"""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


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "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__(      __A			):
						lowerCAmelCase__	:    List[str]    				=		'layoutlm'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.1		,__UpperCAmelCase=5_12		,__UpperCAmelCase=2		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=0		,__UpperCAmelCase="absolute"		,__UpperCAmelCase=True		,__UpperCAmelCase=10_24		,**__UpperCAmelCase		,)       ->					Any:
													super().__init__(pad_token_id=__UpperCAmelCase		,**__UpperCAmelCase  )
													A__						  =				vocab_size
													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				hidden_act
													A__						  =				intermediate_size
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				max_position_embeddings
													A__						  =				type_vocab_size
													A__						  =				initializer_range
													A__						  =				layer_norm_eps
													A__						  =				position_embedding_type
													A__						  =				use_cache
													A__						  =				max_ad_position_embeddings






class      UpperCamelCase__(      __A			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase = "default"		,__UpperCAmelCase = None		,)       ->					Optional[int]:
													super().__init__(__UpperCAmelCase		,task=__UpperCAmelCase		,patching_specs=__UpperCAmelCase  )
													A__						  =				config.max_ad_position_embeddings - 1





						@property
						def 					snake_case__						(							self  )       ->					Mapping[str, Mapping[int, str]]:
													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 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase = -1		,__UpperCAmelCase = -1		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,)       ->					Mapping[str, Any]:
													A__						  =				super().generate_dummy_inputs(
													    __UpperCAmelCase		,batch_size=__UpperCAmelCase		,seq_length=__UpperCAmelCase		,is_pair=__UpperCAmelCase		,framework=__UpperCAmelCase  )

													# Generate a dummy bbox
													A__						  =				[48, 84, 73, 1_28]

													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__  ,	A__						  =				input_dict['input_ids'].shape
													A__						  =				torch.tensor([*[box] * seq_length]  ).tile(__UpperCAmelCase		,1		,1  )
													return input_dict






"""simple docstring"""




def 	UpperCAmelCase       (						UpperCamelCase__ = 100							,     UpperCamelCase__ = 100					):






							"""simple docstring"""





							return max(
							    sum(int(UpperCamelCase__					) for x in str(base**power					)					)
							    for base in range(UpperCamelCase__					)
							    for power in range(UpperCamelCase__					)					)


# Tests
if __name__ == "__main__":
				import doctest

				doctest.testmod()





