





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


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

_lowerCamelCase :    Optional[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 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "falcon"
							UpperCAmelCase_      						=   ["past_key_values"]






							def __init__( self     :					Any,			_UpperCAmelCase     :					str=6_5_0_2_4,			_UpperCAmelCase     :					List[Any]=4_5_4_4,			_UpperCAmelCase     :					Union[str, Any]=3_2,			_UpperCAmelCase     :					str=7_1,			_UpperCAmelCase     :					str=1E-5,			_UpperCAmelCase     :					List[str]=0.02,			_UpperCAmelCase     :					Tuple=True,			_UpperCAmelCase     :					Dict=0.0,			_UpperCAmelCase     :					Any=0.0,			_UpperCAmelCase     :					List[str]=None,			_UpperCAmelCase     :					Optional[Any]=False,			_UpperCAmelCase     :					Any=False,			_UpperCAmelCase     :					Any=True,			_UpperCAmelCase     :					Optional[Any]=True,			_UpperCAmelCase     :					str=False,			_UpperCAmelCase     :					Union[str, Any]=1_1,			_UpperCAmelCase     :					Tuple=1_1,			**_UpperCAmelCase     :					Tuple,			)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						vocab_size
														# Backward compatibility with n_embed kwarg
														SCREAMING_SNAKE_CASE__       :      Any     =						kwargs.pop("n_embed",			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						hidden_size if n_embed is None else n_embed
														SCREAMING_SNAKE_CASE__       :      int     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      str     =						layer_norm_epsilon
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						use_cache
														SCREAMING_SNAKE_CASE__       :      str     =						hidden_dropout
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						attention_dropout

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

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






							@property
							def  A_     ( self     :					Dict						)				->			Dict:





														"""simple docstring"""
														return self.hidden_size // self.num_attention_heads






							@property
							def  A_     ( self     :					Any						)				->			str:





														"""simple docstring"""
														return not self.alibi













from __future__ import annotations




def _a (  SCREAMING_SNAKE_CASE__      :  list[float]			,				SCREAMING_SNAKE_CASE__      :  list[float]       )	->    float:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						sorted(numsa + numsa       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						divmod(len(SCREAMING_SNAKE_CASE__       )			,				2       )
							if mod == 1:
														return all_numbers[div]
							else:
														return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		_lowerCamelCase :    List[str]  							= [float(x) for x in input('''Enter the elements of first array: ''').split()]
		_lowerCamelCase :    Any  							= [float(x) for x in input('''Enter the elements of second array: ''').split()]
		print(f"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}")






