



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 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''gpt_neox'''




				def __init__(							self						,				_snake_case=50432						,				_snake_case=6144						,				_snake_case=44						,				_snake_case=64						,				_snake_case=24576						,				_snake_case="gelu"						,				_snake_case=0.25						,				_snake_case=10000						,				_snake_case=0.0						,				_snake_case=0.0						,				_snake_case=0.1						,				_snake_case=2048						,				_snake_case=0.02						,				_snake_case=1e-5						,				_snake_case=True						,				_snake_case=0						,				_snake_case=2						,				_snake_case=False						,				_snake_case=True						,				_snake_case=None						,				**_snake_case						,				):



									"""simple docstring"""





									super().__init__(bos_token_id=_snake_case						,				eos_token_id=_snake_case						,				**_snake_case		)
									_lowerCAmelCase						=    vocab_size
									_lowerCAmelCase						=    max_position_embeddings
									_lowerCAmelCase						=    hidden_size
									_lowerCAmelCase						=    num_hidden_layers
									_lowerCAmelCase						=    num_attention_heads
									_lowerCAmelCase						=    intermediate_size
									_lowerCAmelCase						=    hidden_act
									_lowerCAmelCase						=    rotary_pct
									_lowerCAmelCase						=    rotary_emb_base
									_lowerCAmelCase						=    attention_dropout
									_lowerCAmelCase						=    hidden_dropout
									_lowerCAmelCase						=    classifier_dropout
									_lowerCAmelCase						=    initializer_range
									_lowerCAmelCase						=    layer_norm_eps
									_lowerCAmelCase						=    use_cache
									_lowerCAmelCase						=    tie_word_embeddings
									_lowerCAmelCase						=    use_parallel_residual
									_lowerCAmelCase						=    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		):



									"""simple docstring"""





									if self.rope_scaling is None:
														return

									if not isinstance(self.rope_scaling						,				_snake_case		) 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}'		)
									_lowerCAmelCase						=    self.rope_scaling.get("""type"""						,				_snake_case		)
									_lowerCAmelCase						=    self.rope_scaling.get("""factor"""						,				_snake_case		)
									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(_snake_case						,				_snake_case		) 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				(   a  )      ->      int:
					if not nums:
										return 0
					__A	:						Optional[int]       =				nums[0]
					__A	:						str       =				0
					for num in nums[1:]:
										__A    ,				__A	:						Tuple       =				(
										    max_excluding + num,
										    max(a					,						a  ),
										)
					return max(a					,						a  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




