code: 
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__    :    Any								=					{
      "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
      # See all RemBERT models at https://huggingface.co/models?filter=rembert
  }
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   '''rembert'''
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE=25_0300			,    SCREAMING_SNAKE_CASE=1152			,    SCREAMING_SNAKE_CASE=32			,    SCREAMING_SNAKE_CASE=18			,    SCREAMING_SNAKE_CASE=256			,    SCREAMING_SNAKE_CASE=1664			,    SCREAMING_SNAKE_CASE=4608			,    SCREAMING_SNAKE_CASE="gelu"			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=0.1			,    SCREAMING_SNAKE_CASE=512			,    SCREAMING_SNAKE_CASE=2			,    SCREAMING_SNAKE_CASE=0.0_2			,    SCREAMING_SNAKE_CASE=1e-12			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=0			,    SCREAMING_SNAKE_CASE=312			,    SCREAMING_SNAKE_CASE=313			,    **SCREAMING_SNAKE_CASE			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE			,    bos_token_id=SCREAMING_SNAKE_CASE			,    eos_token_id=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	vocab_size
  				SCREAMING_SNAKE_CASE_			=	input_embedding_size
  				SCREAMING_SNAKE_CASE_			=	output_embedding_size
  				SCREAMING_SNAKE_CASE_			=	max_position_embeddings
  				SCREAMING_SNAKE_CASE_			=	hidden_size
  				SCREAMING_SNAKE_CASE_			=	num_hidden_layers
  				SCREAMING_SNAKE_CASE_			=	num_attention_heads
  				SCREAMING_SNAKE_CASE_			=	intermediate_size
  				SCREAMING_SNAKE_CASE_			=	hidden_act
  				SCREAMING_SNAKE_CASE_			=	hidden_dropout_prob
  				SCREAMING_SNAKE_CASE_			=	attention_probs_dropout_prob
  				SCREAMING_SNAKE_CASE_			=	classifier_dropout_prob
  				SCREAMING_SNAKE_CASE_			=	initializer_range
  				SCREAMING_SNAKE_CASE_			=	type_vocab_size
  				SCREAMING_SNAKE_CASE_			=	layer_norm_eps
  				SCREAMING_SNAKE_CASE_			=	use_cache
  				SCREAMING_SNAKE_CASE_			=	False
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Mapping[str, Mapping[int, str]]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.task == "multiple-choice":
  							SCREAMING_SNAKE_CASE_			=	{0: 'batch', 1: 'choice', 2: 'sequence'}
  				else:
  							SCREAMING_SNAKE_CASE_			=	{0: 'batch', 1: 'sequence'}
  				return OrderedDict(
  				    [
  				        ('input_ids', dynamic_axis),
  				        ('attention_mask', dynamic_axis),
  				        ('token_type_ids', dynamic_axis),
  				    ]			)
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				float:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return 1e-4
  
style_context: 
  
  
  
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					[{"type": "code", "content": INSTALL_CONTENT}]
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{
      "{processor_class}": "FakeProcessorClass",
      "{model_class}": "FakeModelClass",
      "{object_class}": "FakeObjectClass",
  }
  