code: 
  
  
  
  
  
  
  """simple docstring"""
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  snake_case   :   Union[str, Any]    		=		logging.get_logger(__name__)
  
  snake_case   :   Union[str, Any]    		=		{
      """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 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """ernie"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[int]=3_0_5_2_2							,						UpperCamelCase_   :  Optional[int]=7_6_8							,						UpperCamelCase_   :  Optional[Any]=1_2							,						UpperCamelCase_   :  str=1_2							,						UpperCamelCase_   :  Any=3_0_7_2							,						UpperCamelCase_   :  str="gelu"							,						UpperCamelCase_   :  Dict=0.1							,						UpperCamelCase_   :  Dict=0.1							,						UpperCamelCase_   :  str=5_1_2							,						UpperCamelCase_   :  Union[str, Any]=2							,						UpperCamelCase_   :  Optional[int]=3							,						UpperCamelCase_   :  Any=False							,						UpperCamelCase_   :  Dict=0.02							,						UpperCamelCase_   :  List[str]=1e-1_2							,						UpperCamelCase_   :  List[str]=0							,						UpperCamelCase_   :  Any="absolute"							,						UpperCamelCase_   :  Tuple=True							,						UpperCamelCase_   :  Any=None							,						**UpperCamelCase_   :  List[Any]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__(pad_token_id=UpperCamelCase_							,						**UpperCamelCase_   )
  
             __magic_name__										=						vocab_size
             __magic_name__										=						hidden_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						hidden_act
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						type_vocab_size
             __magic_name__										=						task_type_vocab_size
             __magic_name__										=						use_task_id
             __magic_name__										=						initializer_range
             __magic_name__										=						layer_norm_eps
             __magic_name__										=						position_embedding_type
             __magic_name__										=						use_cache
             __magic_name__										=						classifier_dropout
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task == "multiple-choice":
                    __magic_name__										=						{0: 'batch', 1: 'choice', 2: 'sequence'}
             else:
                    __magic_name__										=						{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_tokenizers_available, is_torch_available
  
  
  snake_case   :   Optional[int]    		=		{
      """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
      """tokenization_splinter""": ["""SplinterTokenizer"""],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		[
       """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SplinterForQuestionAnswering""",
       """SplinterForPreTraining""",
       """SplinterLayer""",
       """SplinterModel""",
       """SplinterPreTrainedModel""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   List[str]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)