


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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

snake_case    :   Dict	    =					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'data2vec-vision'




 def __init__(		self ,  _lowerCamelCase=768 ,  _lowerCamelCase=12 ,  _lowerCamelCase=12 ,  _lowerCamelCase=3072 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=224 ,  _lowerCamelCase=16 ,  _lowerCamelCase=3 ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=True ,  _lowerCamelCase=[3, 5, 7, 11] ,  _lowerCamelCase=[1, 2, 3, 6] ,  _lowerCamelCase=True ,  _lowerCamelCase=0.4 ,  _lowerCamelCase=256 ,  _lowerCamelCase=1 ,  _lowerCamelCase=False ,  _lowerCamelCase=255 ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )

       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Optional[int]										=	num_attention_heads
       a						:Dict										=	intermediate_size
       a						:List[Any]										=	hidden_act
       a						:List[str]										=	hidden_dropout_prob
       a						:Union[str, Any]										=	attention_probs_dropout_prob
       a						:Any										=	initializer_range
       a						:Any										=	layer_norm_eps

       a						:Union[str, Any]										=	image_size
       a						:int										=	patch_size
       a						:Optional[int]										=	num_channels
       a						:Union[str, Any]										=	use_mask_token
       a						:Optional[Any]										=	use_absolute_position_embeddings
       a						:Tuple										=	use_relative_position_bias
       a						:List[Any]										=	use_shared_relative_position_bias
       a						:Dict										=	layer_scale_init_value
       a						:Optional[int]										=	drop_path_rate
       a						:List[str]										=	use_mean_pooling
       # decode head attributes (semantic segmentation)
       a						:str										=	out_indices
       a						:Tuple										=	pool_scales
       # auxiliary head attributes (semantic segmentation)
       a						:List[Any]										=	use_auxiliary_head
       a						:List[Any]										=	auxiliary_loss_weight
       a						:Optional[int]										=	auxiliary_channels
       a						:List[str]										=	auxiliary_num_convs
       a						:str										=	auxiliary_concat_input
       a						:Union[str, Any]										=	semantic_loss_ignore_index







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	version.parse('1.11'							)




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return 1e-4





