


'''simple docstring'''


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

import sentencepiece as spm

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


_UpperCamelCase          =     logging.get_logger(__name__)
_UpperCamelCase          =     {"""vocab_file""": """spiece.model"""}

_UpperCamelCase          =     {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

_UpperCamelCase          =     {
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

_UpperCamelCase          =     """▁"""







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							VOCAB_FILES_NAMES
     lowerCamelCase__						=							PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__						=							['input_ids', 'token_type_ids']


     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase=False ,    lowerCamelCase=True ,    lowerCamelCase=True ,    lowerCamelCase="<unk>" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase="<pad>" ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[MASK]" ,    lowerCamelCase = None ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					Any						     =       (
              AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase ,    normalized=lowerCamelCase		)
              if isinstance(lowerCamelCase ,    lowerCamelCase		)
              else mask_token
          )

          __A  :					int						     =       {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              do_lower_case=lowerCamelCase ,    remove_space=lowerCamelCase ,    keep_accents=lowerCamelCase ,    unk_token=lowerCamelCase ,    sep_token=lowerCamelCase ,    pad_token=lowerCamelCase ,    cls_token=lowerCamelCase ,    mask_token=lowerCamelCase ,    sp_model_kwargs=self.sp_model_kwargs ,    **lowerCamelCase ,    )

          __A  :					int						     =       do_lower_case
          __A  :					int						     =       remove_space
          __A  :					Dict						     =       keep_accents
          __A  :					Dict						     =       vocab_file

          __A  :					Dict						     =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(lowerCamelCase		)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return len(self.sp_model		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       {self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab


     def __getstate__(      self		):


          '''simple docstring'''





          __A  :					List[str]						     =       self.__dict__.copy()
          __A  :					Dict						     =       None
          return state


     def __setstate__(      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       d

          # for backward compatibility
          if not hasattr(self ,    "sp_model_kwargs"		):
               __A  :					Any						     =       {}

          __A  :					Any						     =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(self.vocab_file		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if self.remove_space:
               __A  :					List[str]						     =       " ".join(inputs.strip().split()		)
          else:
               __A  :					Any						     =       inputs
          __A  :					Union[str, Any]						     =       outputs.replace("``" ,    "\""		).replace("''" ,    "\""		)

          if not self.keep_accents:
               __A  :					Dict						     =       unicodedata.normalize("NFKD" ,    lowerCamelCase		)
               __A  :					Union[str, Any]						     =       "".join([c for c in outputs if not unicodedata.combining(lowerCamelCase		)]		)
          if self.do_lower_case:
               __A  :					List[str]						     =       outputs.lower()

          return outputs


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       self.preprocess_text(lowerCamelCase		)
          __A  :					Optional[int]						     =       self.sp_model.encode(lowerCamelCase ,    out_type=lowerCamelCase		)
          __A  :					int						     =       []
          for piece in pieces:
               if len(lowerCamelCase		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
                    __A  :					Tuple						     =       self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase ,    ""		)		)
                    if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0]		) == 1:
                              __A  :					str						     =       cur_pieces[1:]
                         else:
                              __A  :					Optional[int]						     =       cur_pieces[0][1:]
                    cur_pieces.append(piece[-1]		)
                    new_pieces.extend(lowerCamelCase		)
               else:
                    new_pieces.append(lowerCamelCase		)

          return new_pieces


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.sp_model.PieceToId(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.sp_model.IdToPiece(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       []
          __A  :					Dict						     =       ""
          __A  :					Optional[Any]						     =       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(lowerCamelCase		) + token
                    __A  :					Any						     =       True
                    __A  :					int						     =       []
               else:
                    current_sub_tokens.append(lowerCamelCase		)
                    __A  :					List[str]						     =       False
          out_string += self.sp_model.decode(lowerCamelCase		)
          return out_string.strip()


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = False ,    lowerCamelCase = None ,    lowerCamelCase = True ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       kwargs.pop("use_source_tokenizer" ,    lowerCamelCase		)

          __A  :					Dict						     =       self.convert_ids_to_tokens(lowerCamelCase ,    skip_special_tokens=lowerCamelCase		)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          __A  :					Optional[int]						     =       []
          __A  :					Tuple						     =       []
          for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                    continue
               if token in self.added_tokens_encoder:
                    if current_sub_text:
                         sub_texts.append(self.convert_tokens_to_string(lowerCamelCase		)		)
                         __A  :					Any						     =       []
                    sub_texts.append(lowerCamelCase		)
               else:
                    current_sub_text.append(lowerCamelCase		)
          if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(lowerCamelCase		)		)

          # Mimic the behavior of the Rust tokenizer:
          # No space after <unk>
          if spaces_between_special_tokens:
               __A  :					Optional[int]						     =       re.sub(r"(<unk>) " ,    r"\1" ,    " ".join(lowerCamelCase		)		)
          else:
               __A  :					str						     =       "".join(lowerCamelCase		)

          __A  :					int						     =       (
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
               __A  :					List[Any]						     =       self.clean_up_tokenization(lowerCamelCase		)
               return clean_text
          else:
               return text


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       [self.sep_token_id]
          __A  :					Any						     =       [self.cls_token_id]
          if token_ids_a is None:
               return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = False		):


          '''simple docstring'''





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

          if token_ids_a is not None:
               return [1] + ([0] * len(lowerCamelCase		)) + [1] + ([0] * len(lowerCamelCase		)) + [1]
          return [1] + ([0] * len(lowerCamelCase		)) + [1]


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       [self.sep_token_id]
          __A  :					Tuple						     =       [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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A  :					Tuple						     =       os.path.join(
              lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

          if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		) and os.path.isfile(self.vocab_file		):
               copyfile(self.vocab_file ,    lowerCamelCase		)
          elif not os.path.isfile(self.vocab_file		):
               with open(lowerCamelCase ,    "wb"		) as fi:
                    __A  :					List[Any]						     =       self.sp_model.serialized_model_proto()
                    fi.write(lowerCamelCase		)

          return (out_vocab_file,)





'''simple docstring'''


import math
from collections.abc import Generator


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Generator						     =       (i for i in range(1					,				(max_n + 1)       ))
     for i in (n for n in numbers if n > 1):
          for j in range(2					,				SCREAMING_SNAKE_CASE       ):
               if (i % j) == 0:
                    break
          else:
               yield i


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Generator						     =       (i for i in range(1					,				(max_n + 1)       ))
     for i in (n for n in numbers if n > 1):
          # only need to check for factors up to sqrt(i)
          __A  :					Optional[Any]						     =       int(math.sqrt(SCREAMING_SNAKE_CASE       )       ) + 1
          for j in range(2					,				SCREAMING_SNAKE_CASE       ):
               if (i % j) == 0:
                    break
          else:
               yield i


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Generator						     =       (i for i in range(1					,				(max_n + 1)					,				2       ))
     # It's useless to test even numbers as they will not be prime
     if max_n > 2:
          yield 2  # Because 2 will not be tested, it's necessary to yield it now
     for i in (n for n in numbers if n > 1):
          __A  :					List[str]						     =       int(math.sqrt(SCREAMING_SNAKE_CASE       )       ) + 1
          for j in range(3					,				SCREAMING_SNAKE_CASE					,				2       ):
               # As we removed the even numbers, we don't need them now
               if (i % j) == 0:
                    break
          else:
               yield i


def 		_lowercase   ():




     '''simple docstring'''





     from timeit import timeit

     __A  :					List[str]						     =       "from __main__ import slow_primes, primes, fast_primes"
     print(timeit("slow_primes(1_000_000_000_000)"					,				setup=SCREAMING_SNAKE_CASE					,				number=100_0000       )       )
     print(timeit("primes(1_000_000_000_000)"					,				setup=SCREAMING_SNAKE_CASE					,				number=100_0000       )       )
     print(timeit("fast_primes(1_000_000_000_000)"					,				setup=SCREAMING_SNAKE_CASE					,				number=100_0000       )       )


if __name__ == "__main__":
      _UpperCamelCase          =     int(input("""Calculate primes up to:\n>> """).strip())
      for ret in primes(number):
            print(ret)
      benchmark()

