code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  a__				: Dict									=    logging.get_logger(__name__)
  
  a__				: Optional[int]									=    {
      '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''plbart'''
  	__lowerCAmelCase						=     ['''past_key_values''']
  	__lowerCAmelCase						=     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=5_0005			,					UpperCamelCase_=1024			,					UpperCamelCase_=6			,					UpperCamelCase_=3072			,					UpperCamelCase_=12			,					UpperCamelCase_=6			,					UpperCamelCase_=3072			,					UpperCamelCase_=12			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_="gelu"			,					UpperCamelCase_=768			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=0.0			,					UpperCamelCase_=True			,					UpperCamelCase_=1			,					UpperCamelCase_=0			,					UpperCamelCase_=2			,					UpperCamelCase_=2			,					**UpperCamelCase_			,					)			->		Optional[int]:
  		snake_case__  					=      vocab_size
  		snake_case__  					=      max_position_embeddings
  		snake_case__  					=      d_model
  		snake_case__  					=      encoder_ffn_dim
  		snake_case__  					=      encoder_layers
  		snake_case__  					=      encoder_attention_heads
  		snake_case__  					=      decoder_ffn_dim
  		snake_case__  					=      decoder_layers
  		snake_case__  					=      decoder_attention_heads
  		snake_case__  					=      dropout
  		snake_case__  					=      attention_dropout
  		snake_case__  					=      activation_dropout
  		snake_case__  					=      activation_function
  		snake_case__  					=      init_std
  		snake_case__  					=      encoder_layerdrop
  		snake_case__  					=      decoder_layerdrop
  		snake_case__  					=      classifier_dropout
  		snake_case__  					=      use_cache
  		snake_case__  					=      encoder_layers
  		snake_case__  					=      scale_embedding  # scale factor will be sqrt(d_model) if True
  		super().__init__(
  		    pad_token_id=UpperCamelCase_			,					bos_token_id=UpperCamelCase_			,					eos_token_id=UpperCamelCase_			,					is_encoder_decoder=UpperCamelCase_			,					forced_eos_token_id=UpperCamelCase_			,					**UpperCamelCase_			,					)
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Mapping[str, Mapping[int, str]]:
  		return OrderedDict(
  		    [
  		        ('input_ids', {0: 'batch', 1: 'sequence'}),
  		        ('attention_mask', {0: 'batch', 1: 'sequence'}),
  		    ]		)
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Mapping[str, Mapping[int, str]]:
  		if self.use_past:
  			return OrderedDict(
  			    [
  			        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
  			        ('past_keys', {0: 'batch', 2: 'sequence'}),
  			        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
  			    ]		)
  		else:
  			return OrderedDict(
  			    [
  			        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
  			        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
  			    ]		)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->int:
  	return 1 if input_a == 0 else 0
  
  
  
  
  def 	__lowerCamelCase (  )      ->None:
  	assert not_gate(0       ) == 1
  	assert not_gate(1       ) == 0
  
  
  if __name__ == "__main__":
  			print(not_gate(0))
  			print(not_gate(1))
  
  
  
  