






"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


__A		:       Any  				=		logging.getLogger(__name__)





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   "masked_bert"




					def __init__(				self     :						Optional[int]    ,						A     :						int=3_05_22    ,						A     :						int=7_68    ,						A     :						List[Any]=12    ,						A     :						Union[str, Any]=12    ,						A     :						List[str]=30_72    ,						A     :						Dict="gelu"    ,						A     :						Any=0.1    ,						A     :						int=0.1    ,						A     :						Optional[Any]=5_12    ,						A     :						Union[str, Any]=2    ,						A     :						Any=0.02    ,						A     :						str=1e-12    ,						A     :						Optional[int]=0    ,						A     :						Union[str, Any]="topK"    ,						A     :						Union[str, Any]="constant"    ,						A     :						Optional[int]=0.0    ,						**A     :						List[str]    ,						)  ->  int:
							super().__init__(pad_token_id=A    ,						**A   )

							lowercase_ : str							       =   vocab_size
							lowercase_ : List[str]							       =   hidden_size
							lowercase_ : List[Any]							       =   num_hidden_layers
							lowercase_ : int							       =   num_attention_heads
							lowercase_ : Union[str, Any]							       =   hidden_act
							lowercase_ : Any							       =   intermediate_size
							lowercase_ : Tuple							       =   hidden_dropout_prob
							lowercase_ : Optional[Any]							       =   attention_probs_dropout_prob
							lowercase_ : Tuple							       =   max_position_embeddings
							lowercase_ : Union[str, Any]							       =   type_vocab_size
							lowercase_ : Optional[int]							       =   initializer_range
							lowercase_ : List[Any]							       =   layer_norm_eps
							lowercase_ : Any							       =   pruning_method
							lowercase_ : Dict							       =   mask_init
							lowercase_ : Optional[Any]							       =   mask_scale











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A		:       List[str]  				=		{
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''MobileNetV2FeatureExtractor''']
						__A		:       Optional[int]  				=		['''MobileNetV2ImageProcessor''']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Optional[Any]  				=		[
						    '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileNetV2ForImageClassification''',
						    '''MobileNetV2ForSemanticSegmentation''',
						    '''MobileNetV2Model''',
						    '''MobileNetV2PreTrainedModel''',
						    '''load_tf_weights_in_mobilenet_v2''',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						__A		:       List[Any]  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



