code: 
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowerCAmelCase_   =   logging.get_logger(__name__)
  
  lowerCAmelCase_   =   {
      "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 				A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							"""ernie"""
  
  
  
        def __init__(					self     ,  lowercase_=3_0522     ,  lowercase_=768     ,  lowercase_=12     ,  lowercase_=12     ,  lowercase_=3072     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=512     ,  lowercase_=2     ,  lowercase_=3     ,  lowercase_=False     ,  lowercase_=0.02     ,  lowercase_=1E-12     ,  lowercase_=0     ,  lowercase_="absolute"     ,  lowercase_=True     ,  lowercase_=None     ,  **lowercase_     ,  )      ->   str:
  
  
  
               '''simple docstring'''
  
               super().__init__(pad_token_id=lowercase_     ,  **lowercase_					)
  
               _snake_case      :							Optional[int]     				=						vocab_size
               _snake_case      :							List[str]     				=						hidden_size
               _snake_case      :							Any     				=						num_hidden_layers
               _snake_case      :							Tuple     				=						num_attention_heads
               _snake_case      :							str     				=						hidden_act
               _snake_case      :							Any     				=						intermediate_size
               _snake_case      :							Any     				=						hidden_dropout_prob
               _snake_case      :							Dict     				=						attention_probs_dropout_prob
               _snake_case      :							Union[str, Any]     				=						max_position_embeddings
               _snake_case      :							List[Any]     				=						type_vocab_size
               _snake_case      :							Dict     				=						task_type_vocab_size
               _snake_case      :							Union[str, Any]     				=						use_task_id
               _snake_case      :							str     				=						initializer_range
               _snake_case      :							List[Any]     				=						layer_norm_eps
               _snake_case      :							List[str]     				=						position_embedding_type
               _snake_case      :							Optional[int]     				=						use_cache
               _snake_case      :							Any     				=						classifier_dropout
  
  
  class 				A    (__UpperCAmelCase				):
  
  
  
        @property
        def    __a (					self					)      ->   Mapping[str, Mapping[int, str]]:
  
  
  
               '''simple docstring'''
  
               if self.task == "multiple-choice":
                      _snake_case      :							Tuple     				=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
               else:
                      _snake_case      :							Optional[Any]     				=						{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: 
  
  
  
  
  
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_  )			->						Optional[Any]:
         _snake_case      :							Union[str, Any]     				=						np.exp(lowercase_  )
  
         # Add up the all the exponentials
         _snake_case      :							List[str]     				=						np.sum(lowercase_  )
  
         # Divide every exponent by the sum of all exponents
         _snake_case      :							Optional[int]     				=						exponent_vector / sum_of_exponents
  
         return softmax_vector
  
  
  if __name__ == "__main__":
     print(softmax((0,)))
  
  
  
  
  
  
  