

'''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				:   List[str]      				=  logging.get_logger(__name__)

lowerCamelCase				:   Union[str, 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 							A__					( A__						):
  A__					=       'layoutlm'





  def __init__(	self   :			str							,      _a   :			Optional[int]=3_0522							,      _a   :			Optional[int]=768							,      _a   :			Optional[int]=12							,      _a   :			List[str]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Any="gelu"							,      _a   :			List[Any]=0.1							,      _a   :			Optional[int]=0.1							,      _a   :			Dict=512							,      _a   :			Any=2							,      _a   :			Optional[Any]=0.02							,      _a   :			Union[str, Any]=1e-12							,      _a   :			int=0							,      _a   :			List[str]="absolute"							,      _a   :			List[str]=True							,      _a   :			List[str]=1024							,      **_a   :			int							,      )			->					Tuple:





       '''simple docstring'''






       super().__init__(pad_token_id=_a							,      **_a				)
       _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 							A__					( A__						):





  def __init__(	self   :			Optional[int]							,      _a   :			PretrainedConfig							,      _a   :			str = "default"							,      _a   :			List[PatchingSpec] = None							,      )			->					Tuple:





       '''simple docstring'''






       super().__init__(_a							,      task=_a							,      patching_specs=_a				)
       _SCREAMING_SNAKE_CASE												=config.max_ad_position_embeddings - 1





  @property
  def 		A						(	self   :			Union[str, Any]				)			->					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   :			str							,      _a   :			PreTrainedTokenizer							,      _a   :			int = -1							,      _a   :			int = -1							,      _a   :			bool = False							,      _a   :			Optional[TensorType] = None							,      )			->					Mapping[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=super().generate_dummy_inputs(
           _a							,      batch_size=_a							,      seq_length=_a							,      is_pair=_a							,      framework=_a				)

       # 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(_a							,      1							,      1				)
       return input_dict



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  list       )   ->    list:
     """simple docstring"""




     for i, _ in enumerate(_UpperCamelCase       ):
          if (i % 2 == 1) == (nums[i - 1] > nums[i]):
               _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=nums[i], nums[i - 1]

     return nums


if __name__ == "__main__":
     print("Enter the array elements:")
     lowerCamelCase				:   Tuple      				=  list(map(int, input().split()))
     print("The unsorted array is:")
     print(array)
     print("Array after Wiggle sort:")
     print(wiggle_sort(array))
