

def 						UpperCamelCase (lowercase_:			int )					->				bool:
	A__							:   Any					       =		(1 + 24 * n) ** 0.5
	return ((1 + root) / 6) % 1 == 0

def 						UpperCamelCase (lowercase_:			int = 5000 )					->				int:
	A__							:   Dict					       =		[(i * (3 * i - 1)) // 2 for i in range(1   ,				lowercase_ )]
	for i, pentagonal_i in enumerate(lowercase_ ):
		for j in range(lowercase_   ,				len(lowercase_ ) ):
			A__							:   Any					       =		pentagonal_nums[j]
			A__							:   List[str]					       =		pentagonal_i + pentagonal_j
			A__							:   Optional[int]					       =		pentagonal_j - pentagonal_i
			if is_pentagonal(lowercase_ ) and is_pentagonal(lowercase_ ):
				return b

	return -1


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






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


A_	:			str  	=      logging.get_logger(__name__)

A_	:			Any  	=      {
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	'''falcon'''
					UpperCAmelCase__:       Any  							=	['''past_key_values''']






					def __init__(			self   ,	A__=6_5024   ,	A__=4544   ,	A__=32   ,	A__=71   ,	A__=1e-5   ,	A__=0.0_2   ,	A__=True   ,	A__=0.0   ,	A__=0.0   ,	A__=None   ,	A__=False   ,	A__=False   ,	A__=True   ,	A__=True   ,	A__=False   ,	A__=11   ,	A__=11   ,	**A__   ,	):
						A__							:   Dict					       =		vocab_size
						# Backward compatibility with n_embed kwarg
						A__							:   Union[str, Any]					       =		kwargs.pop("""n_embed"""   ,	A__      )
						A__							:   Optional[Any]					       =		hidden_size if n_embed is None else n_embed
						A__							:   Any					       =		num_hidden_layers
						A__							:   Any					       =		num_attention_heads
						A__							:   Optional[Any]					       =		layer_norm_epsilon
						A__							:   Tuple					       =		initializer_range
						A__							:   Tuple					       =		use_cache
						A__							:   str					       =		hidden_dropout
						A__							:   List[str]					       =		attention_dropout

						A__							:   List[Any]					       =		bos_token_id
						A__							:   Optional[Any]					       =		eos_token_id
						A__							:   Optional[Any]					       =		num_attention_heads if num_kv_heads is None else num_kv_heads
						A__							:   List[str]					       =		alibi
						A__							:   Tuple					       =		new_decoder_architecture
						A__							:   List[str]					       =		multi_query  # Ignored when new_decoder_architecture is True
						A__							:   List[Any]					       =		parallel_attn
						A__							:   int					       =		bias

						super().__init__(bos_token_id=A__   ,	eos_token_id=A__   ,	**A__      )






					@property
					def   __A      (			self      ):
						return self.hidden_size // self.num_attention_heads






					@property
					def   __A      (			self      ):
						return not self.alibi



