


import math
from collections.abc import Iterator
from itertools import takewhile




def    snake_case_     (  snake_case     )  ->				Optional[int]:





       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(A__     ) + 1     )			,							6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def    snake_case_     (  )  ->				List[str]:
       lowercase__:   Union[str, Any]  			=   2
       while True:
              if is_prime(A__     ):
                     yield num
              num += 1




def    snake_case_     (  snake_case = 2_00_00_00     )  ->				List[str]:
       return sum(takewhile(lambda snake_case     : x < n			,							prime_generator()     )     )


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






'''simple docstring'''






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    __snake_case       (   _SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    lowercase    =					BioGptTokenizer
    lowercase    =					False

    def __lowercase   (   self    :  List[Any]		)   ->  List[str]:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCAmelCase_			:      Optional[Any]			=   [
          """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>""",
      ]
      lowerCAmelCase_			:      int			=   dict(zip(lowerCamelCase    ,						range(len(lowerCamelCase		)		)		)		)
      lowerCAmelCase_			:      int			=   ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

      lowerCAmelCase_			:      Optional[int]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]		)
      lowerCAmelCase_			:      Union[str, Any]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]		)
      with open(self.vocab_file    ,						"""w"""		) as fp:
        fp.write(json.dumps(lowerCamelCase		)		)
      with open(self.merges_file    ,						"""w"""		) as fp:
        fp.write("""\n""".join(lowerCamelCase		)		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any		)   ->  List[str]:
      lowerCAmelCase_			:      Dict			=   """lower newer"""
      lowerCAmelCase_			:      str			=   """lower newer"""
      return input_text, output_text

    def __lowercase   (   self    :  Optional[int]		)   ->  str:
      lowerCAmelCase_			:      Any			=   BioGptTokenizer(self.vocab_file    ,						self.merges_file		)

      lowerCAmelCase_			:      int			=   """lower"""
      lowerCAmelCase_			:      str			=   ["""low""", """er</w>"""]
      lowerCAmelCase_			:      Any			=   tokenizer.tokenize(lowerCamelCase		)
      self.assertListEqual(lowerCamelCase    ,						lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   tokens + ["""<unk>"""]
      lowerCAmelCase_			:      Optional[Any]			=   [14, 15, 20]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		)    ,						lowerCamelCase		)





    @slow
    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Dict			=   BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)

      lowerCAmelCase_			:      int			=   tokenizer.encode("""sequence builders"""    ,						add_special_tokens=lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   tokenizer.encode("""multi-sequence build"""    ,						add_special_tokens=lowerCamelCase		)

      lowerCAmelCase_			:      str			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase    ,						lowerCamelCase		)

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



