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


lowercase						:      Any									= logging.get_logger(__name__)

lowercase						:      List[Any]									= {
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'markuplm'







	def __init__(      self	:Optional[int]  ,     a	:Union[str, Any]=3_0_5_2_2  ,     a	:Optional[Any]=7_6_8  ,     a	:Tuple=1_2  ,     a	:List[Any]=1_2  ,     a	:Tuple=3_0_7_2  ,     a	:Union[str, Any]="gelu"  ,     a	:str=0.1  ,     a	:List[str]=0.1  ,     a	:Optional[int]=5_1_2  ,     a	:List[Any]=2  ,     a	:List[Any]=0.02  ,     a	:List[str]=1E-1_2  ,     a	:Any=0  ,     a	:str=0  ,     a	:Dict=2  ,     a	:List[Any]=2_5_6  ,     a	:str=1_0_2_4  ,     a	:Any=2_1_6  ,     a	:List[str]=1_0_0_1  ,     a	:Optional[Any]=3_2  ,     a	:int=5_0  ,     a	:str="absolute"  ,     a	:int=True  ,     a	:Any=None  ,     **a	:Union[str, Any]  ,     )	->				Union[str, Any]:
								super().__init__(
								    pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     **a  ,     )
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     Optional[int]         =      hidden_size
								__UpperCamelCase			:     Optional[int]         =      num_hidden_layers
								__UpperCamelCase			:     List[str]         =      num_attention_heads
								__UpperCamelCase			:     int         =      hidden_act
								__UpperCamelCase			:     Union[str, Any]         =      intermediate_size
								__UpperCamelCase			:     int         =      hidden_dropout_prob
								__UpperCamelCase			:     int         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Optional[int]         =      max_position_embeddings
								__UpperCamelCase			:     str         =      type_vocab_size
								__UpperCamelCase			:     List[str]         =      initializer_range
								__UpperCamelCase			:     Optional[int]         =      layer_norm_eps
								__UpperCamelCase			:     List[str]         =      position_embedding_type
								__UpperCamelCase			:     int         =      use_cache
								__UpperCamelCase			:     List[str]         =      classifier_dropout
								# additional properties
								__UpperCamelCase			:     int         =      max_depth
								__UpperCamelCase			:     Tuple         =      max_xpath_tag_unit_embeddings
								__UpperCamelCase			:     Dict         =      max_xpath_subs_unit_embeddings
								__UpperCamelCase			:     Tuple         =      tag_pad_id
								__UpperCamelCase			:     List[str]         =      subs_pad_id
								__UpperCamelCase			:     str         =      xpath_unit_hidden_size

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Tuple									= {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Union[str, Any]									= [
		    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Swinv2ForImageClassification',
		    'Swinv2ForMaskedImageModeling',
		    'Swinv2Model',
		    'Swinv2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swinva import (
				    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinvaForImageClassification,
				    SwinvaForMaskedImageModeling,
				    SwinvaModel,
				    SwinvaPreTrainedModel,
				)


else:
		import sys

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