


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


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

snake_case    :   Dict	    =					{
    '''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 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'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=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )
       a						:str										=	vocab_size
       a						:Any										=	max_position_embeddings
       a						:str										=	hidden_size
       a						:str										=	num_hidden_layers
       a						:Union[str, Any]										=	num_attention_heads
       a						:List[Any]										=	intermediate_size
       a						:int										=	hidden_act
       a						:int										=	rotary_pct
       a						:str										=	rotary_emb_base
       a						:Optional[int]										=	attention_dropout
       a						:str										=	hidden_dropout
       a						:int										=	classifier_dropout
       a						:Tuple										=	initializer_range
       a						:Any										=	layer_norm_eps
       a						:List[Any]										=	use_cache
       a						:Any										=	tie_word_embeddings
       a						:Union[str, Any]										=	use_parallel_residual
       a						:Dict										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       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						:Optional[Any]										=	self.rope_scaling.get('''type''' ,  _lowerCamelCase   )
       a						:Optional[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}'''   )









from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .multicontrolnet import MultiControlNetModel
     from .pipeline_controlnet import StableDiffusionControlNetPipeline
     from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
     from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
     from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline





