

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


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					Optional[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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "gpt_neox"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]=50432     , __lowerCamelCase					:			int=6144     , __lowerCamelCase					:			Optional[Any]=44     , __lowerCamelCase					:			Tuple=64     , __lowerCamelCase					:			Optional[int]=24576     , __lowerCamelCase					:			List[str]="gelu"     , __lowerCamelCase					:			Any=0.25     , __lowerCamelCase					:			List[Any]=10000     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[Any]=2048     , __lowerCamelCase					:			Tuple=0.02     , __lowerCamelCase					:			Tuple=1e-5     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int=0     , __lowerCamelCase					:			Optional[Any]=2     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			Optional[int]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   rotary_pct
       SCREAMING_SNAKE_CASE										=   rotary_emb_base
       SCREAMING_SNAKE_CASE										=   attention_dropout
       SCREAMING_SNAKE_CASE										=   hidden_dropout
       SCREAMING_SNAKE_CASE										=   classifier_dropout
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   use_cache
       SCREAMING_SNAKE_CASE										=   tie_word_embeddings
       SCREAMING_SNAKE_CASE										=   use_parallel_residual
       SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Union[str, Any]			):
       if self.rope_scaling is None:
           return

       if not isinstance(self.rope_scaling     , __lowerCamelCase			) 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										=   self.rope_scaling.get("type"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("factor"     , __lowerCamelCase			)
       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(__lowerCamelCase     , __lowerCamelCase			) or rope_scaling_factor <= 1.0:
           raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"			)

def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      if index == number_of_items:
            return 0
      UpperCamelCase__     : str											=					0
      UpperCamelCase__     : Optional[Any]											=					0
      UpperCamelCase__     : int											=					knapsack(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		index + 1		)
      if weights[index] <= max_weight:
            UpperCamelCase__     : List[Any]											=					values[index] + knapsack(
                UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		max_weight - weights[index]			,		index + 1		)
      return max(UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


