


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


UpperCamelCase					:       Optional[Any]			    =     logging.get_logger(__name__)

UpperCamelCase					:       str			    =     {
    """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 A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		'gpt_neox'

  def __init__( self			:  int				,				lowerCamelCase__			:  int=50_432				,				lowerCamelCase__			:  Optional[int]=6_144				,				lowerCamelCase__			:  Optional[Any]=44				,				lowerCamelCase__			:  Tuple=64				,				lowerCamelCase__			:  Union[str, Any]=24_576				,				lowerCamelCase__			:  List[str]="gelu"				,				lowerCamelCase__			:  Union[str, Any]=0.25				,				lowerCamelCase__			:  Optional[int]=10_000				,				lowerCamelCase__			:  Union[str, Any]=0.0				,				lowerCamelCase__			:  List[Any]=0.0				,				lowerCamelCase__			:  int=0.1				,				lowerCamelCase__			:  Tuple=2_048				,				lowerCamelCase__			:  str=0.02				,				lowerCamelCase__			:  Any=1E-5				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  Optional[Any]=0				,				lowerCamelCase__			:  Tuple=2				,				lowerCamelCase__			:  Optional[Any]=False				,				lowerCamelCase__			:  str=True				,				lowerCamelCase__			:  Optional[int]=None				,				**lowerCamelCase__			:  Tuple				,				):
        super().__init__(bos_token_id=lowerCamelCase__				,				eos_token_id=lowerCamelCase__				,				**lowerCamelCase__       )
        a__	:  int     =   vocab_size
        a__	:  str     =   max_position_embeddings
        a__	:  Optional[int]     =   hidden_size
        a__	:  List[str]     =   num_hidden_layers
        a__	:  Tuple     =   num_attention_heads
        a__	:  int     =   intermediate_size
        a__	:  Optional[int]     =   hidden_act
        a__	:  str     =   rotary_pct
        a__	:  Optional[Any]     =   rotary_emb_base
        a__	:  Any     =   attention_dropout
        a__	:  Dict     =   hidden_dropout
        a__	:  Optional[int]     =   classifier_dropout
        a__	:  Dict     =   initializer_range
        a__	:  int     =   layer_norm_eps
        a__	:  Tuple     =   use_cache
        a__	:  Optional[Any]     =   tie_word_embeddings
        a__	:  List[str]     =   use_parallel_residual
        a__	:  str     =   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    _UpperCamelCase( self			:  Optional[Any]       ):
        if self.rope_scaling is None:
              return

        if not isinstance(self.rope_scaling				,				lowerCamelCase__       ) 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__	:  Dict     =   self.rope_scaling.get("type"				,				lowerCamelCase__       )
        a__	:  List[str]     =   self.rope_scaling.get("factor"				,				lowerCamelCase__       )
        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(lowerCamelCase__				,				lowerCamelCase__       ) or rope_scaling_factor <= 1.0:
              raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''       )



import copy

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


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      'encoder-decoder'
	a     :Any       	=      True
	def __init__(			self    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					assert (
					    "encoder" in kwargs and "decoder" in kwargs
					), "Config has to be initialized with encoder and decoder config"
					lowercase_	    =   kwargs.pop('''encoder'''			)
					lowercase_	    =   encoder_config.pop('''model_type'''			)
					lowercase_	    =   kwargs.pop('''decoder'''			)
					lowercase_	    =   decoder_config.pop('''model_type'''			)

					from ..auto.configuration_auto import AutoConfig

					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   True
	@classmethod
	def 		_lowercase    (			cls    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	PretrainedConfig:
					logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
					lowercase_	    =   True
					lowercase_	    =   True

					return cls(encoder=encoder_config.to_dict()			,				decoder=decoder_config.to_dict()			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   copy.deepcopy(self.__dict__			)
					lowercase_	    =   self.encoder.to_dict()
					lowercase_	    =   self.decoder.to_dict()
					lowercase_	    =   self.__class__.model_type
					return output




