code: 
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _lowerCAmelCase     :			str    =logging.get_logger(__name__)
  
  _lowerCAmelCase     :			Optional[int]    ={
      'squeezebert/squeezebert-uncased': (
          'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
      ),
      'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
      'squeezebert/squeezebert-mnli-headless': (
          'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
      ),
  }
  
  class __UpperCamelCase    (							A__		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  					__magic_name__     			=      "squeezebert"
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__=3_0_5_2_2							,			lowerCamelCase__=7_6_8							,			lowerCamelCase__=1_2							,			lowerCamelCase__=1_2							,			lowerCamelCase__=3_0_7_2							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.1							,			lowerCamelCase__=5_1_2							,			lowerCamelCase__=2							,			lowerCamelCase__=0.02							,			lowerCamelCase__=1e-12							,			lowerCamelCase__=0							,			lowerCamelCase__=7_6_8							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			lowerCamelCase__=1							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			**lowerCamelCase__							,			):
  						super().__init__(pad_token_id=_lowerCAmelCase							,			**_lowerCAmelCase	)
  
  						UpperCAmelCase__:  List[str]		       =   vocab_size
  						UpperCAmelCase__:  str		       =   hidden_size
  						UpperCAmelCase__:  List[str]		       =   num_hidden_layers
  						UpperCAmelCase__:  str		       =   num_attention_heads
  						UpperCAmelCase__:  Optional[Any]		       =   hidden_act
  						UpperCAmelCase__:  Dict		       =   intermediate_size
  						UpperCAmelCase__:  Dict		       =   hidden_dropout_prob
  						UpperCAmelCase__:  int		       =   attention_probs_dropout_prob
  						UpperCAmelCase__:  Dict		       =   max_position_embeddings
  						UpperCAmelCase__:  List[str]		       =   type_vocab_size
  						UpperCAmelCase__:  List[Any]		       =   initializer_range
  						UpperCAmelCase__:  Union[str, Any]		       =   layer_norm_eps
  						UpperCAmelCase__:  str		       =   embedding_size
  						UpperCAmelCase__:  List[Any]		       =   q_groups
  						UpperCAmelCase__:  Union[str, Any]		       =   k_groups
  						UpperCAmelCase__:  str		       =   v_groups
  						UpperCAmelCase__:  List[Any]		       =   post_attention_groups
  						UpperCAmelCase__:  str		       =   intermediate_groups
  						UpperCAmelCase__:  Any		       =   output_groups
  
  class __UpperCamelCase    (							A__		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						if self.task == "multiple-choice":
  							UpperCAmelCase__:  str		       =   {0: "batch", 1: "choice", 2: "sequence"}
  						else:
  							UpperCAmelCase__:  Dict		       =   {0: "batch", 1: "sequence"}
  						return OrderedDict(
  						    [
  						        ("input_ids", dynamic_axis),
  						        ("attention_mask", dynamic_axis),
  						        ("token_type_ids", dynamic_axis),
  						    ]	)
style_context: 
  
  import numpy as np
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)
  
  	# Add up the all the exponentials
  	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)
  
  	# Divide every exponent by the sum of all exponents
  	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents
  
  	return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))