code: 
  
  
  """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
  
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  UpperCAmelCase   =	{
      '''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   lowercase__							(				lowercase__							):
     __UpperCAmelCase       					=				"""layoutlm"""
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=3_0522			,      SCREAMING_SNAKE_CASE=768			,      SCREAMING_SNAKE_CASE=12			,      SCREAMING_SNAKE_CASE=12			,      SCREAMING_SNAKE_CASE=3072			,      SCREAMING_SNAKE_CASE="gelu"			,      SCREAMING_SNAKE_CASE=0.1			,      SCREAMING_SNAKE_CASE=0.1			,      SCREAMING_SNAKE_CASE=512			,      SCREAMING_SNAKE_CASE=2			,      SCREAMING_SNAKE_CASE=0.02			,      SCREAMING_SNAKE_CASE=1e-1_2			,      SCREAMING_SNAKE_CASE=0			,      SCREAMING_SNAKE_CASE="absolute"			,      SCREAMING_SNAKE_CASE=True			,      SCREAMING_SNAKE_CASE=1024			,      **SCREAMING_SNAKE_CASE			,      )     ->     Optional[int]:
        super().__init__(pad_token_id=_A			,      **_A)
        _lowerCamelCase  :				str      =		vocab_size
        _lowerCamelCase  :				Optional[int]      =		hidden_size
        _lowerCamelCase  :				Optional[Any]      =		num_hidden_layers
        _lowerCamelCase  :				List[Any]      =		num_attention_heads
        _lowerCamelCase  :				Any      =		hidden_act
        _lowerCamelCase  :				List[str]      =		intermediate_size
        _lowerCamelCase  :				Optional[int]      =		hidden_dropout_prob
        _lowerCamelCase  :				Optional[int]      =		attention_probs_dropout_prob
        _lowerCamelCase  :				Union[str, Any]      =		max_position_embeddings
        _lowerCamelCase  :				Union[str, Any]      =		type_vocab_size
        _lowerCamelCase  :				Dict      =		initializer_range
        _lowerCamelCase  :				List[Any]      =		layer_norm_eps
        _lowerCamelCase  :				Tuple      =		position_embedding_type
        _lowerCamelCase  :				Union[str, Any]      =		use_cache
        _lowerCamelCase  :				List[str]      =		max_ad_position_embeddings
  
  
  
  
  
  
  
  class   lowercase__							(				lowercase__							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = "default"			,      SCREAMING_SNAKE_CASE = None			,      )     ->     List[Any]:
        super().__init__(_A			,      task=_A			,      patching_specs=_A)
        _lowerCamelCase  :				int      =		config.max_ad_position_embeddings - 1
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       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 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = -1			,      SCREAMING_SNAKE_CASE = -1			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = None			,      )     ->     Mapping[str, Any]:
        _lowerCamelCase  :				List[str]      =		super().generate_dummy_inputs(
            _A			,      batch_size=_A			,      seq_length=_A			,      is_pair=_A			,      framework=_A)
  
        # Generate a dummy bbox
        _lowerCamelCase  :				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
  
        _lowerCamelCase  :				Dict      =		input_dict['input_ids'].shape
        _lowerCamelCase  :				List[Any]      =		torch.tensor([*[box] * seq_length]).tile(_A			,      1			,      1)
        return input_dict
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  from itertools import permutations
  
  def 							_snake_case			(      __snake_case       :			int = 5      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if gon_side < 3 or gon_side > 5:
        raise ValueError("""gon_side must be in the range [3, 5]"""      )
  
     # Since it's 16, we know 10 is on the outer ring
     # Put the big numbers at the end so that they are never the first number
     _lowerCamelCase  :				Dict      =		list(range(gon_side + 1	,   0	,   -1      )      )
     _lowerCamelCase  :				Optional[int]      =		list(range(gon_side + 2	,   gon_side * 2 + 1      )      )
  
     for perm in permutations(small_numbers + big_numbers      ):
        _lowerCamelCase  :				List[str]      =		generate_gon_ring(__snake_case	,   list(__snake_case      )      )
        if is_magic_gon(__snake_case      ):
           return int("""""".join(str(__snake_case      ) for n in numbers      )      )
  
     _lowerCamelCase  :				Union[str, Any]      =		F'Magic {gon_side}-gon ring is impossible'
     raise ValueError(__snake_case      )
  
  def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Union[str, Any]      =		[0] * (gon_side * 3)
     _lowerCamelCase  :				Dict      =		perm[0:3]
     perm.append(perm[1]      )
  
     _lowerCamelCase  :				Tuple      =		1 if gon_side < 5 else 2
  
     for i in range(1	,   len(__snake_case      ) // 3 + magic_number      ):
        _lowerCamelCase  :				Dict      =		perm[2 * i + 1]
        _lowerCamelCase  :				str      =		result[3 * i - 1]
        _lowerCamelCase  :				Dict      =		perm[2 * i + 2]
  
     return result
  
  def 							_snake_case			(      __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if len(__snake_case      ) % 3 != 0:
        raise ValueError("""a gon ring should have a length that is a multiple of 3"""      )
  
     if min(numbers[::3]      ) != numbers[0]:
        return False
  
     _lowerCamelCase  :				str      =		sum(numbers[:3]      )
  
     return all(sum(numbers[i : i + 3]      ) == total for i in range(3	,   len(__snake_case      )	,   3      )      )
  
  
  if __name__ == "__main__":
   print(solution())
  
  