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   a_  (_a    ):
         __lowerCAmelCase    :				Dict    =      """layoutlm"""
  
  
  
  
         def __init__(     self   ,   snake_case_=3_0_5_2_2   ,   snake_case_=7_6_8   ,   snake_case_=1_2   ,   snake_case_=1_2   ,   snake_case_=3_0_7_2   ,   snake_case_="gelu"   ,   snake_case_=0.1   ,   snake_case_=0.1   ,   snake_case_=5_1_2   ,   snake_case_=2   ,   snake_case_=0.02   ,   snake_case_=1E-12   ,   snake_case_=0   ,   snake_case_="absolute"   ,   snake_case_=True   ,   snake_case_=1_0_2_4   ,   **snake_case_   ,   ):
           super().__init__(pad_token_id=snake_case_   ,   **snake_case_  )
           _lowerCAmelCase       :  Tuple   				=					vocab_size
           _lowerCAmelCase       :  int   				=					hidden_size
           _lowerCAmelCase       :  Any   				=					num_hidden_layers
           _lowerCAmelCase       :  str   				=					num_attention_heads
           _lowerCAmelCase       :  int   				=					hidden_act
           _lowerCAmelCase       :  Dict   				=					intermediate_size
           _lowerCAmelCase       :  Tuple   				=					hidden_dropout_prob
           _lowerCAmelCase       :  Union[str, Any]   				=					attention_probs_dropout_prob
           _lowerCAmelCase       :  str   				=					max_position_embeddings
           _lowerCAmelCase       :  Dict   				=					type_vocab_size
           _lowerCAmelCase       :  List[Any]   				=					initializer_range
           _lowerCAmelCase       :  str   				=					layer_norm_eps
           _lowerCAmelCase       :  Dict   				=					position_embedding_type
           _lowerCAmelCase       :  Any   				=					use_cache
           _lowerCAmelCase       :  str   				=					max_ad_position_embeddings
  
  
  
  class   a_  (_a    ):
  
  
  
  
         def __init__(     self   ,   snake_case_   ,   snake_case_ = "default"   ,   snake_case_ = None   ,   ):
           super().__init__(snake_case_   ,   task=snake_case_   ,   patching_specs=snake_case_  )
           _lowerCAmelCase       :  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   ,   snake_case_   ,   snake_case_ = -1   ,   snake_case_ = -1   ,   snake_case_ = False   ,   snake_case_ = None   ,   ):
           _lowerCAmelCase       :  Optional[Any]   				=					super().generate_dummy_inputs(
               snake_case_   ,   batch_size=snake_case_   ,   seq_length=snake_case_   ,   is_pair=snake_case_   ,   framework=snake_case_  )
  
           # Generate a dummy bbox
           _lowerCAmelCase       :  List[Any]   				=					[4_8, 8_4, 7_3, 1_2_8]
  
           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       :  List[str]   				=					input_dict["""input_ids"""].shape
           _lowerCAmelCase       :  Tuple   				=					torch.tensor([*[box] * seq_length]  ).tile(snake_case_   ,   1   ,   1  )
           return input_dict
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int		)    ->						float:
    if common_ratio == 1:
      # Formula for sum if common ratio is 1
      return num_of_terms * first_term
  
    # Formula for finding sum of n terms of a GeometricProgression
    return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)
  
  
  