
"""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


snake_case    =		logging.get_logger(__name__)

snake_case    =		{
    '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	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Optional[int]						  =				"layoutlm"


				def __init__(				self		,	lowercase__=30522		,	lowercase__=768		,	lowercase__=12		,	lowercase__=12		,	lowercase__=3072		,	lowercase__="gelu"		,	lowercase__=0.1		,	lowercase__=0.1		,	lowercase__=512		,	lowercase__=2		,	lowercase__=0.02		,	lowercase__=1E-12		,	lowercase__=0		,	lowercase__="absolute"		,	lowercase__=True		,	lowercase__=1024		,	**lowercase__		,	)    ->    List[str]:
									"""simple docstring"""







									super().__init__(pad_token_id=lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	vocab_size
									SCREAMING_SNAKE_CASE				=	hidden_size
									SCREAMING_SNAKE_CASE				=	num_hidden_layers
									SCREAMING_SNAKE_CASE				=	num_attention_heads
									SCREAMING_SNAKE_CASE				=	hidden_act
									SCREAMING_SNAKE_CASE				=	intermediate_size
									SCREAMING_SNAKE_CASE				=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE				=	attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE				=	max_position_embeddings
									SCREAMING_SNAKE_CASE				=	type_vocab_size
									SCREAMING_SNAKE_CASE				=	initializer_range
									SCREAMING_SNAKE_CASE				=	layer_norm_eps
									SCREAMING_SNAKE_CASE				=	position_embedding_type
									SCREAMING_SNAKE_CASE				=	use_cache
									SCREAMING_SNAKE_CASE				=	max_ad_position_embeddings






class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__ = "default"		,	lowercase__ = None		,	)    ->    Union[str, Any]:
									"""simple docstring"""







									super().__init__(lowercase__		,	task=lowercase__		,	patching_specs=lowercase__			)
									SCREAMING_SNAKE_CASE				=	config.max_ad_position_embeddings - 1


				@property
				def 							A					(				self			)    ->    Mapping[str, Mapping[int, str]]:
									"""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 							A					(				self		,	lowercase__		,	lowercase__ = -1		,	lowercase__ = -1		,	lowercase__ = False		,	lowercase__ = None		,	)    ->    Mapping[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	super().generate_dummy_inputs(
									    lowercase__		,	batch_size=lowercase__		,	seq_length=lowercase__		,	is_pair=lowercase__		,	framework=lowercase__			)

									# Generate a dummy bbox
									SCREAMING_SNAKE_CASE				=	[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

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	input_dict['input_ids'].shape
									SCREAMING_SNAKE_CASE				=	torch.tensor([*[box] * seq_length]			).tile(lowercase__		,	1		,	1			)
									return input_dict


"""simple docstring"""




snake_case    =		1_0_0_0

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					return x * x

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = 4			):
					assert callable(
					    SCREAMING_SNAKE_CASE_			), f'''the function(object) passed should be callable your input : {function}'''
					assert isinstance(SCREAMING_SNAKE_CASE_,     (float, int)			), f'''a should be float or integer your input : {a}'''
					assert isinstance(function(SCREAMING_SNAKE_CASE_			),     (float, int)			), (
					    "the function should return integer or float return type of your function, "
					    f'''{type(SCREAMING_SNAKE_CASE_			)}'''
					)
					assert isinstance(SCREAMING_SNAKE_CASE_,     (float, int)			), f'''b should be float or integer your input : {b}'''
					assert (
					    isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) and precision > 0
					), f'''precision should be positive integer your input : {precision}'''

					# just applying the formula of simpson for approximate integration written in
					# mentioned article in first comment of this file and above this function

					SCREAMING_SNAKE_CASE				=	(b - a) / N_STEPS
					SCREAMING_SNAKE_CASE				=	function(SCREAMING_SNAKE_CASE_			) + function(SCREAMING_SNAKE_CASE_			)

					for i in range(1,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	a + h * i
										result += function(SCREAMING_SNAKE_CASE_			) * (4 if i % 2 else 2)

					result *= h / 3
					return round(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
