






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


__a      :       Any				  =   logging.get_logger(__name__)

__a      :       Union[str, Any]				  =   {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"gpt_neox"




     def __init__(				self       :			Tuple ,	UpperCamelCase_       :			int=50_432 ,	UpperCamelCase_       :			Optional[int]=6_144 ,	UpperCamelCase_       :			Union[str, Any]=44 ,	UpperCamelCase_       :			str=64 ,	UpperCamelCase_       :			Dict=24_576 ,	UpperCamelCase_       :			List[str]="gelu" ,	UpperCamelCase_       :			Optional[Any]=0.25 ,	UpperCamelCase_       :			int=10_000 ,	UpperCamelCase_       :			Dict=0.0 ,	UpperCamelCase_       :			Dict=0.0 ,	UpperCamelCase_       :			Optional[int]=0.1 ,	UpperCamelCase_       :			Optional[int]=2_048 ,	UpperCamelCase_       :			List[str]=0.02 ,	UpperCamelCase_       :			str=1e-5 ,	UpperCamelCase_       :			str=True ,	UpperCamelCase_       :			Any=0 ,	UpperCamelCase_       :			List[str]=2 ,	UpperCamelCase_       :			Optional[Any]=False ,	UpperCamelCase_       :			Any=True ,	UpperCamelCase_       :			Union[str, Any]=None ,	**UpperCamelCase_       :			Dict ,	):


         """simple docstring"""



         super().__init__(bos_token_id=UpperCamelCase_ ,	eos_token_id=UpperCamelCase_ ,	**UpperCamelCase_				)
         __A         =	vocab_size
         __A         =	max_position_embeddings
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	intermediate_size
         __A         =	hidden_act
         __A         =	rotary_pct
         __A         =	rotary_emb_base
         __A         =	attention_dropout
         __A         =	hidden_dropout
         __A         =	classifier_dropout
         __A         =	initializer_range
         __A         =	layer_norm_eps
         __A         =	use_cache
         __A         =	tie_word_embeddings
         __A         =	use_parallel_residual
         __A         =	rope_scaling
         self._rope_scaling_validation()

         if self.hidden_size % self.num_attention_heads != 0:
             raise ValueError(
                 """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""				)





     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         if self.rope_scaling is None:
             return

         if not isinstance(self.rope_scaling ,	UpperCamelCase_				) or len(self.rope_scaling				) != 2:
             raise ValueError(
                 """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                 F"got {self.rope_scaling}"				)
         __A         =	self.rope_scaling.get("""type""" ,	UpperCamelCase_				)
         __A         =	self.rope_scaling.get("""factor""" ,	UpperCamelCase_				)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
         if rope_scaling_factor is None or not isinstance(UpperCamelCase_ ,	UpperCamelCase_				) or rope_scaling_factor <= 1.0:
             raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

    if sum(__lowercase    ) > max_sum or (remaining_nums_sum + sum(__lowercase    )) < max_sum:
        return
    if sum(__lowercase    ) == max_sum:
        result.append(__lowercase    )
        return
    for index in range(__lowercase			,				len(__lowercase    )    ):
        create_state_space_tree(
            __lowercase			,				__lowercase			,				index + 1			,				[*path, nums[index]]			,				__lowercase			,				remaining_nums_sum - nums[index]			,				)
__a      :       str				  =   [3, 34, 4, 12, 5, 2]
__a      :       Optional[Any]				  =   9
__a      :       List[str]				  =   generate_sum_of_subsets_soln(nums, max_sum)






print(*result)




