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__							(				A_							):
     __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			,      )     ->     int:
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		vocab_size
        _lowerCamelCase  :				Any      =		hidden_size
        _lowerCamelCase  :				Optional[Any]      =		num_hidden_layers
        _lowerCamelCase  :				Tuple      =		num_attention_heads
        _lowerCamelCase  :				Tuple      =		hidden_act
        _lowerCamelCase  :				Tuple      =		intermediate_size
        _lowerCamelCase  :				int      =		hidden_dropout_prob
        _lowerCamelCase  :				str      =		attention_probs_dropout_prob
        _lowerCamelCase  :				Union[str, Any]      =		max_position_embeddings
        _lowerCamelCase  :				Tuple      =		type_vocab_size
        _lowerCamelCase  :				Optional[int]      =		initializer_range
        _lowerCamelCase  :				List[str]      =		layer_norm_eps
        _lowerCamelCase  :				int      =		position_embedding_type
        _lowerCamelCase  :				Optional[int]      =		use_cache
        _lowerCamelCase  :				Dict      =		max_ad_position_embeddings
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = "default"			,      SCREAMING_SNAKE_CASE = None			,      )     ->     Any:
        super().__init__(SCREAMING_SNAKE_CASE			,      task=SCREAMING_SNAKE_CASE			,      patching_specs=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[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  :				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
        _lowerCamelCase  :				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
  
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		input_dict["""input_ids"""].shape
        _lowerCamelCase  :				int      =		torch.tensor([*[box] * seq_length]).tile(SCREAMING_SNAKE_CASE			,      1			,      1)
        return input_dict
  
  
style_context: 
  
  
  """simple docstring"""
  import timeit
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     Tuple:
        if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):  # Initializes a matrix identical to the one provided.
           _lowerCamelCase  :				List[str]      =		arg
           _lowerCamelCase  :				List[str]      =		len(SCREAMING_SNAKE_CASE)
        else:  # Initializes a square matrix of the given size and set values to zero.
           _lowerCamelCase  :				List[Any]      =		arg
           _lowerCamelCase  :				Union[str, Any]      =		[[0 for _ in range(self.n)] for _ in range(self.n)]
  
  
  
  
  
     def __mul__(       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[Any]:
        _lowerCamelCase  :				List[Any]      =		Matrix(self.n)
        for i in range(self.n):
           for j in range(self.n):
              for k in range(self.n):
                 matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[Any]      =		Matrix([[1, 0], [0, 1]]      )
     while b > 0:
        if b & 1:
           matrix *= a
        a *= a
        b >>= 1
     return matrix
  
  def 							_snake_case			(      __snake_case       :			List[str]	,   __snake_case       :			str	,   __snake_case       :			List[Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if n == 1:
        return fa
     elif n == 2:
        return fa
     _lowerCamelCase  :				Any      =		Matrix([[1, 1], [1, 0]]      )
     _lowerCamelCase  :				List[str]      =		modular_exponentiation(__snake_case	,   n - 2      )
     return fa * matrix.t[0][0] + fa * matrix.t[0][1]
  
  def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if n == 1:
        return fa
     elif n == 2:
        return fa
  
     _lowerCamelCase  :				Optional[Any]      =		fa
     _lowerCamelCase  :				int      =		fa
     n -= 2
  
     while n > 0:
        _lowerCamelCase      ,						_lowerCamelCase  :				Tuple      =		fn_a + fn_a, fn_a
        n -= 1
  
     return fn_a
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[str]      =		"""
  from random import randint
  from __main__ import fibonacci_with_matrix_exponentiation
  """
     _lowerCamelCase  :				int      =		"""fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
     _lowerCamelCase  :				Tuple      =		timeit.timeit(setup=__snake_case	,   stmt=__snake_case	,   number=100      )
     print("""With matrix exponentiation the average execution time is """	,   exec_time / 100      )
     return exec_time
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Dict      =		"""
  from random import randint
  from __main__ import simple_fibonacci
  """
     _lowerCamelCase  :				Optional[int]      =		"""simple_fibonacci(randint(1,70000), 1, 1)"""
     _lowerCamelCase  :				List[Any]      =		timeit.timeit(setup=__snake_case	,   stmt=__snake_case	,   number=100      )
     print(
         """Without matrix exponentiation the average execution time is """	,   exec_time / 100      )
     return exec_time
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     matrix_exponentiation_time()
     simple_fibonacci_time()
  
  
  if __name__ == "__main__":
   main()
  
  