






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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    str       						=  '''visual_bert'''



		def __init__(							self				,		_UpperCamelCase=3_0_5_2_2				,		_UpperCamelCase=7_6_8				,		_UpperCamelCase=5_1_2				,		_UpperCamelCase=1_2				,		_UpperCamelCase=1_2				,		_UpperCamelCase=3_0_7_2				,		_UpperCamelCase="gelu"				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.1				,		_UpperCamelCase=5_1_2				,		_UpperCamelCase=2				,		_UpperCamelCase=0.02				,		_UpperCamelCase=1E-12				,		_UpperCamelCase=False				,		_UpperCamelCase=True				,		_UpperCamelCase=1				,		_UpperCamelCase=0				,		_UpperCamelCase=2				,		**_UpperCamelCase				,		)	->							List[Any]:
							super().__init__(pad_token_id=_UpperCamelCase				,		bos_token_id=_UpperCamelCase				,		eos_token_id=_UpperCamelCase				,		**_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						vocab_size
							UpperCAmelCase_    :      List[Any]            =						max_position_embeddings
							UpperCAmelCase_    :      Dict            =						hidden_size
							UpperCAmelCase_    :      Any            =						visual_embedding_dim
							UpperCAmelCase_    :      Any            =						num_hidden_layers
							UpperCAmelCase_    :      str            =						num_attention_heads
							UpperCAmelCase_    :      int            =						intermediate_size
							UpperCAmelCase_    :      Tuple            =						hidden_act
							UpperCAmelCase_    :      Any            =						hidden_dropout_prob
							UpperCAmelCase_    :      Optional[Any]            =						attention_probs_dropout_prob
							UpperCAmelCase_    :      Any            =						initializer_range
							UpperCAmelCase_    :      Union[str, Any]            =						type_vocab_size
							UpperCAmelCase_    :      List[Any]            =						layer_norm_eps
							UpperCAmelCase_    :      Any            =						bypass_transformer
							UpperCAmelCase_    :      Union[str, Any]            =						special_visual_initialize













'''simple docstring'''







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


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    int      	=			{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='gpt_bigcode'
				__a            =['past_key_values']
				__a            ={
				    'hidden_size': 'n_embd',
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(  self     :					Optional[Any]					,				__a     :					Tuple=5_02_57					,				__a     :					str=10_24					,				__a     :					Dict=7_68					,				__a     :					Tuple=12					,				__a     :					str=12					,				__a     :					Optional[int]=None					,				__a     :					Dict="gelu_pytorch_tanh"					,				__a     :					Tuple=0.1					,				__a     :					Tuple=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Tuple=1e-5					,				__a     :					str=0.02					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[int]=5_02_56					,				__a     :					Optional[int]=5_02_56					,				__a     :					Union[str, Any]=True					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				**__a     :					List[Any]					,				):
								_a								= vocab_size
								_a								= n_positions
								_a								= n_embd
								_a								= n_layer
								_a								= n_head
								_a								= n_inner
								_a								= activation_function
								_a								= resid_pdrop
								_a								= embd_pdrop
								_a								= attn_pdrop
								_a								= layer_norm_epsilon
								_a								= initializer_range
								_a								= scale_attn_weights
								_a								= use_cache
								_a								= attention_softmax_in_fpaa
								_a								= scale_attention_softmax_in_fpaa
								_a								= multi_query

								_a								= bos_token_id
								_a								= eos_token_id

								super().__init__(bos_token_id=__a					,				eos_token_id=__a					,				**__a					)
