


'''simple docstring'''



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__lowercase			:		int =			logging.get_logger(__name__)

__lowercase			:		List[Any] =			{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}





class 							__UpperCamelCase       (	lowercase__      ):
	A_			    =					"""layoutlm"""




	def __init__(     self		,       __a=3_0522		,       __a=768		,       __a=12		,       __a=12		,       __a=3072		,       __a="gelu"		,       __a=0.1		,       __a=0.1		,       __a=512		,       __a=2		,       __a=0.02		,       __a=1E-1_2		,       __a=0		,       __a="absolute"		,       __a=True		,       __a=1024		,       **__a		,       ):

						'''simple docstring'''





						super().__init__(pad_token_id=_A		,       **_A							)
						__a    : str      					=						vocab_size
						__a    : Optional[int]      					=						hidden_size
						__a    : Optional[Any]      					=						num_hidden_layers
						__a    : List[Any]      					=						num_attention_heads
						__a    : Any      					=						hidden_act
						__a    : List[str]      					=						intermediate_size
						__a    : Optional[int]      					=						hidden_dropout_prob
						__a    : Optional[int]      					=						attention_probs_dropout_prob
						__a    : Union[str, Any]      					=						max_position_embeddings
						__a    : Union[str, Any]      					=						type_vocab_size
						__a    : Dict      					=						initializer_range
						__a    : List[Any]      					=						layer_norm_eps
						__a    : Tuple      					=						position_embedding_type
						__a    : Union[str, Any]      					=						use_cache
						__a    : List[str]      					=						max_ad_position_embeddings





class 							__UpperCamelCase       (	lowercase__      ):




	def __init__(     self		,       __a		,       __a = "default"		,       __a = None		,       ):

						'''simple docstring'''





						super().__init__(_A		,       task=_A		,       patching_specs=_A							)
						__a    : int      					=						config.max_ad_position_embeddings - 1




	@property
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						return OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'sequence'}),
						        ('bbox', {0: 'batch', 1: 'sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
						        ('token_type_ids', {0: 'batch', 1: 'sequence'}),
						    ]							)





	def    __UpperCAmelCase							(     self		,       __a		,       __a = -1		,       __a = -1		,       __a = False		,       __a = None		,       ):

						'''simple docstring'''





						__a    : List[str]      					=						super().generate_dummy_inputs(
						    _A		,       batch_size=_A		,       seq_length=_A		,       is_pair=_A		,       framework=_A							)

						# Generate a dummy bbox
						__a    : Tuple      					=						[48, 84, 73, 128]

						if not framework == TensorType.PYTORCH:
											raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'							)

						if not is_torch_available():
											raise ValueError('Cannot generate dummy inputs without PyTorch installed.'							)
						import torch

						__a    : Dict      					=						input_dict['input_ids'].shape
						__a    : List[Any]      					=						torch.tensor([*[box] * seq_length]							).tile(_A		,       1		,       1							)
						return input_dict







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase			:		Optional[int] =			[
	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VisualBertForMultipleChoice',
	    'VisualBertForPreTraining',
	    'VisualBertForQuestionAnswering',
	    'VisualBertForRegionToPhraseAlignment',
	    'VisualBertForVisualReasoning',
	    'VisualBertLayer',
	    'VisualBertModel',
	    'VisualBertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_visual_bert import (
		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VisualBertForMultipleChoice,
		    VisualBertForPreTraining,
		    VisualBertForQuestionAnswering,
		    VisualBertForRegionToPhraseAlignment,
		    VisualBertForVisualReasoning,
		    VisualBertLayer,
		    VisualBertModel,
		    VisualBertPreTrainedModel,
		)


else:
	import sys

	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



