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
  
  
  __lowerCamelCase       :       int				   =			logging.get_logger(__name__)
  
  __lowerCamelCase       :       List[str]				   =			{
      "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      (      UpperCamelCase_   ):
  		__lowercase													=		"""layoutlm"""
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase=3_05_22	,     __UpperCamelCase=7_68	,     __UpperCamelCase=12	,     __UpperCamelCase=12	,     __UpperCamelCase=30_72	,     __UpperCamelCase="gelu"	,     __UpperCamelCase=0.1	,     __UpperCamelCase=0.1	,     __UpperCamelCase=5_12	,     __UpperCamelCase=2	,     __UpperCamelCase=0.02	,     __UpperCamelCase=1E-12	,     __UpperCamelCase=0	,     __UpperCamelCase="absolute"	,     __UpperCamelCase=True	,     __UpperCamelCase=10_24	,     **__UpperCamelCase	,     )->			Dict:
  						'''simple docstring'''
  
  
  						super().__init__(pad_token_id=__UpperCamelCase	,     **__UpperCamelCase							)
  						A__     :							int   				=vocab_size
  						A__     :							Dict   				=hidden_size
  						A__     :							Any   				=num_hidden_layers
  						A__     :							List[Any]   				=num_attention_heads
  						A__     :							Any   				=hidden_act
  						A__     :							List[str]   				=intermediate_size
  						A__     :							Union[str, Any]   				=hidden_dropout_prob
  						A__     :							List[Any]   				=attention_probs_dropout_prob
  						A__     :							List[str]   				=max_position_embeddings
  						A__     :							Union[str, Any]   				=type_vocab_size
  						A__     :							List[str]   				=initializer_range
  						A__     :							Dict   				=layer_norm_eps
  						A__     :							List[str]   				=position_embedding_type
  						A__     :							Union[str, Any]   				=use_cache
  						A__     :							Any   				=max_ad_position_embeddings
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase = "default"	,     __UpperCamelCase = None	,     )->			List[str]:
  						'''simple docstring'''
  
  
  						super().__init__(__UpperCamelCase	,     task=__UpperCamelCase	,     patching_specs=__UpperCamelCase							)
  						A__     :							Any   				=config.max_ad_position_embeddings - 1
  
  
  
  
  
  
  		@property
  		def       lowerCAmelCase_  (  self							)->			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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = -1	,     __UpperCamelCase = -1	,     __UpperCamelCase = False	,     __UpperCamelCase = None	,     )->			Mapping[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=super().generate_dummy_inputs(
  						    __UpperCamelCase	,     batch_size=__UpperCamelCase	,     seq_length=__UpperCamelCase	,     is_pair=__UpperCamelCase	,     framework=__UpperCamelCase							)
  
  						# Generate a dummy bbox
  						A__     :							Tuple   				=[48, 84, 73, 1_28]
  
  						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
  
  						A__				,  A__     :							Any   				=input_dict['''input_ids'''].shape
  						A__     :							List[str]   				=torch.tensor([*[box] * seq_length]							).tile(__UpperCamelCase	,     1	,     1							)
  						return input_dict
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		{
  		    """sld_warmup_steps""": 15,
  		    """sld_guidance_scale""": 20,
  		    """sld_threshold""": 0.0,
  		    """sld_momentum_scale""": 0.0,
  		    """sld_mom_beta""": 0.0,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 10,
  		    """sld_guidance_scale""": 1_000,
  		    """sld_threshold""": 0.01,
  		    """sld_momentum_scale""": 0.3,
  		    """sld_mom_beta""": 0.4,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 7,
  		    """sld_guidance_scale""": 2_000,
  		    """sld_threshold""": 0.0_25,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  		__lowercase													=		{
  		    """sld_warmup_steps""": 0,
  		    """sld_guidance_scale""": 5_000,
  		    """sld_threshold""": 1.0,
  		    """sld_momentum_scale""": 0.5,
  		    """sld_mom_beta""": 0.7,
  		}
  
  
  
  
  
  
  
  @dataclass
  class 			a      (      UpperCamelCase_   ):
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  		__lowercase													=		42
  
  
  if is_transformers_available() and is_torch_available():
  						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  						from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  