




'''simple docstring'''





from math import ceil





def       __a      (  _UpperCamelCase:       int = 1_001       )		->					int:

     """simple docstring"""




     _snake_case				= 1

     for i in range(1   ,		int(ceil(n / 2.0       )       )       ):
          _snake_case				= 2 * i + 1
          _snake_case				= 2 * i
          _snake_case				= total + 4 * odd**2 - 6 * even

     return total


if __name__ == "__main__":
   import sys

   if len(sys.argv) == 1:
      print(solution())
   else:
      try:
         UpperCamelCase_		:     int          =		int(sys.argv[1])
         print(solution(n))
      except ValueError:
         print('''Invalid entry - please enter a number''')








'''simple docstring'''





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


UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     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							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""falcon"""
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		["""past_key_values"""]






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=65_024   ,_SCREAMING_SNAKE_CASE=4_544   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=71   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=11   ,_SCREAMING_SNAKE_CASE=11   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          _snake_case				= vocab_size
          # Backward compatibility with n_embed kwarg
          _snake_case				= kwargs.pop("n_embed"   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= hidden_size if n_embed is None else n_embed
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= layer_norm_epsilon
          _snake_case				= initializer_range
          _snake_case				= use_cache
          _snake_case				= hidden_dropout
          _snake_case				= attention_dropout

          _snake_case				= bos_token_id
          _snake_case				= eos_token_id
          _snake_case				= num_attention_heads if num_kv_heads is None else num_kv_heads
          _snake_case				= alibi
          _snake_case				= new_decoder_architecture
          _snake_case				= multi_query  # Ignored when new_decoder_architecture is True
          _snake_case				= parallel_attn
          _snake_case				= bias

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






     @property
     def 							_lowercase      (					self       )				->     Union[str, Any]:
          return self.hidden_size // self.num_attention_heads






     @property
     def 							_lowercase      (					self       )				->     Optional[int]:
          return not self.alibi


