



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    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      """layoutlm"""





     def __init__(							self			:				int  ,	UpperCamelCase			:				Optional[Any]=3_0_5_2_2  ,	UpperCamelCase			:				str=7_6_8  ,	UpperCamelCase			:				str=1_2  ,	UpperCamelCase			:				Optional[Any]=1_2  ,	UpperCamelCase			:				Dict=3_0_7_2  ,	UpperCamelCase			:				Tuple="gelu"  ,	UpperCamelCase			:				int=0.1  ,	UpperCamelCase			:				Union[str, Any]=0.1  ,	UpperCamelCase			:				List[Any]=5_1_2  ,	UpperCamelCase			:				List[str]=2  ,	UpperCamelCase			:				Optional[Any]=0.0_2  ,	UpperCamelCase			:				int=1E-12  ,	UpperCamelCase			:				str=0  ,	UpperCamelCase			:				Optional[int]="absolute"  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				List[Any]=1_0_2_4  ,	**UpperCamelCase			:				Dict  ,	)->int:
      super().__init__(pad_token_id=UpperCamelCase  ,	**UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						vocab_size
      __SCREAMING_SNAKE_CASE							:     Any							=						hidden_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						num_hidden_layers
      __SCREAMING_SNAKE_CASE							:     str							=						num_attention_heads
      __SCREAMING_SNAKE_CASE							:     int							=						hidden_act
      __SCREAMING_SNAKE_CASE							:     Any							=						intermediate_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Tuple							=						max_position_embeddings
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						type_vocab_size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     List[str]							=						layer_norm_eps
      __SCREAMING_SNAKE_CASE							:     int							=						position_embedding_type
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						use_cache
      __SCREAMING_SNAKE_CASE							:     Dict							=						max_ad_position_embeddings






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				PretrainedConfig  ,	UpperCamelCase			:				str = "default"  ,	UpperCamelCase			:				List[PatchingSpec] = None  ,	)->Optional[int]:
      super().__init__(UpperCamelCase  ,	task=UpperCamelCase  ,	patching_specs=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						config.max_ad_position_embeddings - 1





     @property
     def 						__snake_case     (							self			:				Union[str, Any]					)->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			:				Tuple  ,	UpperCamelCase			:				PreTrainedTokenizer  ,	UpperCamelCase			:				int = -1  ,	UpperCamelCase			:				int = -1  ,	UpperCamelCase			:				bool = False  ,	UpperCamelCase			:				Optional[TensorType] = None  ,	)->Mapping[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						super().generate_dummy_inputs(
          UpperCamelCase  ,	batch_size=UpperCamelCase  ,	seq_length=UpperCamelCase  ,	is_pair=UpperCamelCase  ,	framework=UpperCamelCase					)

      # Generate a dummy bbox
      __SCREAMING_SNAKE_CASE							:     List[str]							=						[4_8, 8_4, 7_3, 1_2_8]

      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							:     str							=						input_dict["input_ids"].shape
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.tensor([*[box] * seq_length]					).tile(UpperCamelCase  ,	1  ,	1					)
      return input_dict











'''simple docstring'''



import math



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if not isinstance(lowerCamelCase					,		lowerCamelCase       ):
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be an integer'''
		raise TypeError(lowerCamelCase       )
	if number < 1:
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be a positive integer'''
		raise ValueError(lowerCamelCase       )
	__lowerCAmelCase       				=	[number]
	while number != 1:
		if number % 2 == 0:
			__lowerCAmelCase       				=	math.floor(math.sqrt(lowerCamelCase       )       )
		else:
			__lowerCAmelCase       				=	math.floor(
			    math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       )       )
		sequence.append(lowerCamelCase       )
	return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()