



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   (    snake_case_	):







					'''simple docstring'''







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







							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}'''  )
							UpperCAmelCase_						       =					self.rope_scaling.get("type"     ,						__lowercase  )
							UpperCAmelCase_						       =					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase_			:  List[str]										=   {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		List[Any]     				=    '''fnet'''


 def __init__(   self   ,   __lowercase=32_000   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu_new"   ,   __lowercase=0.1   ,   __lowercase=512   ,   __lowercase=4   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=False   ,   __lowercase=512   ,   __lowercase=3   ,   __lowercase=1   ,   __lowercase=2   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(pad_token_id=__lowercase   ,   bos_token_id=__lowercase   ,   eos_token_id=__lowercase   ,   **__lowercase    )

  __A	:		Union[str, Any]	  =      vocab_size
  __A	:		Dict	  =      max_position_embeddings
  __A	:		List[str]	  =      hidden_size
  __A	:		Tuple	  =      num_hidden_layers
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		Dict	  =      hidden_act
  __A	:		List[str]	  =      hidden_dropout_prob
  __A	:		str	  =      initializer_range
  __A	:		Dict	  =      type_vocab_size
  __A	:		int	  =      layer_norm_eps
  __A	:		Tuple	  =      use_tpu_fourier_optimizations
  __A	:		Optional[Any]	  =      tpu_short_seq_length






