





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

from tokenizers import processors

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


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    __lowerCamelCase :  Optional[int]										=  None


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


__lowerCamelCase :  List[Any]										=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase :  Tuple										=  {
    """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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase :  Optional[int]										=  {
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
__lowerCamelCase :  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       A__     ( __snake_case    ):
      _UpperCAmelCase			:Tuple							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:Optional[int]							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Dict							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:Any							=     ['input_ids', 'attention_mask']
      _UpperCAmelCase			:str							=     MBartTokenizer

      _UpperCAmelCase			:List[int]							=     []
      _UpperCAmelCase			:List[int]							=     []





      def __init__(				self ,							A_=None ,							A_=None ,							A_="<s>" ,							A_="</s>" ,							A_="</s>" ,							A_="<s>" ,							A_="<unk>" ,							A_="<pad>" ,							A_="<mask>" ,							A_=None ,							A_=None ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     ) if isinstance(A_ ,							A_     ) else mask_token

       super().__init__(
           vocab_file=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_ ,							src_lang=A_ ,							tgt_lang=A_ ,							additional_special_tokens=A_ ,							**A_ ,							)

       UpperCamelCase			:      int 			=     vocab_file
       UpperCamelCase			:      int 			=     False if not self.vocab_file else True

       UpperCamelCase			:      Optional[int] 			=     FAIRSEQ_LANGUAGE_CODES.copy()

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

       self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}     )
       UpperCamelCase			:      Optional[int] 			=     {
           lang_code: self.convert_tokens_to_ids(A_     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       UpperCamelCase			:      Optional[Any] 			=     src_lang if src_lang is not None else "en_XX"
       UpperCamelCase			:      List[str] 			=     self.convert_tokens_to_ids(self._src_lang     )
       UpperCamelCase			:      Optional[int] 			=     tgt_lang
       self.set_src_lang_special_tokens(self._src_lang     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self._src_lang





      @src_lang.setter
      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     new_src_lang
       self.set_src_lang_special_tokens(self._src_lang     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       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  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      int 			=     [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 ,							A_ ,							A_ ,							A_ ,							A_ ,							**A_     ):
       '''simple docstring'''




       if src_lang is None or tgt_lang is None:
        raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"     )
       UpperCamelCase			:      Tuple 			=     src_lang
       UpperCamelCase			:      Union[str, Any] 			=     self(A_ ,							add_special_tokens=A_ ,							return_tensors=A_ ,							**A_     )
       UpperCamelCase			:      int 			=     self.convert_tokens_to_ids(A_     )
       UpperCamelCase			:      Tuple 			=     tgt_lang_id
       return inputs





      def  __UpperCamelCase(				self ,							A_ ,							A_ = "en_XX" ,							A_ = None ,							A_ = "ro_RO" ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     src_lang
       UpperCamelCase			:      Optional[int] 			=     tgt_lang
       return super().prepare_seqaseq_batch(A_ ,							A_ ,							**A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.set_src_lang_special_tokens(self.src_lang     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.set_tgt_lang_special_tokens(self.tgt_lang     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.convert_tokens_to_ids(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     []
       UpperCamelCase			:      List[str] 			=     [self.eos_token_id, self.cur_lang_code]

       UpperCamelCase			:      Dict 			=     self.convert_ids_to_tokens(self.prefix_tokens     )
       UpperCamelCase			:      Tuple 			=     self.convert_ids_to_tokens(self.suffix_tokens     )

       UpperCamelCase			:      Dict 			=     processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,							pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,							self.prefix_tokens + self.suffix_tokens     )     ) ,							)





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.convert_tokens_to_ids(A_     )
       UpperCamelCase			:      List[Any] 			=     []
       UpperCamelCase			:      Tuple 			=     [self.eos_token_id, self.cur_lang_code]

       UpperCamelCase			:      Dict 			=     self.convert_ids_to_tokens(self.prefix_tokens     )
       UpperCamelCase			:      List[Any] 			=     self.convert_ids_to_tokens(self.suffix_tokens     )

       UpperCamelCase			:      Any 			=     processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,							pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,							self.prefix_tokens + self.suffix_tokens     )     ) ,							)





      def  __UpperCamelCase(				self ,							A_ ,							A_ = 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
       UpperCamelCase			:      Optional[Any] 			=     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,)







from __future__ import annotations



def 	A_       (  _lowerCAmelCase  )		->				list[int]:
 UpperCamelCase			:      Optional[Any] 			=     [True] * limit
 UpperCamelCase			:      Optional[Any] 			=     False
 UpperCamelCase			:      List[str] 			=     False
 UpperCamelCase			:      Tuple 			=     True

 for i in range(3							,     int(limit**0.5 + 1  )							,     2  ):
  UpperCamelCase			:      Optional[Any] 			=     i * 2
  while index < limit:
   UpperCamelCase			:      int 			=     False
   UpperCamelCase			:      Optional[int] 			=     index + i

 UpperCamelCase			:      Any 			=     [2]

 for i in range(3							,     _lowerCAmelCase							,     2  ):
  if is_prime[i]:
   primes.append(_lowerCAmelCase  )

 return primes



def 	A_       (  _lowerCAmelCase = 100_0000  )		->				int:
 UpperCamelCase			:      Union[str, Any] 			=     prime_sieve(_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     0

 for i in range(len(_lowerCAmelCase  )  ):
  for j in range(i + length							,     len(_lowerCAmelCase  )  ):
   UpperCamelCase			:      Dict 			=     sum(primes[i:j]  )
   if sol >= ceiling:
    break

   if sol in primes:
    UpperCamelCase			:      int 			=     j - i
    UpperCamelCase			:      Dict 			=     sol

 return largest


if __name__ == "__main__":
    print(f"""{solution() = }""")
