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							:     Optional[int]								=			logging.get_logger(__name__)
  
  lowerCamelCase							:     List[str]								=			{
      'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
      'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
      'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
      'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
      'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
      'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
  }
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  List[str]						      =							"""ernie"""
  
  
  
        def __init__(self					:					List[Any]     ,		UpperCamelCase					:					List[str]=30522     ,		UpperCamelCase					:					Dict=768     ,		UpperCamelCase					:					List[str]=12     ,		UpperCamelCase					:					Tuple=12     ,		UpperCamelCase					:					Dict=3072     ,		UpperCamelCase					:					Tuple="gelu"     ,		UpperCamelCase					:					Any=0.1     ,		UpperCamelCase					:					Optional[Any]=0.1     ,		UpperCamelCase					:					Union[str, Any]=512     ,		UpperCamelCase					:					int=2     ,		UpperCamelCase					:					Optional[int]=3     ,		UpperCamelCase					:					Any=False     ,		UpperCamelCase					:					List[str]=0.02     ,		UpperCamelCase					:					int=1E-12     ,		UpperCamelCase					:					List[str]=0     ,		UpperCamelCase					:					List[Any]="absolute"     ,		UpperCamelCase					:					int=True     ,		UpperCamelCase					:					List[Any]=None     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(pad_token_id=UpperCamelCase     ,		**UpperCamelCase				)
  
               lowercase__       =				vocab_size
               lowercase__       =				hidden_size
               lowercase__       =				num_hidden_layers
               lowercase__       =				num_attention_heads
               lowercase__       =				hidden_act
               lowercase__       =				intermediate_size
               lowercase__       =				hidden_dropout_prob
               lowercase__       =				attention_probs_dropout_prob
               lowercase__       =				max_position_embeddings
               lowercase__       =				type_vocab_size
               lowercase__       =				task_type_vocab_size
               lowercase__       =				use_task_id
               lowercase__       =				initializer_range
               lowercase__       =				layer_norm_eps
               lowercase__       =				position_embedding_type
               lowercase__       =				use_cache
               lowercase__       =				classifier_dropout
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.task == "multiple-choice":
                      lowercase__       =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
               else:
                      lowercase__       =				{0: '''batch''', 1: '''sequence'''}
               return OrderedDict(
                   [
                       ('''input_ids''', dynamic_axis),
                       ('''attention_mask''', dynamic_axis),
                       ('''token_type_ids''', dynamic_axis),
                       ('''task_type_ids''', dynamic_axis),
                   ]				)
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCamelCase							:     Optional[Any]								=			{
      'configuration_mobilevitv2': [
          'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'MobileViTV2Config',
          'MobileViTV2OnnxConfig',
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Optional[int]								=			[
       'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTV2ForImageClassification',
       'MobileViTV2ForSemanticSegmentation',
       'MobileViTV2Model',
       'MobileViTV2PreTrainedModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )
  
  else:
   import sys
  
   lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  