





'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


a__					:			Tuple          =     logging.get_logger(__name__)

a__					:			Dict          =     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__					:			List[str]          =     {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

a__					:			List[Any]          =     {
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  VOCAB_FILES_NAMES
	__lowerCAmelCase       						=  PRETRAINED_VOCAB_FILES_MAP
	__lowerCAmelCase       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__lowerCAmelCase       						=  ['''input_ids''', '''attention_mask''']
	__lowerCAmelCase       						=  RobertaTokenizer





	def __init__(		self      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       UpperCamelCase_="replace"      ,       UpperCamelCase_="<s>"      ,       UpperCamelCase_="</s>"      ,       UpperCamelCase_="</s>"      ,       UpperCamelCase_="<s>"      ,       UpperCamelCase_="<unk>"      ,       UpperCamelCase_="<pad>"      ,       UpperCamelCase_="<mask>"      ,       UpperCamelCase_=False      ,       UpperCamelCase_=True      ,       **UpperCamelCase_      ,       )      ->		Union[str, Any]:
		super().__init__(
		    UpperCamelCase_      ,       UpperCamelCase_      ,       tokenizer_file=UpperCamelCase_      ,       errors=UpperCamelCase_      ,       bos_token=UpperCamelCase_      ,       eos_token=UpperCamelCase_      ,       sep_token=UpperCamelCase_      ,       cls_token=UpperCamelCase_      ,       unk_token=UpperCamelCase_      ,       pad_token=UpperCamelCase_      ,       mask_token=UpperCamelCase_      ,       add_prefix_space=UpperCamelCase_      ,       trim_offsets=UpperCamelCase_      ,       **UpperCamelCase_      ,       )

		snake_case__	       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
		if pre_tok_state.get('add_prefix_space'      ,       UpperCamelCase_		) != add_prefix_space:
			snake_case__	       =   getattr(UpperCamelCase_      ,       pre_tok_state.pop('type'		)		)
			snake_case__	       =   add_prefix_space
			snake_case__	       =   pre_tok_class(**UpperCamelCase_		)

		snake_case__	       =   add_prefix_space

		snake_case__	       =   'post_processor'
		snake_case__	       =   getattr(self.backend_tokenizer      ,       UpperCamelCase_      ,       UpperCamelCase_		)
		if tokenizer_component_instance:
			snake_case__	       =   json.loads(tokenizer_component_instance.__getstate__()		)

			# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
			if "sep" in state:
				snake_case__	       =   tuple(state['sep']		)
			if "cls" in state:
				snake_case__	       =   tuple(state['cls']		)

			snake_case__	       =   False

			if state.get('add_prefix_space'      ,       UpperCamelCase_		) != add_prefix_space:
				snake_case__	       =   add_prefix_space
				snake_case__	       =   True

			if state.get('trim_offsets'      ,       UpperCamelCase_		) != trim_offsets:
				snake_case__	       =   trim_offsets
				snake_case__	       =   True

			if changes_to_apply:
				snake_case__	       =   getattr(UpperCamelCase_      ,       state.pop('type'		)		)
				snake_case__	       =   component_class(**UpperCamelCase_		)
				setattr(self.backend_tokenizer      ,       UpperCamelCase_      ,       UpperCamelCase_		)





	@property
	def   _snake_case   (		self		)      ->		str:
		if self._mask_token is None:
			if self.verbose:
				logger.error('Using mask_token, but it is not set yet.'		)
			return None
		return str(self._mask_token		)





	@mask_token.setter
	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Dict:
		snake_case__	       =   AddedToken(UpperCamelCase_      ,       lstrip=UpperCamelCase_      ,       rstrip=UpperCamelCase_		) if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) else value
		snake_case__	       =   value





	def   _snake_case   (		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		BatchEncoding:
		snake_case__	       =   kwargs.get('is_split_into_words'      ,       UpperCamelCase_		)
		assert self.add_prefix_space or not is_split_into_words, (
		    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
		    "to use it with pretokenized inputs."
		)

		return super()._batch_encode_plus(*UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		BatchEncoding:
		snake_case__	       =   kwargs.get('is_split_into_words'      ,       UpperCamelCase_		)

		assert self.add_prefix_space or not is_split_into_words, (
		    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
		    "to use it with pretokenized inputs."
		)

		return super()._encode_plus(*UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None		)      ->		Tuple[str]:
		snake_case__	       =   self._tokenizer.model.save(UpperCamelCase_      ,       name=UpperCamelCase_		)
		return tuple(UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Dict:
		snake_case__	       =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
		if token_ids_a is None:
			return output

		return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None		)      ->		List[int]:
		snake_case__	       =   [self.sep_token_id]
		snake_case__	       =   [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 + sep + token_ids_a + sep		) * [0]










'''simple docstring'''




def 						__lowerCamelCase			(	UpperCAmelCase_ = 10_00							)   ->int:
	return sum(e for e in range(3					,				UpperCAmelCase_							) if e % 3 == 0 or e % 5 == 0							)


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



