




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


lowerCamelCase__ :   int					=							logging.get_logger(__name__)

lowerCamelCase__ :   int					=							{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__ :   int					=							{
    """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"""
        ),
    },
}

lowerCamelCase__ :   Optional[int]					=							{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}





class        lowerCamelCase_							(      __UpperCamelCase					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    ["input_ids", "attention_mask"]
 lowercase_				    =    RobertaTokenizer



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

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

       SCREAMING_SNAKE_CASE_							=			add_prefix_space

       SCREAMING_SNAKE_CASE_							=			'post_processor'
       SCREAMING_SNAKE_CASE_							=			getattr(self.backend_tokenizer				,				_lowerCAmelCase				,				_lowerCAmelCase						)
       if tokenizer_component_instance:
             SCREAMING_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:
                   SCREAMING_SNAKE_CASE_							=			tuple(state['sep']						)
             if "cls" in state:
                   SCREAMING_SNAKE_CASE_							=			tuple(state['cls']						)

             SCREAMING_SNAKE_CASE_							=			False

             if state.get('add_prefix_space'				,				_lowerCAmelCase						) != add_prefix_space:
                   SCREAMING_SNAKE_CASE_							=			add_prefix_space
                   SCREAMING_SNAKE_CASE_							=			True

             if state.get('trim_offsets'				,				_lowerCAmelCase						) != trim_offsets:
                   SCREAMING_SNAKE_CASE_							=			trim_offsets
                   SCREAMING_SNAKE_CASE_							=			True

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



 @property
 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       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       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			AddedToken(_lowerCAmelCase				,				lstrip=_lowerCAmelCase				,				rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase				,				_lowerCAmelCase						) else value
       SCREAMING_SNAKE_CASE_							=			value



 def       lowerCAmelCase_  (							self  :		int				,				*_lowerCAmelCase  :		Union[str, Any]				,				**_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			kwargs.get('is_split_into_words'				,				_lowerCAmelCase						)
       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(*_lowerCAmelCase				,				**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				*_lowerCAmelCase  :		int				,				**_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			kwargs.get('is_split_into_words'				,				_lowerCAmelCase						)

       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(*_lowerCAmelCase				,				**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       SCREAMING_SNAKE_CASE_							=			self._tokenizer.model.save(_lowerCAmelCase				,				name=_lowerCAmelCase						)
       return tuple(_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[Any]=None						):
       SCREAMING_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       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.sep_token_id]
       SCREAMING_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]






from __future__ import annotations

import math


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5						,	int(math.sqrt(_UpperCAmelCase ) + 1 )						,	6 ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


_UpperCAmelCase				:       Any	   =				[num for num in range(3, 10_00_01, 2) if not is_prime(num)]


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[int]:
    if not isinstance(_UpperCAmelCase						,	_UpperCAmelCase ):
        raise ValueError('n must be an integer' )
    if n <= 0:
        raise ValueError('n must be >= 0' )

    lowerCamelCase__    :      int					 =		[]
    for num in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      Union[str, Any]					 =		0
        while 2 * i * i <= odd_composites[num]:
            lowerCamelCase__    :      Dict					 =		odd_composites[num] - 2 * i * i
            if is_prime(_UpperCAmelCase ):
                break
            i += 1
        else:
            list_nums.append(odd_composites[num] )
            if len(_UpperCAmelCase ) == n:
                return list_nums

    return []


def        SCREAMING_SNAKE_CASE      (       )			->     int:
    return compute_nums(1 )[0]


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





