




"""simple docstring"""
from itertools import combinations, product





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   if xa == ya == 0 or xa == ya == 0:
      return False
   SCREAMING_SNAKE_CASE_:	List[Any]            =xa * xa + ya * ya
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =xa * xa + ya * ya
   SCREAMING_SNAKE_CASE_:	List[str]            =(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
   return (
       a_square + b_square == c_square
       or a_square + c_square == b_square
       or b_square + c_square == a_square
   )





def        __magic_name__			(							lowercase = 50	):
   return sum(
       1
       for pta, pta in combinations(product(range(limit + 1	)							,     repeat=2	)							,     2	)
       if is_right(*lowercase							,     *lowercase	)	)


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








"""simple docstring"""
import copy

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


_UpperCAmelCase       =     logging.get_logger(__name__)
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			int         =   'hybrid-clip'
  UpperCamelCase		:			Optional[int]         =   True



  def __init__(  self	:   List[Any]    ,						lowerCAmelCase	:   Any=512    ,						**lowerCAmelCase	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase						)

     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`."""						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =kwargs.pop("""text_config"""						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =kwargs.pop("""vision_config"""						)

     SCREAMING_SNAKE_CASE_:	int            =text_config.pop("""model_type"""						)
     SCREAMING_SNAKE_CASE_:	str            =vision_config.pop("""model_type"""						)

     from transformers import AutoConfig

     SCREAMING_SNAKE_CASE_:	Optional[int]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     if vision_model_type == "clip":
        SCREAMING_SNAKE_CASE_:	Tuple            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						).vision_config
     elif vision_model_type == "clip_vision_model":
        from transformers import CLIPVisionConfig

        SCREAMING_SNAKE_CASE_:	List[str]            =CLIPVisionConfig(**lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =AutoConfig.for_model(lowerCAmelCase    ,						**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Any            =projection_dim
     SCREAMING_SNAKE_CASE_:	int            =1.0



  @classmethod
  def 		lowerCamelCase__    (  cls	:   List[Any]    ,						lowerCAmelCase	:   PretrainedConfig    ,						lowerCAmelCase	:   PretrainedConfig    ,						**lowerCAmelCase	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




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





  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =copy.deepcopy(self.__dict__						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.text_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.vision_config.to_dict()
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.__class__.model_type
     return output



