code: 
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  snake_case				:	List[Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	Optional[int]							   =						{
      "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
      "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
      "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
      "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
      "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
      "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
      "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					'ernie'
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=30_522		,     _a=768		,     _a=12		,     _a=12		,     _a=3_072		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=2		,     _a=3		,     _a=False		,     _a=0.02		,     _a=1e-12		,     _a=0		,     _a="absolute"		,     _a=True		,     _a=None		,     **_a		,     ):
  				super().__init__(pad_token_id=_a		,     **_a	)
  
  				__magic_name__	:	Optional[Any]			     =  vocab_size
  				__magic_name__	:	int			     =  hidden_size
  				__magic_name__	:	List[Any]			     =  num_hidden_layers
  				__magic_name__	:	Tuple			     =  num_attention_heads
  				__magic_name__	:	Optional[Any]			     =  hidden_act
  				__magic_name__	:	Tuple			     =  intermediate_size
  				__magic_name__	:	Any			     =  hidden_dropout_prob
  				__magic_name__	:	Union[str, Any]			     =  attention_probs_dropout_prob
  				__magic_name__	:	List[Any]			     =  max_position_embeddings
  				__magic_name__	:	List[Any]			     =  type_vocab_size
  				__magic_name__	:	int			     =  task_type_vocab_size
  				__magic_name__	:	List[Any]			     =  use_task_id
  				__magic_name__	:	List[Any]			     =  initializer_range
  				__magic_name__	:	List[Any]			     =  layer_norm_eps
  				__magic_name__	:	str			     =  position_embedding_type
  				__magic_name__	:	str			     =  use_cache
  				__magic_name__	:	Optional[Any]			     =  classifier_dropout
  
  
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				if self.task == "multiple-choice":
  					__magic_name__	:	Dict			     =  {0: "batch", 1: "choice", 2: "sequence"}
  				else:
  					__magic_name__	:	Optional[int]			     =  {0: "batch", 1: "sequence"}
  				return OrderedDict(
  				    [
  				        ("input_ids", dynamic_axis),
  				        ("attention_mask", dynamic_axis),
  				        ("token_type_ids", dynamic_axis),
  				        ("task_type_ids", dynamic_axis),
  				    ]	)
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if not isinstance(_snake_case   ,     _snake_case     ) or number < 0:
  		raise ValueError("Input must be a non-negative integer"     )
  
  	__magic_name__	:	Dict			     =  number & ~(number - 1)
  	__magic_name__	:	Optional[Any]			     =  0
  	while intermediate:
  		intermediate >>= 1
  		index += 1
  	return index - 1
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod(verbose=True)
  