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


__SCREAMING_SNAKE_CASE      : str       =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      : 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 		__magic_name__ (    a_     ):
		_lowerCAmelCase		=						"gpt_neox"





		def __init__(				self     :						int    ,	lowerCamelCase__     :						Optional[int]=5_0_4_3_2    ,	lowerCamelCase__     :						int=6_1_4_4    ,	lowerCamelCase__     :						Tuple=4_4    ,	lowerCamelCase__     :						Any=6_4    ,	lowerCamelCase__     :						Dict=2_4_5_7_6    ,	lowerCamelCase__     :						Tuple="gelu"    ,	lowerCamelCase__     :						Tuple=0.2_5    ,	lowerCamelCase__     :						List[Any]=1_0_0_0_0    ,	lowerCamelCase__     :						List[Any]=0.0    ,	lowerCamelCase__     :						Optional[Any]=0.0    ,	lowerCamelCase__     :						str=0.1    ,	lowerCamelCase__     :						Union[str, Any]=2_0_4_8    ,	lowerCamelCase__     :						int=0.0_2    ,	lowerCamelCase__     :						Optional[Any]=1E-5    ,	lowerCamelCase__     :						List[str]=True    ,	lowerCamelCase__     :						Dict=0    ,	lowerCamelCase__     :						Optional[int]=2    ,	lowerCamelCase__     :						Optional[int]=False    ,	lowerCamelCase__     :						Dict=True    ,	lowerCamelCase__     :						Dict=None    ,	**lowerCamelCase__     :						Tuple    ,	):
							super().__init__(bos_token_id=__UpperCamelCase    ,	eos_token_id=__UpperCamelCase    ,	**__UpperCamelCase					)
							lowerCAmelCase		:       Tuple							=  vocab_size
							lowerCAmelCase		:       Optional[Any]							=  max_position_embeddings
							lowerCAmelCase		:       str							=  hidden_size
							lowerCAmelCase		:       Tuple							=  num_hidden_layers
							lowerCAmelCase		:       List[str]							=  num_attention_heads
							lowerCAmelCase		:       int							=  intermediate_size
							lowerCAmelCase		:       Tuple							=  hidden_act
							lowerCAmelCase		:       Optional[Any]							=  rotary_pct
							lowerCAmelCase		:       Dict							=  rotary_emb_base
							lowerCAmelCase		:       Optional[int]							=  attention_dropout
							lowerCAmelCase		:       List[str]							=  hidden_dropout
							lowerCAmelCase		:       Optional[int]							=  classifier_dropout
							lowerCAmelCase		:       str							=  initializer_range
							lowerCAmelCase		:       Tuple							=  layer_norm_eps
							lowerCAmelCase		:       Union[str, Any]							=  use_cache
							lowerCAmelCase		:       int							=  tie_word_embeddings
							lowerCAmelCase		:       Tuple							=  use_parallel_residual
							lowerCAmelCase		:       Optional[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    _A				(				self     :						Optional[int]					):
							if self.rope_scaling is None:
												return

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








from math import pow, sqrt





def    __lowerCAmelCase   (    *__snake_case							):
     __lowerCAmelCase             =       len(__snake_case							) > 0 and all(value > 0.0 for value in values							)
     return result





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     return (
         round(sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case							)
         else ValueError("Input Error: Molar mass values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(effusion_rate * sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(effusion_rate / sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(molar_mass / pow(effusion_rate_a / effusion_rate_a       ,			2							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(pow(effusion_rate_a / effusion_rate_a       ,			2							) / molar_mass       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )






