


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_A		:				List[Any]									=       {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A		:				Any									=       {
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

_A		:				int									=       {
    '''google/electra-small-generator''': 512,
    '''google/electra-base-generator''': 512,
    '''google/electra-large-generator''': 512,
    '''google/electra-small-discriminator''': 512,
    '''google/electra-base-discriminator''': 512,
    '''google/electra-large-discriminator''': 512,
}

_A		:				Tuple									=       {
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  List[Any]						      =	VOCAB_FILES_NAMES
     UpperCAmelCase__ :  str						      =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :  List[str]						      =	PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__ :  Union[str, Any]						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :  Dict						      =	ElectraTokenizer





     def __init__(    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=None    ,	SCREAMING_SNAKE_CASE_					:				Any=None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Any="[UNK]"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="[SEP]"    ,	SCREAMING_SNAKE_CASE_					:				str="[PAD]"    ,	SCREAMING_SNAKE_CASE_					:				List[str]="[CLS]"    ,	SCREAMING_SNAKE_CASE_					:				str="[MASK]"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=None    ,	**SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	):
        super().__init__(
            SCREAMING_SNAKE_CASE_    ,	tokenizer_file=SCREAMING_SNAKE_CASE_    ,	do_lower_case=SCREAMING_SNAKE_CASE_    ,	unk_token=SCREAMING_SNAKE_CASE_    ,	sep_token=SCREAMING_SNAKE_CASE_    ,	pad_token=SCREAMING_SNAKE_CASE_    ,	cls_token=SCREAMING_SNAKE_CASE_    ,	mask_token=SCREAMING_SNAKE_CASE_    ,	tokenize_chinese_chars=SCREAMING_SNAKE_CASE_    ,	strip_accents=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_    ,	)

        __lowerCamelCase:				Optional[int]     =	json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
        if (
            normalizer_state.get("""lowercase"""    ,	SCREAMING_SNAKE_CASE_							) != do_lower_case
            or normalizer_state.get("""strip_accents"""    ,	SCREAMING_SNAKE_CASE_							) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""    ,	SCREAMING_SNAKE_CASE_							) != tokenize_chinese_chars
        ):
           __lowerCamelCase:				List[str]     =	getattr(SCREAMING_SNAKE_CASE_    ,	normalizer_state.pop("""type"""							)							)
           __lowerCamelCase:				Optional[Any]     =	do_lower_case
           __lowerCamelCase:				str     =	strip_accents
           __lowerCamelCase:				Optional[int]     =	tokenize_chinese_chars
           __lowerCamelCase:				Union[str, Any]     =	normalizer_class(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	do_lower_case





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Tuple=None							):
        __lowerCamelCase:				int     =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

        return output





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        __lowerCamelCase:				Dict     =	[self.sep_token_id]
        __lowerCamelCase:				Dict     =	[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  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None							):
        __lowerCamelCase:				int     =	self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,	name=SCREAMING_SNAKE_CASE_							)
        return tuple(SCREAMING_SNAKE_CASE_							)






def     __lowerCAmelCase						(							snake_case	:    int = 1000000 )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	set(range(3						,      snake_case						,      2 ) )
   primes.add(2 )
   for p in range(3						,      snake_case						,      2 ):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p						,      snake_case						,      snake_case ) ) )

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

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

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


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


