


'''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


_a   :   Dict												=   logging.get_logger(__name__)

_a   :   Dict												=   '''▁'''

_a   :   Optional[Any]												=   {'''vocab_file''': '''sentencepiece.bpe.model'''}

_a   :   Any												=   {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_a   :   Dict												=   {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_024,
}

# fmt: off
_a   :   Tuple												=   ['''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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']




class 			_lowercase (	_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE  :  List[Any]          =	VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE  :  str          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE  :  Tuple          =	["input_ids", "attention_mask"]

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

        __snake_case           =						{} if sp_model_kwargs is None else sp_model_kwargs

        __snake_case           =						kwargs.get('additional_special_tokens'       ,   []      )
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=UpperCamelCase__       ,   tgt_lang=UpperCamelCase__       ,   eos_token=UpperCamelCase__       ,   unk_token=UpperCamelCase__       ,   sep_token=UpperCamelCase__       ,   cls_token=UpperCamelCase__       ,   pad_token=UpperCamelCase__       ,   mask_token=UpperCamelCase__       ,   sp_model_kwargs=self.sp_model_kwargs       ,   **UpperCamelCase__       ,   )

        __snake_case           =						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.Load(str(UpperCamelCase__      )      )
        __snake_case           =						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
        __snake_case           =						{'<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
        __snake_case           =						1

        __snake_case           =						len(self.sp_model      )
        __snake_case           =						{
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase__      )
        }
        __snake_case           =						{v: k for k, v in self.lang_code_to_id.items()}
        __snake_case           =						len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
        __snake_case           =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}

        __snake_case           =						src_lang if src_lang is not None else 'en_XX'
        __snake_case           =						self.lang_code_to_id[self._src_lang]
        __snake_case           =						tgt_lang
        self.set_src_lang_special_tokens(self._src_lang      )
   @property
   def a  (						self       :      List[Any]      ) ->							int:
        return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token
   @property
   def a  (						self       :      int      ) ->							str:
        return self._src_lang
   @src_lang.setter
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							None:
        __snake_case           =						new_src_lang
        self.set_src_lang_special_tokens(self._src_lang      )
   def __getstate__(						self       :      Tuple      ) ->							Dict:
        __snake_case           =						self.__dict__.copy()
        __snake_case           =						None
        return state
   def __setstate__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Dict      ) ->							None:
        __snake_case           =						d

        # for backward compatibility
        if not hasattr(self       ,   'sp_model_kwargs'      ):
             __snake_case           =						{}

        __snake_case           =						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.Load(self.vocab_file      )
   def a  (						self       :      Union[str, Any]      ) ->							Dict:
        __snake_case           =						{self.convert_ids_to_tokens(UpperCamelCase__      ): i for i in range(self.vocab_size      )}
        vocab.update(self.added_tokens_encoder      )
        return vocab
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							List[str]:
        return self.sp_model.encode(UpperCamelCase__       ,   out_type=UpperCamelCase__      )
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							int:
        if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
        __snake_case           =						self.sp_model.PieceToId(UpperCamelCase__      )

        # 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 a  (						self       :      Optional[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 a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]      ) ->							Optional[Any]:
        __snake_case           =						[]
        __snake_case           =						''
        __snake_case           =						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(UpperCamelCase__      ) + token
                  __snake_case           =						True
                  __snake_case           =						[]
             else:
                  current_sub_tokens.append(UpperCamelCase__      )
                  __snake_case           =						False
        out_string += self.sp_model.decode(UpperCamelCase__      )
        return out_string.strip()
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      Optional[str] = None      ) ->							Tuple[str]:
        if not os.path.isdir(UpperCamelCase__      ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
             return
        __snake_case           =						os.path.join(
            UpperCamelCase__       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

        if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase__      ) and os.path.isfile(self.vocab_file      ):
             copyfile(self.vocab_file       ,   UpperCamelCase__      )
        elif not os.path.isfile(self.vocab_file      ):
             with open(UpperCamelCase__       ,   'wb'      ) as fi:
                  __snake_case           =						self.sp_model.serialized_model_proto()
                  fi.write(UpperCamelCase__      )

        return (out_vocab_file,)
   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[List[int]] = None       ,   SCREAMING_SNAKE_CASE_       :      bool = False      ) ->							List[int]:

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

        __snake_case           =						[1] * len(self.prefix_tokens      )
        __snake_case           =						[1] * len(self.suffix_tokens      )
        if token_ids_a is None:
             return prefix_ones + ([0] * len(UpperCamelCase__      )) + suffix_ones
        return prefix_ones + ([0] * len(UpperCamelCase__      )) + ([0] * len(UpperCamelCase__      )) + suffix_ones
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      List[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[List[int]] = None      ) ->							List[int]:
        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 a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      Optional[str]       ,   SCREAMING_SNAKE_CASE_       :      Optional[str]       ,   **SCREAMING_SNAKE_CASE_       :      Any      ) ->							str:
        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'      )
        __snake_case           =						src_lang
        __snake_case           =						self(UpperCamelCase__       ,   add_special_tokens=UpperCamelCase__       ,   return_tensors=UpperCamelCase__       ,   **UpperCamelCase__      )
        __snake_case           =						self.convert_tokens_to_ids(UpperCamelCase__      )
        __snake_case           =						tgt_lang_id
        return inputs
   def a  (						self       :      str       ,   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_       :      Dict       ,   ) ->							BatchEncoding:
        __snake_case           =						src_lang
        __snake_case           =						tgt_lang
        return super().prepare_seqaseq_batch(UpperCamelCase__       ,   UpperCamelCase__       ,   **UpperCamelCase__      )
   def a  (						self       :      Any      ) ->							int:
        return self.set_src_lang_special_tokens(self.src_lang      )
   def a  (						self       :      Any      ) ->							Any:
        return self.set_tgt_lang_special_tokens(self.tgt_lang      )
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							None:
        __snake_case           =						self.lang_code_to_id[src_lang]
        __snake_case           =						[self.cur_lang_code_id]
        __snake_case           =						[self.eos_token_id]





   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							None:
        __snake_case           =						self.lang_code_to_id[tgt_lang]
        __snake_case           =						[self.cur_lang_code_id]
        __snake_case           =						[self.eos_token_id]





'''simple docstring'''






import math





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  []
     _A    		=  2
     _A    		=  int(math.sqrt(__snake_case				)				)  # Size of every segment
     _A    		=  [True] * (end + 1)
     _A    		=  []

     while start <= end:
          if temp[start] is True:
               in_prime.append(__snake_case				)
               for i in range(start * start   ,		end + 1   ,		__snake_case				):
                    _A    		=  False
          start += 1
     prime += in_prime

     _A    		=  end + 1
     _A    		=  min(2 * end   ,		__snake_case				)

     while low <= n:
          _A    		=  [True] * (high - low + 1)
          for each in in_prime:
               _A    		=  math.floor(low / each				) * each
               if t < low:
                    t += each

               for j in range(__snake_case   ,		high + 1   ,		__snake_case				):
                    _A    		=  False

          for j in range(len(__snake_case				)				):
               if temp[j] is True:
                    prime.append(j + low				)

          _A    		=  high + 1
          _A    		=  min(high + end   ,		__snake_case				)

     return prime
print(sieve(10**6))



