code: 
  
  
  
  
  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 :		Optional[Any]      							=    logging.get_logger(__name__)
  
  __A :		Optional[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 							lowerCamelCase		( UpperCAmelCase_				):
     lowercase       :  List[str]      =				'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-12    ,      SCREAMING_SNAKE_CASE_=0    ,      SCREAMING_SNAKE_CASE_="absolute"    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=1024    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							vocab_size
         UpperCamelCase				:   Tuple          =							hidden_size
         UpperCamelCase				:   Dict          =							num_hidden_layers
         UpperCamelCase				:   List[str]          =							num_attention_heads
         UpperCamelCase				:   Optional[int]          =							hidden_act
         UpperCamelCase				:   Optional[Any]          =							intermediate_size
         UpperCamelCase				:   List[Any]          =							hidden_dropout_prob
         UpperCamelCase				:   Any          =							attention_probs_dropout_prob
         UpperCamelCase				:   List[str]          =							max_position_embeddings
         UpperCamelCase				:   Optional[Any]          =							type_vocab_size
         UpperCamelCase				:   List[str]          =							initializer_range
         UpperCamelCase				:   Dict          =							layer_norm_eps
         UpperCamelCase				:   Any          =							position_embedding_type
         UpperCamelCase				:   Union[str, Any]          =							use_cache
         UpperCamelCase				:   Tuple          =							max_ad_position_embeddings
  
  
  
  
  
  
  
  class 							lowerCamelCase		( UpperCAmelCase_				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "default"    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         super().__init__(SCREAMING_SNAKE_CASE_    ,      task=SCREAMING_SNAKE_CASE_    ,      patching_specs=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							config.max_ad_position_embeddings - 1
  
  
  
  
  
  
  
     @property
     def       a_							(							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       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = -1    ,      SCREAMING_SNAKE_CASE_ = -1    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         UpperCamelCase				:   Optional[int]          =							super().generate_dummy_inputs(
             SCREAMING_SNAKE_CASE_    ,      batch_size=SCREAMING_SNAKE_CASE_    ,      seq_length=SCREAMING_SNAKE_CASE_    ,      is_pair=SCREAMING_SNAKE_CASE_    ,      framework=SCREAMING_SNAKE_CASE_ )
  
         # Generate a dummy bbox
         UpperCamelCase				:   Optional[int]          =							[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       ,							UpperCamelCase				:   Optional[Any]          =							input_dict["""input_ids"""].shape
         UpperCamelCase				:   Union[str, Any]          =							torch.tensor([*[box] * seq_length] ).tile(SCREAMING_SNAKE_CASE_    ,      1    ,      1 )
         return input_dict
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							min(img.size    )
  
      if s < target_image_size:
          raise ValueError(f'min dim for image {s} < {target_image_size}'    )
  
      UpperCamelCase				:   Dict          =							target_image_size / s
      UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
      UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
      UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
      UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
      return img
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							2.0 * x - 1.0
      return x
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							x.detach().cpu()
      if process:
          UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
      UpperCamelCase				:   List[Any]          =							x.numpy()
      if process:
          UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
      UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
      if not x.mode == mode:
          UpperCamelCase				:   Any          =							x.convert(snake_case_    )
      return x
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
      UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
      UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
      return x
  
  
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
      return x.permute(2						,0						,1    ).unsqueeze(0    )
  
  
  
  
  