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
  
  
  _a	 =							logging.get_logger(__name__)
  
  _a	 =							{
      '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_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Any						  =	"""layoutlm"""
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_=3_0522       ,     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-1_2       ,     lowercase_=0       ,     lowercase_="absolute"       ,     lowercase_=True       ,     lowercase_=1024       ,     **lowercase_       ,     ):
       super().__init__(pad_token_id=lowercase_       ,     **lowercase_						)
       UpperCAmelCase_      :      Union[str, Any]      =					vocab_size
       UpperCAmelCase_      :      Optional[int]      =					hidden_size
       UpperCAmelCase_      :      Union[str, Any]      =					num_hidden_layers
       UpperCAmelCase_      :      int      =					num_attention_heads
       UpperCAmelCase_      :      Optional[int]      =					hidden_act
       UpperCAmelCase_      :      Union[str, Any]      =					intermediate_size
       UpperCAmelCase_      :      Union[str, Any]      =					hidden_dropout_prob
       UpperCAmelCase_      :      Tuple      =					attention_probs_dropout_prob
       UpperCAmelCase_      :      List[str]      =					max_position_embeddings
       UpperCAmelCase_      :      Optional[int]      =					type_vocab_size
       UpperCAmelCase_      :      Optional[int]      =					initializer_range
       UpperCAmelCase_      :      Dict      =					layer_norm_eps
       UpperCAmelCase_      :      Dict      =					position_embedding_type
       UpperCAmelCase_      :      Tuple      =					use_cache
       UpperCAmelCase_      :      Optional[int]      =					max_ad_position_embeddings
  
  
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_ = "default"       ,     lowercase_ = None       ,     ):
       super().__init__(lowercase_       ,     task=lowercase_       ,     patching_specs=lowercase_						)
       UpperCAmelCase_      :      Optional[Any]      =					config.max_ad_position_embeddings - 1
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
       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       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
       UpperCAmelCase_      :      Optional[Any]      =					super().generate_dummy_inputs(
           lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       # Generate a dummy bbox
       UpperCAmelCase_      :      Any      =					[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
  
       UpperCAmelCase_      :      Tuple      =					input_dict['''input_ids'''].shape
       UpperCAmelCase_      :      Optional[int]      =					torch.tensor([*[box] * seq_length]						).tile(lowercase_       ,     1       ,     1						)
       return input_dict
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from hashlib import mda, shaaaa
  
  _a	 =							(shaaaa, mda)
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_ = 8						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       UpperCAmelCase_      :      Union[str, Any]      =					size
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.hash_(lowercase_						)
       self.bitarray |= h
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					self.hash_(lowercase_						)
       return (h & self.bitarray) == h
  
  
  
  
  
  
  
    def __contains__(	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.exists(lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					bin(lowercase_						)[2:]
       return res.zfill(self.size						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return self.format_bin(self.bitarray						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       for func in HASH_FUNCTIONS:
          UpperCAmelCase_      :      str      =					(
              int.from_bytes(func(value.encode()						).digest()       ,     "little"						) % self.size
          )
          res |= 2**position
       return res
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.format_bin(self.hash_(lowercase_						)						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					bin(self.bitarray						).count("1"						)
       return (n_ones / self.size) ** len(lowercase_						)
  
  
  