'''simple docstring'''

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


__a: Dict          =   logging.get_logger(__name__)

__a: List[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 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "gpt_neox"

       def __init__(						self	,   __lowerCAmelCase=50432	,   __lowerCAmelCase=6144	,   __lowerCAmelCase=44	,   __lowerCAmelCase=64	,   __lowerCAmelCase=24576	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.2_5	,   __lowerCAmelCase=10000	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=2048	,   __lowerCAmelCase=0.0_2	,   __lowerCAmelCase=1E-5	,   __lowerCAmelCase=True	,   __lowerCAmelCase=0	,   __lowerCAmelCase=2	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   __lowerCAmelCase=None	,   **__lowerCAmelCase	,   )  ->  Optional[Any]:
        super().__init__(bos_token_id=__lowerCAmelCase	,   eos_token_id=__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Dict  =					vocab_size
        lowercase__				:    List[Any]  =					max_position_embeddings
        lowercase__				:    Any  =					hidden_size
        lowercase__				:    Any  =					num_hidden_layers
        lowercase__				:    Optional[Any]  =					num_attention_heads
        lowercase__				:    List[str]  =					intermediate_size
        lowercase__				:    Optional[int]  =					hidden_act
        lowercase__				:    int  =					rotary_pct
        lowercase__				:    Optional[int]  =					rotary_emb_base
        lowercase__				:    str  =					attention_dropout
        lowercase__				:    Tuple  =					hidden_dropout
        lowercase__				:    Any  =					classifier_dropout
        lowercase__				:    Any  =					initializer_range
        lowercase__				:    Any  =					layer_norm_eps
        lowercase__				:    List[str]  =					use_cache
        lowercase__				:    Union[str, Any]  =					tie_word_embeddings
        lowercase__				:    List[Any]  =					use_parallel_residual
        lowercase__				:    Optional[int]  =					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]:
        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}""" )
        lowercase__				:    str  =					self.rope_scaling.get('''type'''	,   __lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					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}""" )


'''simple docstring'''

import copy

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


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

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

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

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


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

