFAULT_BLOCK_SIZE	,			):
    lowercase__      ,     lowercase__      ,     lowercase__				:     int					      =       read_key_file(_lowerCamelCase)
    if key_size < block_size * 8:
        sys.exit(
            "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
            "requires the block size to be equal to or greater than the key size. "
            "Either decrease the block size or use different keys.".format(
                block_size * 8	,			_lowerCamelCase))

    lowercase__				:     List[str]					      =       [str(_lowerCamelCase) for i in encrypt_message(_lowerCamelCase	,			(n, e)	,			_lowerCamelCase)]

    lowercase__				:     str					      =       ",".join(_lowerCamelCase)
    lowercase__				:     Optional[Any]					      =       f'''{len(_lowerCamelCase)}_{block_size}_{encrypted_content}'''
    with open(_lowerCamelCase	,			"w") as fo:
        fo.write(_lowerCamelCase)
    return encrypted_content

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: str):
    lowercase__      ,     lowercase__      ,     lowercase__				:     Tuple					      =       read_key_file(_lowerCamelCase)
    with open(_lowerCamelCase) as fo:
        lowercase__				:     Tuple					      =       fo.read()
    lowercase__      ,     lowercase__      ,     lowercase__				:     Optional[Any]					      =       content.split("_")
    lowercase__				:     List[Any]					      =       int(_lowerCamelCase)
    lowercase__				:     Optional[Any]					      =       int(_lowerCamelCase)

    if key_size < block_size * 8:
        sys.exit(
            "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
            "requires the block size to be equal to or greater than the key size. "
            "Did you specify the correct key file and encrypted file?".format(
                block_size * 8	,			_lowerCamelCase))

    lowercase__				:     Optional[int]					      =       []
    for block in encrypted_message.split(","):
        encrypted_blocks.append(int(_lowerCamelCase))

    return decrypt_message(_lowerCamelCase	,			_lowerCamelCase	,			(n, d)	,			_lowerCamelCase)

def     lowercase_       (    ):
    lowercase__				:     Optional[Any]					      =       "encrypted_file.txt"
    lowercase__				:     int					      =       input(R"Encrypt\Decrypt [e\d]: ")

    if response.lower().startswith("e"):
        lowercase__				:     str					      =       "encrypt"
    elif response.lower().startswith("d"):
        lowercase__				:     List[Any]					      =       "decrypt"

    if mode == "encrypt":
        if not os.path.exists("rsa_pubkey.txt"):
            rkg.make_key_files("rsa"	,			1024)

        lowercase__				:     Tuple					      =       input("\nEnter message: ")
        lowercase__				:     Any					      =       "rsa_pubkey.txt"
        print(f'''Encrypting and writing to {filename}...''')
        lowercase__				:     Any					      =       encrypt_and_write_to_file(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)

        print("\nEncrypted text:")
        print(_lowerCamelCase)

    elif mode == "decrypt":
        lowercase__				:     Tuple					      =       "rsa_privkey.txt"
        print(f'''Reading from {filename} and decrypting...''')
        lowercase__				:     List[Any]					      =       read_from_file_and_decrypt(_lowerCamelCase	,			_lowerCamelCase)
        print("writing decryption to rsa_decryption.txt...")
        with open("rsa_decryption.txt"	,			"w") as dec:
            dec.write(_lowerCamelCase)

        print("\nDecryption:")
        print(_lowerCamelCase)


if __name__ == "__main__":
      main()




import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class 						snake_case_ (	tf.keras.layers.Layer ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  bool			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  str = "longest"			,		lowercase_       :  bool = True			,		lowercase_       :  int = 5_12			,		lowercase_       :  int = None			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		**lowercase_       :  Tuple			,		)      ->		Any:
       super().__init__()
       if use_fast_bert_tokenizer:
           lowercase__				:     Optional[Any]					      =       FastBertTokenizer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case_nfd_strip_accents=lowercase_			,		**lowercase_						)
       else:
           lowercase__				:     List[str]					      =       tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=lowercase_			,		key_dtype=tf.string			,		values=tf.range(tf.size(lowercase_			,		out_type=tf.intaa						)			,		dtype=tf.intaa						)			,		value_dtype=tf.intaa			,		)			,		num_oov_buckets=1			,		)
           lowercase__				:     Optional[Any]					      =       BertTokenizerLayer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case=lowercase_			,		**lowercase_						)

       lowercase__				:     Optional[int]					      =       vocab_list
       lowercase__				:     List[Any]					      =       do_lower_case
       lowercase__				:     Dict					      =       cls_token_id or vocab_list.index("[CLS]"						)
       lowercase__				:     Any					      =       sep_token_id or vocab_list.index("[SEP]"						)
       lowercase__				:     Union[str, Any]					      =       pad_token_id or vocab_list.index("[PAD]"						)
       lowercase__				:     List[str]					      =       ShrinkLongestTrimmer(max_length - 3			,		axis=1						)  # Allow room for special tokens
       lowercase__				:     Dict					      =       max_length
       lowercase__				:     List[str]					      =       padding
       lowercase__				:     Tuple					      =       truncation
       lowercase__				:     Tuple					      =       pad_to_multiple_of
       lowercase__				:     Optional[Any]					      =       return_token_type_ids
       lowercase__				:     Dict					      =       return_attention_mask






   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  "PreTrainedTokenizerBase"			,		**lowercase_       :  Optional[Any]						)      ->		int:  # noqa: F821
       lowercase__				:     Optional[Any]					      =       kwargs.pop("do_lower_case"			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("cls_token_id"			,		lowercase_						)
       lowercase__				:     List[Any]					      =       tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       lowercase__				:     Dict					      =       kwargs.pop("sep_token_id"			,		lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       lowercase__				:     Any					      =       kwargs.pop("pad_token_id"			,		lowercase_						)
       lowercase__				:     Tuple					      =       tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       lowercase__				:     Union[str, Any]					      =       tokenizer.get_vocab()
       lowercase__				:     Tuple					      =       sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,		key=lambda lowercase_						: x[1]						)
       lowercase__				:     Tuple					      =       [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowercase_			,		do_lower_case=lowercase_			,		cls_token_id=lowercase_			,		sep_token_id=lowercase_			,		pad_token_id=lowercase_			,		**lowercase_			,		)






   @classmethod
   def    __UpperCamelCase  (   cls       :  Any			,		lowercase_       :  Union[str, os.PathLike]			,		*lowercase_       :  Optional[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[int]:
       try:
           lowercase__				:     str					      =       BertTokenizer.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           lowercase__				:     int					      =       BertTokenizerFast.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       return cls.from_tokenizer(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  List[Any]						)      ->		Any:
       if self.do_lower_case:
           lowercase__				:     Optional[int]					      =       case_fold_utfa(lowercase_						)
       lowercase__				:     Optional[Any]					      =       self.tf_tokenizer.tokenize(lowercase_						)
       return tokens.merge_dims(1			,		-1						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]			,		lowercase_       :  Dict=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Tuple=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[Any]=None			,		lowercase_       :  List[Any]=None			,		)      ->		int:
       if padding is None:
           lowercase__				:     str					      =       self.padding
       if padding not in ("longest", "max_length"):
           raise ValueError("Padding must be either 'longest' or 'max_length'!"						)
       if max_length is not None and text_pair is not None:
           # Because we have to instantiate a Trimmer to do it properly
           raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"						)
       if max_length is None:
           lowercase__				:     int					      =       self.max_length
       if truncation is None:
           lowercase__				:     int					      =       self.truncation
       if pad_to_multiple_of is None:
           lowercase__				:     Union[str, Any]					      =       self.pad_to_multiple_of
       if return_token_type_ids is None:
           lowercase__				:     Dict					      =       self.return_token_type_ids
       if return_attention_mask is None:
           lowercase__				:     Dict					      =       self.return_attention_mask
       if not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Union[str, Any]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None and not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Optional[int]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None:
           if text.shape.rank > 1:
               raise ValueError("text argument should not be multidimensional when a text pair is supplied!"						)
           if text_pair.shape.rank > 1:
               raise ValueError("text_pair should not be multidimensional!"						)
       if text.shape.rank == 2:
           lowercase__      ,     lowercase__				:     List[Any]					      =       text[:, 0], text[:, 1]
       lowercase__				:     List[Any]					      =       self.unpaired_tokenize(lowercase_						)
       if text_pair is None:  # Unpaired text
           if truncation:
               lowercase__				:     Any					      =       text[:, : max_length - 2]  # Allow room for special tokens
           lowercase__      ,     lowercase__				:     Dict					      =       combine_segments(
               (text,)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
           lowercase__				:     Any					      =       self.unpaired_tokenize(lowercase_						)
           if truncation:
               lowercase__      ,     lowercase__				:     List[Any]					      =       self.paired_trimmer.trim([text, text_pair]						)
           lowercase__      ,     lowercase__				:     str					      =       combine_segments(
               (text, text_pair)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
           lowercase__				:     List[str]					      =       input_ids.bounding_shape(axis=1						)
           if pad_to_multiple_of is not None:
               # No ceiling division in tensorflow, so we negate floordiv instead
               lowercase__				:     List[Any]					      =       pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,		lowercase_						))
       else:
           lowercase__				:     Optional[Any]					      =       max_length

       lowercase__      ,     lowercase__				:     str					      =       pad_model_inputs(lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
       lowercase__				:     str					      =       {"input_ids": input_ids}
       if return_attention_mask:
           lowercase__				:     List[Any]					      =       attention_mask
       if return_token_type_ids:
           lowercase__      ,     lowercase__				:     List[str]					      =       pad_model_inputs(
               lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
           lowercase__				:     List[Any]					      =       token_type_ids
       return output







   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       return {
           "vocab_list": self.vocab_list,
           "do_lower_case": self.do_lower_case,
           "cls_token_id": self.cls_token_id,
           "sep_token_id": self.sep_token_id,
           "pad_token_id": self.pad_token_id,
       }



