

'''simple docstring'''






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


__SCREAMING_SNAKE_CASE     =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =						{
    """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__						):




     """simple docstring"""





     __UpperCamelCase									= "gpt_neox"







     def __init__(		self						:	Any      ,						A__						:	List[str]=5_0_4_3_2      ,						A__						:	Any=6_1_4_4      ,						A__						:	str=4_4      ,						A__						:	Tuple=6_4      ,						A__						:	int=2_4_5_7_6      ,						A__						:	List[Any]="gelu"      ,						A__						:	Dict=0.25      ,						A__						:	List[str]=1_0_0_0_0      ,						A__						:	List[Any]=0.0      ,						A__						:	Optional[Any]=0.0      ,						A__						:	List[Any]=0.1      ,						A__						:	Optional[int]=2_0_4_8      ,						A__						:	List[str]=0.02      ,						A__						:	int=1E-5      ,						A__						:	str=True      ,						A__						:	List[Any]=0      ,						A__						:	Optional[int]=2      ,						A__						:	List[str]=False      ,						A__						:	Dict=True      ,						A__						:	Any=None      ,						**A__						:	Any      ,						)       ->			List[str]:
          '''simple docstring'''




          super().__init__(bos_token_id=SCREAMING_SNAKE_CASE__      ,						eos_token_id=SCREAMING_SNAKE_CASE__      ,						**SCREAMING_SNAKE_CASE__	)
          a__   :      Optional[Any]     =						vocab_size
          a__   :      str     =						max_position_embeddings
          a__   :      Union[str, Any]     =						hidden_size
          a__   :      Any     =						num_hidden_layers
          a__   :      Union[str, Any]     =						num_attention_heads
          a__   :      Union[str, Any]     =						intermediate_size
          a__   :      Any     =						hidden_act
          a__   :      int     =						rotary_pct
          a__   :      Any     =						rotary_emb_base
          a__   :      Union[str, Any]     =						attention_dropout
          a__   :      Optional[Any]     =						hidden_dropout
          a__   :      Any     =						classifier_dropout
          a__   :      Any     =						initializer_range
          a__   :      Optional[Any]     =						layer_norm_eps
          a__   :      List[Any]     =						use_cache
          a__   :      str     =						tie_word_embeddings
          a__   :      List[Any]     =						use_parallel_residual
          a__   :      Tuple     =						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[int]	)       ->			Tuple:
          '''simple docstring'''




          if self.rope_scaling is None:
               return

          if not isinstance(self.rope_scaling      ,						SCREAMING_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}'	)
          a__   :      List[str]     =						self.rope_scaling.get('''type'''      ,						SCREAMING_SNAKE_CASE__	)
          a__   :      Optional[Any]     =						self.rope_scaling.get('''factor'''      ,						SCREAMING_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(SCREAMING_SNAKE_CASE__      ,						SCREAMING_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}'	)




"""simple docstring"""






from collections.abc import Callable

import numpy as np





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  np.ndarray:
			_UpperCamelCase					:str					     =	int(np.ceil((x_end - xa) / step_size		)		)
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros((n + 1,)		)
			_UpperCamelCase					:List[str]					     =	ya
			_UpperCamelCase					:Any					     =	xa

			for k in range(snake_case__		):
						_UpperCamelCase					:Union[str, Any]					     =	y[k] + step_size * ode_func(snake_case__       ,					y[k]		)
						x += step_size

			return y


if __name__ == "__main__":
					import doctest

					doctest.testmod()

