code: 
  
  """simple docstring"""
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowercase__  :					Any												=	logging.get_logger(__name__)
  
  lowercase__  :					List[str]												=	{
      'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
      # See all RemBERT models at https://huggingface.co/models?filter=rembert
  }
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					"rembert"
  
  
  		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	str=2_5_0_3_0_0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_1_5_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_8  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=2_5_6  ,  __SCREAMING_SNAKE_CASE				:	List[str]=1_6_6_4  ,  __SCREAMING_SNAKE_CASE				:	str=4_6_0_8  ,  __SCREAMING_SNAKE_CASE				:	List[Any]="gelu"  ,  __SCREAMING_SNAKE_CASE				:	Any=0.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=0.0  ,  __SCREAMING_SNAKE_CASE				:	int=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Dict=2  ,  __SCREAMING_SNAKE_CASE				:	Any=0.0_2  ,  __SCREAMING_SNAKE_CASE				:	str=1E-12  ,  __SCREAMING_SNAKE_CASE				:	Dict=True  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=3_1_2  ,  __SCREAMING_SNAKE_CASE				:	List[str]=3_1_3  ,  **__SCREAMING_SNAKE_CASE				:	Any  ,  ):
  							super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,  bos_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  							A    =      vocab_size
  							A    =      input_embedding_size
  							A    =      output_embedding_size
  							A    =      max_position_embeddings
  							A    =      hidden_size
  							A    =      num_hidden_layers
  							A    =      num_attention_heads
  							A    =      intermediate_size
  							A    =      hidden_act
  							A    =      hidden_dropout_prob
  							A    =      attention_probs_dropout_prob
  							A    =      classifier_dropout_prob
  							A    =      initializer_range
  							A    =      type_vocab_size
  							A    =      layer_norm_eps
  							A    =      use_cache
  							A    =      False
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							if self.task == "multiple-choice":
  												A    =      {0: "batch", 1: "choice", 2: "sequence"}
  							else:
  												A    =      {0: "batch", 1: "sequence"}
  							return OrderedDict(
  							    [
  							        ("input_ids", dynamic_axis),
  							        ("attention_mask", dynamic_axis),
  							        ("token_type_ids", dynamic_axis),
  							    ])
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							return 1E-4
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'NystromformerForMaskedLM',
  	    'NystromformerForMultipleChoice',
  	    'NystromformerForQuestionAnswering',
  	    'NystromformerForSequenceClassification',
  	    'NystromformerForTokenClassification',
  	    'NystromformerLayer',
  	    'NystromformerModel',
  	    'NystromformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_nystromformer import (
  		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    NystromformerForMaskedLM,
  		    NystromformerForMultipleChoice,
  		    NystromformerForQuestionAnswering,
  		    NystromformerForSequenceClassification,
  		    NystromformerForTokenClassification,
  		    NystromformerLayer,
  		    NystromformerModel,
  		    NystromformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  