


import math






def  _UpperCAmelCase						( __lowerCamelCase							)		->bool:


 '''simple docstring'''

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






def  _UpperCAmelCase						( __lowerCamelCase = 2000000							)		->int:


 '''simple docstring'''
 return sum(num for num in range(3			,  __lowerCamelCase			,  2							) if is_prime(__lowerCamelCase							)							) + 2 if n > 2 else 0


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










import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a					:				Optional[Any]       =      logging.get_logger(__name__)

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hybrid-clip"""
       _UpperCamelCase     		=   True

       def __init__(      self						,    a__=5_12						,    **a__       ):






        '''simple docstring'''






        super().__init__(**a__       )

        if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''       )

        if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''       )

        snake_case :			int							=   kwargs.pop('''text_config'''       )
        snake_case :			Optional[int]							=   kwargs.pop('''vision_config'''       )

        snake_case :			Optional[Any]							=   text_config.pop('''model_type'''       )
        snake_case :			str							=   vision_config.pop('''model_type'''       )

        from transformers import AutoConfig

        snake_case :			List[Any]							=   AutoConfig.for_model(a__						,    **a__       )

        if vision_model_type == "clip":
         snake_case :			Dict							=   AutoConfig.for_model(a__						,    **a__       ).vision_config
        elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         snake_case :			str							=   CLIPVisionConfig(**a__       )
        else:
         snake_case :			Any							=   AutoConfig.for_model(a__						,    **a__       )

        snake_case :			Optional[Any]							=   projection_dim
        snake_case :			int							=   1.0

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    **a__       ):






        '''simple docstring'''






        return cls(text_config=text_config.to_dict()						,    vision_config=vision_config.to_dict()						,    **a__       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   copy.deepcopy(self.__dict__       )
        snake_case :			Dict							=   self.text_config.to_dict()
        snake_case :			int							=   self.vision_config.to_dict()
        snake_case :			List[Any]							=   self.__class__.model_type
        return output






