






'''simple docstring'''
from __future__ import annotations





def  _a(  UpperCamelCase__				:						int,	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				list[list[int]]									=[]
      create_all_state(1,	UpperCamelCase__,	UpperCamelCase__,	[],	UpperCamelCase__							)
      return result





def  _a(  UpperCamelCase__				:						int,	UpperCamelCase__				:						int,	UpperCamelCase__				:						int,	UpperCamelCase__				:						list[int],	UpperCamelCase__				:						list[list[int]],	):




      '''simple docstring'''






      if level == 0:
            total_list.append(current_list[:]							)
            return

      for i in range(UpperCamelCase__,	total_number - level + 2							):
            current_list.append(UpperCamelCase__							)
            create_all_state(i + 1,	UpperCamelCase__,	level - 1,	UpperCamelCase__,	UpperCamelCase__							)
            current_list.pop()





def  _a(  UpperCamelCase__				:						list[list[int]]							):




      '''simple docstring'''






      for i in total_list:
            print(*UpperCamelCase__							)


if __name__ == "__main__":
  a_					= 4
  a_					= 2
  a_					= generate_all_combinations(n, k)
  print_all_state(total_list)







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					= logging.get_logger(__name__)

a_					= {
    '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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""gpt_neox"""




 def __init__(      self	:							List[Any]					,      __lowercase	:							Union[str, Any]=5_04_32					,      __lowercase	:							int=61_44					,      __lowercase	:							Tuple=44					,      __lowercase	:							List[str]=64					,      __lowercase	:							str=2_45_76					,      __lowercase	:							Dict="gelu"					,      __lowercase	:							Tuple=0.25					,      __lowercase	:							Tuple=1_00_00					,      __lowercase	:							Tuple=0.0					,      __lowercase	:							str=0.0					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Dict=20_48					,      __lowercase	:							Any=0.02					,      __lowercase	:							Dict=1e-5					,      __lowercase	:							List[Any]=True					,      __lowercase	:							str=0					,      __lowercase	:							Optional[Any]=2					,      __lowercase	:							Tuple=False					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Optional[Any]=None					,      **__lowercase	:							Any					,      )	->						Dict:
       super().__init__(bos_token_id=__lowercase					,      eos_token_id=__lowercase					,      **__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Any									=hidden_size
       SCREAMING_SNAKE_CASE__     :				str									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Any									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Dict									=hidden_act
       SCREAMING_SNAKE_CASE__     :				str									=rotary_pct
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=rotary_emb_base
       SCREAMING_SNAKE_CASE__     :				List[Any]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_dropout
       SCREAMING_SNAKE_CASE__     :				str									=classifier_dropout
       SCREAMING_SNAKE_CASE__     :				Any									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=layer_norm_eps
       SCREAMING_SNAKE_CASE__     :				Any									=use_cache
       SCREAMING_SNAKE_CASE__     :				Tuple									=tie_word_embeddings
       SCREAMING_SNAKE_CASE__     :				Tuple									=use_parallel_residual
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       if self.rope_scaling is None:
             return

       if not isinstance(self.rope_scaling					,      __lowercase      ) 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}"      )
       SCREAMING_SNAKE_CASE__     :				int									=self.rope_scaling.get('''type'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.rope_scaling.get('''factor'''					,      __lowercase      )
       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(__lowercase					,      __lowercase      ) or rope_scaling_factor <= 1.0:
             raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"      )