

"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase_			=  {
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

lowercase_			=  {
    'google/realm-cc-news-pretrained-embedder': 512,
    'google/realm-cc-news-pretrained-encoder': 512,
    'google/realm-cc-news-pretrained-scorer': 512,
    'google/realm-cc-news-pretrained-openqa': 512,
    'google/realm-orqa-nq-openqa': 512,
    'google/realm-orqa-nq-reader': 512,
    'google/realm-orqa-wq-openqa': 512,
    'google/realm-orqa-wq-reader': 512,
}

lowercase_			=  {
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase		     =							RealmTokenizer


    def __init__(				self			:	Optional[int],lowercase_			:	Dict=None,lowercase_			:	Union[str, Any]=None,lowercase_			:	Union[str, Any]=True,lowercase_			:	Tuple="[UNK]",lowercase_			:	List[Any]="[SEP]",lowercase_			:	int="[PAD]",lowercase_			:	Tuple="[CLS]",lowercase_			:	Optional[int]="[MASK]",lowercase_			:	Dict=True,lowercase_			:	Any=None,**lowercase_			:	int,)->   int:





        '''simple docstring'''







        super().__init__(
            _snake_case,tokenizer_file=_snake_case,do_lower_case=_snake_case,unk_token=_snake_case,sep_token=_snake_case,pad_token=_snake_case,cls_token=_snake_case,mask_token=_snake_case,tokenize_chinese_chars=_snake_case,strip_accents=_snake_case,**_snake_case,)

        A__   				=       json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
        if (
            normalizer_state.get('lowercase',_snake_case		) != do_lower_case
            or normalizer_state.get('strip_accents',_snake_case		) != strip_accents
            or normalizer_state.get('handle_chinese_chars',_snake_case		) != tokenize_chinese_chars
        ):
            A__   				=       getattr(_snake_case,normalizer_state.pop('type'		)		)
            A__   				=       do_lower_case
            A__   				=       strip_accents
            A__   				=       tokenize_chinese_chars
            A__   				=       normalizer_class(**_snake_case		)

        A__   				=       do_lower_case


    def 						snake_case__					(				self			:	int,lowercase_			:	List[Any],**lowercase_			:	int		)->   List[str]:





        '''simple docstring'''







        A__   				=       PaddingStrategy.MAX_LENGTH

        A__   				=       text
        A__   				=       kwargs.pop('text_pair',_snake_case		)
        A__   				=       kwargs.pop('return_tensors',_snake_case		)

        A__   				=       {
            'input_ids': [],
            'attention_mask': [],
            'token_type_ids': [],
        }

        for idx, candidate_text in enumerate(_snake_case		):
            if batch_text_pair is not None:
                A__   				=       batch_text_pair[idx]
            else:
                A__   				=       None

            A__   				=       super().__call__(_snake_case,_snake_case,return_tensors=_snake_case,**_snake_case		)

            A__   				=       encoded_candidates.get('input_ids'		)
            A__   				=       encoded_candidates.get('attention_mask'		)
            A__   				=       encoded_candidates.get('token_type_ids'		)

            if encoded_input_ids is not None:
                output_data["input_ids"].append(_snake_case		)
            if encoded_attention_mask is not None:
                output_data["attention_mask"].append(_snake_case		)
            if encoded_token_type_ids is not None:
                output_data["token_type_ids"].append(_snake_case		)

        A__   				=       {key: item for key, item in output_data.items() if len(_snake_case		) != 0}

        return BatchEncoding(_snake_case,tensor_type=_snake_case		)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Dict,lowercase_			:	Optional[int]=None		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       [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 						snake_case__					(				self			:	List[Any],lowercase_			:	Optional[Any],lowercase_			:	List[str] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [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 						snake_case__					(				self			:	List[Any],lowercase_			:	Optional[int],lowercase_			:	Tuple = None		)->   Tuple[str]:





        '''simple docstring'''







        A__   				=       self._tokenizer.model.save(_snake_case,name=_snake_case		)
        return tuple(_snake_case		)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int = 1000				)							->				int:





    '''simple docstring'''






    A__   				=       3
    A__   				=       0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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

