code: 
  
  """simple docstring"""
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __A  :					Dict												=	logging.get_logger(__name__)
  
  __A  :					Union[str, Any]												=	{
      'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
  }
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					"mobilevitv2"
  
  
  		def __init__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=3  ,  __SCREAMING_SNAKE_CASE				:	Any=2_5_6  ,  __SCREAMING_SNAKE_CASE				:	str=2  ,  __SCREAMING_SNAKE_CASE				:	Any=2.0  ,  __SCREAMING_SNAKE_CASE				:	List[Any]="swish"  ,  __SCREAMING_SNAKE_CASE				:	Tuple=3  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=3_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=0.1  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.0_2  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1E-5  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=[6, 1_2, 1_8]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=0.1  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=2_5_5  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=[2, 4, 3]  ,  __SCREAMING_SNAKE_CASE				:	Dict=[1_2_8, 1_9_2, 2_5_6]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1.0  ,  __SCREAMING_SNAKE_CASE				:	int=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=0.0  ,  __SCREAMING_SNAKE_CASE				:	int=0.0  ,  **__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  ):
  							super().__init__(**__SCREAMING_SNAKE_CASE)
  
  							A    =      num_channels
  							A    =      image_size
  							A    =      patch_size
  							A    =      expand_ratio
  							A    =      hidden_act
  							A    =      conv_kernel_size
  							A    =      output_stride
  							A    =      initializer_range
  							A    =      layer_norm_eps
  							A    =      n_attn_blocks
  							A    =      base_attn_unit_dims
  							A    =      width_multiplier
  							A    =      ffn_multiplier
  							A    =      ffn_dropout
  							A    =      attn_dropout
  							A    =      classifier_dropout_prob
  
  							# decode head attributes for semantic segmentation
  							A    =      aspp_out_channels
  							A    =      atrous_rates
  							A    =      aspp_dropout_prob
  							A    =      semantic_loss_ignore_index
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					version.parse("1.11"		)
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							if self.task == "image-classification":
  												return OrderedDict([("logits", {0: "batch"})])
  							else:
  												return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							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__)
  
  
  
  
  
  