





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class  __lowerCAmelCase	(      A_,					unittest.TestCase					):
			lowerCamelCase_					:					int	      =				BioGptTokenizer
			lowerCamelCase_					:					Union[str, Any]	      =				False






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_						:   Dict           =  [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "w</w>",
							    "r</w>",
							    "t</w>",
							    "lo",
							    "low",
							    "er</w>",
							    "low</w>",
							    "lowest</w>",
							    "newer</w>",
							    "wider</w>",
							    "<unk>",
							]
							snake_case_						:   int           =  dict(zip(snake_case__ ,							range(len(snake_case__   )   )   )   )
							snake_case_						:   Tuple           =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

							snake_case_						:   Optional[int]           =  os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES['''vocab_file''']   )
							snake_case_						:   int           =  os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES['''merges_file''']   )
							with open(self.vocab_file ,							'''w'''   ) as fp:
											fp.write(json.dumps(snake_case__   )   )
							with open(self.merges_file ,							'''w'''   ) as fp:
											fp.write('''\n'''.join(snake_case__   )   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  "lower newer"
							snake_case_						:   List[str]           =  "lower newer"
							return input_text, output_text






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  BioGptTokenizer(self.vocab_file ,							self.merges_file   )

							snake_case_						:   List[str]           =  "lower"
							snake_case_						:   Optional[int]           =  ["low", "er</w>"]
							snake_case_						:   List[Any]           =  tokenizer.tokenize(snake_case__   )
							self.assertListEqual(snake_case__ ,							snake_case__   )

							snake_case_						:   str           =  tokens + ["<unk>"]
							snake_case_						:   Dict           =  [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__   ) ,							snake_case__   )






			@slow
			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''   )

							snake_case_						:   Optional[Any]           =  tokenizer.encode('''sequence builders''' ,							add_special_tokens=snake_case__   )
							snake_case_						:   List[Any]           =  tokenizer.encode('''multi-sequence build''' ,							add_special_tokens=snake_case__   )

							snake_case_						:   Tuple           =  tokenizer.build_inputs_with_special_tokens(snake_case__   )
							snake_case_						:   Optional[int]           =  tokenizer.build_inputs_with_special_tokens(snake_case__ ,							snake_case__   )

							self.assertTrue(encoded_sentence == [2] + text   )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a   )







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)))
