





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_bart import BartTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase_    =    {
    '''facebook/bart-base''': 1_0_2_4,
    '''facebook/bart-large''': 1_0_2_4,
    '''facebook/bart-large-mnli''': 1_0_2_4,
    '''facebook/bart-large-cnn''': 1_0_2_4,
    '''facebook/bart-large-xsum''': 1_0_2_4,
    '''yjernite/bart_eli5''': 1_0_2_4,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Tuple	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					Union[str, Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					Optional[int]	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Any	      =				['''input_ids''', '''attention_mask''']
			lowerCamelCase_					:					int	      =				BartTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="replace" ,							__magic_name__="<s>" ,							__magic_name__="</s>" ,							__magic_name__="</s>" ,							__magic_name__="<s>" ,							__magic_name__="<unk>" ,							__magic_name__="<pad>" ,							__magic_name__="<mask>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Dict:


							'''simple docstring'''



							super().__init__(
							    __magic_name__ ,							__magic_name__ ,							tokenizer_file=__magic_name__ ,							errors=__magic_name__ ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							sep_token=__magic_name__ ,							cls_token=__magic_name__ ,							unk_token=__magic_name__ ,							pad_token=__magic_name__ ,							mask_token=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							**__magic_name__ ,							)

							snake_case_						:   List[str]           =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get('''add_prefix_space''' ,							__magic_name__   ) != add_prefix_space:
											snake_case_						:   Any           =  getattr(__magic_name__ ,							pre_tok_state.pop('''type'''   )   )
											snake_case_						:   Any           =  add_prefix_space
											snake_case_						:   List[str]           =  pre_tok_class(**__magic_name__   )

							snake_case_						:   Optional[int]           =  add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							snake_case_						:   Optional[int]           =  '''post_processor'''
							snake_case_						:   str           =  getattr(self.backend_tokenizer ,							__magic_name__ ,							__magic_name__   )
							if tokenizer_component_instance:
											snake_case_						:   List[Any]           =  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_						:   Optional[Any]           =  tuple(state['''sep''']   )
											if "cls" in state:
															snake_case_						:   Dict           =  tuple(state['''cls''']   )

											snake_case_						:   Dict           =  False

											if state.get('''add_prefix_space''' ,							__magic_name__   ) != add_prefix_space:
															snake_case_						:   List[str]           =  add_prefix_space
															snake_case_						:   Tuple           =  True

											if state.get('''trim_offsets''' ,							__magic_name__   ) != trim_offsets:
															snake_case_						:   List[Any]           =  trim_offsets
															snake_case_						:   List[str]           =  True

											if changes_to_apply:
															snake_case_						:   Dict           =  getattr(__magic_name__ ,							state.pop('''type'''   )   )
															snake_case_						:   Union[str, Any]           =  component_class(**__magic_name__   )
															setattr(self.backend_tokenizer ,							__magic_name__ ,							__magic_name__   )






			@property
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							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 ,							__magic_name__   )					->       str:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  AddedToken(__magic_name__ ,							lstrip=__magic_name__ ,							rstrip=__magic_name__   ) if isinstance(__magic_name__ ,							__magic_name__   ) else value
							snake_case_						:   List[Any]           =  value






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       BatchEncoding:


							'''simple docstring'''



							snake_case_						:   int           =  kwargs.get('''is_split_into_words''' ,							__magic_name__   )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''   )

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






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       BatchEncoding:


							'''simple docstring'''



							snake_case_						:   str           =  kwargs.get('''is_split_into_words''' ,							__magic_name__   )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''   )

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       Tuple[str]:


							'''simple docstring'''



							snake_case_						:   Any           =  self._tokenizer.model.save(__magic_name__ ,							name=__magic_name__   )
							return tuple(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Tuple           =  [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 ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [self.sep_token_id]
							snake_case_						:   Any           =  [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]







import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=1_000			)		->						str:






				"""simple docstring"""


				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case_						:   int           =  n - 1
				snake_case_						:   Union[str, Any]           =  0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case_						:   List[Any]           =  0
				while count < prec:
								snake_case_						:   Tuple           =  random.randint(2			,    n - 1			)
								snake_case_						:   List[Any]           =  bin_exp_mod(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								if b != 1:
												snake_case_						:   Any           =  True
												for _ in range(_UpperCamelCase			):
																if b == n - 1:
																				snake_case_						:   Tuple           =  False
																				break
																snake_case_						:   List[Any]           =  b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
	lowerCAmelCase_    =    abs(int(input('''Enter bound : ''').strip()))
	print('''Here\'s the list of primes:''')
	print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
