





"""simple docstring"""




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


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

a		:				int  			=    {
    '''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  __UpperCamelCase       (		lowercase__		):
							lowerCamelCase     :      Union[str, Any]         ="""gpt_neox"""






							def __init__(     self   ,      lowerCAmelCase__=5_0432   ,      lowerCAmelCase__=6144   ,      lowerCAmelCase__=44   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__=2_4576   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=0.25   ,      lowerCAmelCase__=1_0000   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=2048   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=1E-5   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								super().__init__(bos_token_id=__lowercase   ,      eos_token_id=__lowercase   ,      **__lowercase   )
								a   :	List[str]			=       vocab_size
								a   :	Dict			=       max_position_embeddings
								a   :	Any			=       hidden_size
								a   :	Any			=       num_hidden_layers
								a   :	List[str]			=       num_attention_heads
								a   :	Optional[int]			=       intermediate_size
								a   :	str			=       hidden_act
								a   :	List[str]			=       rotary_pct
								a   :	Optional[int]			=       rotary_emb_base
								a   :	Optional[int]			=       attention_dropout
								a   :	Dict			=       hidden_dropout
								a   :	Optional[Any]			=       classifier_dropout
								a   :	Dict			=       initializer_range
								a   :	str			=       layer_norm_eps
								a   :	List[Any]			=       use_cache
								a   :	Union[str, Any]			=       tie_word_embeddings
								a   :	Optional[int]			=       use_parallel_residual
								a   :	Optional[int]			=       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 					__a     (     self   )			->				str:
								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}"""   )
								a   :	int			=       self.rope_scaling.get("type"   ,      __lowercase   )
								a   :	List[str]			=       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}"""   )









"""simple docstring"""




from ..utils import DummyObject, requires_backends


class  __UpperCamelCase       (		metaclass=a__		):
							lowerCamelCase     :      Optional[Any]         =["""transformers""", """torch""", """note_seq"""]






							def __init__(     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								requires_backends(self   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				int:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )


