


"""simple docstring"""




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

import sentencepiece as spm

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


UpperCAmelCase_				:			Any   =	logging.get_logger(__name__)

UpperCAmelCase_				:			Tuple   =	"▁"

UpperCAmelCase_				:			List[str]   =	{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase_				:			int   =	{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase_				:			Tuple   =	{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
UpperCAmelCase_				:			Optional[Any]   =	["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class 			SCREAMING_SNAKE_CASE__  (    lowerCamelCase__      ):
      snake_case__    :					List[Any]					       =						VOCAB_FILES_NAMES
      snake_case__    :					Optional[Any]					       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :					int					       =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :					Optional[Any]					       =						['''input_ids''', '''attention_mask''']

      snake_case__    :					Dict					       =						[]
      snake_case__    :					List[Any]					       =						[]
      def __init__(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]="<s>"					,							SCREAMING_SNAKE_CASE__    :	str="</s>"					,							SCREAMING_SNAKE_CASE__    :	List[str]="</s>"					,							SCREAMING_SNAKE_CASE__    :	int="<s>"					,							SCREAMING_SNAKE_CASE__    :	Tuple="<unk>"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="<pad>"					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="<mask>"					,							SCREAMING_SNAKE_CASE__    :	Any=None					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=None					,							SCREAMING_SNAKE_CASE__    :	Any=None					,							SCREAMING_SNAKE_CASE__    :	Optional[Dict[str, Any]] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=None					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					,							)		->      int:
             # Mask token behave like a normal word, i.e. include the space before it
             a_ :		Union[str, Any]							=  AddedToken(__A					,							lstrip=__A					,							rstrip=__A					) if isinstance(__A					,							__A					) else mask_token

             a_ :		str							=  {} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=__A					,							eos_token=__A					,							unk_token=__A					,							sep_token=__A					,							cls_token=__A					,							pad_token=__A					,							mask_token=__A					,							tokenizer_file=__A					,							src_lang=__A					,							tgt_lang=__A					,							additional_special_tokens=__A					,							sp_model_kwargs=self.sp_model_kwargs					,							**__A					,							)

             a_ :		int							=  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.Load(str(__A					)					)
             a_ :		List[str]							=  vocab_file

             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
             # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # Mimic fairseq token-to-id alignment for the first 4 token
             a_ :		Optional[int]							=  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             a_ :		List[str]							=  1

             a_ :		Optional[int]							=  len(self.sp_model					)
             a_ :		int							=  {
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A					)
             }
             a_ :		str							=  {v: k for k, v in self.lang_code_to_id.items()}
             a_ :		List[str]							=  len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
             a_ :		List[Any]							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
             a_ :		Optional[int]							=  list(self.lang_code_to_id.keys()					)

             if additional_special_tokens is not None:
                    # Only add those special tokens if they are not already there.
                    self._additional_special_tokens.extend(
                        [t for t in additional_special_tokens if t not in self._additional_special_tokens]					)

             a_ :		Any							=  src_lang if src_lang is not None else '''en_XX'''
             a_ :		Any							=  self.lang_code_to_id[self._src_lang]
             a_ :		Any							=  tgt_lang
             self.set_src_lang_special_tokens(self._src_lang					)
      def __getstate__(	self    :	Union[str, Any]					)		->      Dict:
             a_ :		Dict							=  self.__dict__.copy()
             a_ :		Union[str, Any]							=  None
             a_ :		List[Any]							=  self.sp_model.serialized_model_proto()
             return state
      def __setstate__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					)		->      Union[str, Any]:
             a_ :		Optional[Any]							=  d

             # for backward compatibility
             if not hasattr(self					,							'sp_model_kwargs'					):
                    a_ :		Optional[int]							=  {}

             a_ :		Any							=  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Optional[int]:
             return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Union[str, Any]:
             return self._src_lang
      @src_lang.setter
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  new_src_lang
             self.set_src_lang_special_tokens(self._src_lang					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[List[int]] = None					,							SCREAMING_SNAKE_CASE__    :	bool = False					)		->      Dict:

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

             a_ :		List[str]							=  [1] * len(self.prefix_tokens					)
             a_ :		List[Any]							=  [1] * len(self.suffix_tokens					)
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(__A					)) + suffix_ones
             return prefix_ones + ([0] * len(__A					)) + ([0] * len(__A					)) + suffix_ones
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[List[int]] = None					)		->      Any:
             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a + self.suffix_tokens
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	List[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[List[int]] = None					)		->      List[str]:
             a_ :		Any							=  [self.sep_token_id]
             a_ :		List[Any]							=  [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 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[str]					,							SCREAMING_SNAKE_CASE__    :	Optional[str]					,							**SCREAMING_SNAKE_CASE__    :	Tuple					)		->      Any:
             if src_lang is None or tgt_lang is None:
                    raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
             a_ :		List[str]							=  src_lang
             a_ :		Tuple							=  self(__A					,							add_special_tokens=__A					,							return_tensors=__A					,							**__A					)
             a_ :		Dict							=  self.convert_tokens_to_ids(__A					)
             a_ :		Optional[Any]							=  tgt_lang_id
             return inputs
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
             a_ :		Any							=  {self.convert_ids_to_tokens(__A					): i for i in range(self.vocab_size					)}
             vocab.update(self.added_tokens_encoder					)
             return vocab
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      Optional[Any]:
             return self.sp_model.encode(__A					,							out_type=__A					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple					)		->      Any:
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             a_ :		int							=  self.sp_model.PieceToId(__A					)

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	int					)		->      str:
             if index in self.fairseq_ids_to_tokens:
                    return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	int					)		->      List[str]:
             a_ :		Tuple							=  ''''''.join(__A					).replace(__A					,							' '					).strip()
             return out_string
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[str] = None					)		->      int:
             if not os.path.isdir(__A					):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
                    return
             a_ :		List[str]							=  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					) and os.path.isfile(self.vocab_file					):
                    copyfile(self.vocab_file					,							__A					)
             elif not os.path.isfile(self.vocab_file					):
                    with open(__A					,							'wb'					) as fi:
                           a_ :		Dict							=  self.sp_model.serialized_model_proto()
                           fi.write(__A					)

             return (out_vocab_file,)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	str = "en_XX"					,							SCREAMING_SNAKE_CASE__    :	Optional[List[str]] = None					,							SCREAMING_SNAKE_CASE__    :	str = "ro_RO"					,							**SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							)		->      str:
             a_ :		Union[str, Any]							=  src_lang
             a_ :		Optional[Any]							=  tgt_lang
             return super().prepare_seqaseq_batch(__A					,							__A					,							**__A					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             return self.set_src_lang_special_tokens(self.src_lang					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Union[str, Any]:
             return self.set_tgt_lang_special_tokens(self.tgt_lang					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Any					)		->      Union[str, Any]:
             a_ :		Dict							=  self.lang_code_to_id[src_lang]
             a_ :		int							=  []
             a_ :		str							=  [self.eos_token_id, self.cur_lang_code]

      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	str					)		->      Optional[int]:
             a_ :		str							=  self.lang_code_to_id[lang]
             a_ :		int							=  []
             a_ :		Any							=  [self.eos_token_id, self.cur_lang_code]






from string import ascii_uppercase

UpperCAmelCase_				:			Dict   =	{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_				:			Optional[int]   =	dict(enumerate(ascii_uppercase))







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							=  len(__A    )
       a_ :		int							=  0
       while True:
              if x == i:
                     a_ :		Tuple							=  0
              if len(__A    ) == len(__A    ):
                     break
              key += key[i]
              i += 1
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Optional[int]							=  ''
       a_ :		Any							=  0
       for letter in message:
              if letter == " ":
                     cipher_text += " "
              else:
                     a_ :		Optional[Any]							=  (dicta[letter] - dicta[key_new[i]]) % 26
                     i += 1
                     cipher_text += dicta[x]
       return cipher_text







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  ''
       a_ :		Optional[Any]							=  0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     a_ :		Union[str, Any]							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Tuple							=  'THE GERMAN ATTACK'
       a_ :		Dict							=  'SECRET'
       a_ :		Optional[Any]							=  generate_key(__A  ,   __A    )
       a_ :		Union[str, Any]							=  cipher_text(__A  ,   __A    )
       print(F"""Encrypted Text = {s}"""    )
       print(F"""Original Text = {original_text(__A  ,   __A    )}"""    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


