


'''simple docstring'''







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


_SCREAMING_SNAKE_CASE							: Dict										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							: int										=			{
    '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 		_snake_case     (					__SCREAMING_SNAKE_CASE		):






						'''simple docstring'''







						__snake_case				=   "visual_bert"







						def __init__(	self:						int   ,				__UpperCamelCase:						Dict=3_0522   ,				__UpperCamelCase:						Union[str, Any]=768   ,				__UpperCamelCase:						List[str]=512   ,				__UpperCamelCase:						Optional[int]=12   ,				__UpperCamelCase:						List[str]=12   ,				__UpperCamelCase:						Tuple=3072   ,				__UpperCamelCase:						Union[str, Any]="gelu"   ,				__UpperCamelCase:						Tuple=0.1   ,				__UpperCamelCase:						str=0.1   ,				__UpperCamelCase:						List[Any]=512   ,				__UpperCamelCase:						Any=2   ,				__UpperCamelCase:						Optional[int]=0.0_2   ,				__UpperCamelCase:						Union[str, Any]=1E-12   ,				__UpperCamelCase:						List[str]=False   ,				__UpperCamelCase:						int=True   ,				__UpperCamelCase:						str=1   ,				__UpperCamelCase:						Tuple=0   ,				__UpperCamelCase:						Any=2   ,				**__UpperCamelCase:						List[str]   ,				)				->						str:
										super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE   ,				bos_token_id=_SCREAMING_SNAKE_CASE   ,				eos_token_id=_SCREAMING_SNAKE_CASE   ,				**_SCREAMING_SNAKE_CASE  )

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




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:  int =       {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       ['PLBartTokenizer']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  List[str] =       [
	    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'PLBartForCausalLM',
	    'PLBartForConditionalGeneration',
	    'PLBartForSequenceClassification',
	    'PLBartModel',
	    'PLBartPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	__snake_case				:  Any =       _LazyModule(__name__, globals()['__file__'], _import_structure)