'''simple docstring'''

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

__a: Optional[int]          =   """▁"""

__a: Any          =   {"""vocab_file""": """prophetnet.tokenizer"""}

__a: List[Any]          =   {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

__a: Tuple          =   {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

__a: List[Any]          =   {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    str  =					collections.OrderedDict()
 with open(UpperCAmelCase		,     '''r'''		,     encoding='''utf-8'''     ) as reader:
  lowercase__				:    int  =					reader.readlines()
 for index, token in enumerate(UpperCAmelCase     ):
  lowercase__				:    List[Any]  =					token.rstrip('''\n'''     )
  lowercase__				:    List[str]  =					index
 return vocab





class 			UpperCAmelCase  (						__SCREAMING_SNAKE_CASE					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE							=    PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE							=    ["""input_ids""", """attention_mask"""]

       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase="[SEP]"	,   __lowerCAmelCase="[SEP]"	,   __lowerCAmelCase="[SEP]"	,   __lowerCAmelCase="[UNK]"	,   __lowerCAmelCase="[PAD]"	,   __lowerCAmelCase="[CLS]"	,   __lowerCAmelCase="[MASK]"	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  str:
        lowercase__				:    Tuple  =					{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   sep_token=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   cls_token=__lowerCAmelCase	,   mask_token=__lowerCAmelCase	,   sp_model_kwargs=self.sp_model_kwargs	,   **__lowerCAmelCase	,   )

        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece''' )
         raise

        lowercase__				:    Dict  =					spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(str(__lowerCAmelCase ) )
        lowercase__				:    Optional[int]  =					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'

        # put special tokens and [unused] tokens into the vocab
        lowercase__				:    Any  =					{"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

        for i in range(10 ):
         lowercase__				:    Dict  =					F"""[unused{i}]"""
         lowercase__				:    List[Any]  =					5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase__				:    Any  =					12
        lowercase__				:    Optional[int]  =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(__lowerCAmelCase )

       def __getstate__(						self )  ->  int:
        lowercase__				:    List[Any]  =					self.__dict__.copy()
        lowercase__				:    Union[str, Any]  =					None
        return state

       def __setstate__(						self	,   __lowerCAmelCase )  ->  Union[str, Any]:
        lowercase__				:    str  =					d
        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece''' )
         raise

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

        lowercase__				:    List[Any]  =					spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   __lowerCAmelCase = False )  ->  Optional[int]:

        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 None:
         return ([0] * len(__lowerCAmelCase )) + [1]
        return ([0] * len(__lowerCAmelCase )) + [1] + ([0] * len(__lowerCAmelCase )) + [1]

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Dict:
        lowercase__				:    Tuple  =					[self.sep_token_id]

        if token_ids_a is None:
         return len(token_ids_a + sep ) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]

       @property
       def _lowerCAmelCase(						self )  ->  Tuple:
        return len(self.sp_model ) + self.fairseq_offset

       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    List[str]  =					{self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Union[str, Any]:
        return self.sp_model.encode(__lowerCAmelCase	,   out_type=__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        lowercase__				:    List[str]  =					self.sp_model.PieceToId(__lowerCAmelCase )

        # 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 _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Any:
        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 _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    Any  =					"""""".join(__lowerCAmelCase ).replace(__lowerCAmelCase	,   ''' ''' ).strip()
        return out_string

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Optional[Any]:
        if not os.path.isdir(__lowerCAmelCase ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
         return
        lowercase__				:    List[Any]  =					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:
          lowercase__				:    Optional[Any]  =					self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase )

        return (out_vocab_file,)


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Optional[int]:

        if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
        lowercase__				:    List[str]  =					[self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep



def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


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




