code: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __a   				=      logging.get_logger(__name__)
  
  __a   				=      {
      '''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 				__SCREAMING_SNAKE_CASE   (			A__   ):
        A				:							Dict    							=     'ernie'
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__=30522							,						SCREAMING_SNAKE_CASE__=768							,						SCREAMING_SNAKE_CASE__=12							,						SCREAMING_SNAKE_CASE__=12							,						SCREAMING_SNAKE_CASE__=3072							,						SCREAMING_SNAKE_CASE__="gelu"							,						SCREAMING_SNAKE_CASE__=0.1							,						SCREAMING_SNAKE_CASE__=0.1							,						SCREAMING_SNAKE_CASE__=512							,						SCREAMING_SNAKE_CASE__=2							,						SCREAMING_SNAKE_CASE__=3							,						SCREAMING_SNAKE_CASE__=False							,						SCREAMING_SNAKE_CASE__=0.02							,						SCREAMING_SNAKE_CASE__=1E-12							,						SCREAMING_SNAKE_CASE__=0							,						SCREAMING_SNAKE_CASE__="absolute"							,						SCREAMING_SNAKE_CASE__=True							,						SCREAMING_SNAKE_CASE__=None							,						**SCREAMING_SNAKE_CASE__							,						):
              super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
  
              lowercase			:   int					   =		vocab_size
              lowercase			:   Tuple					   =		hidden_size
              lowercase			:   int					   =		num_hidden_layers
              lowercase			:   Optional[int]					   =		num_attention_heads
              lowercase			:   List[str]					   =		hidden_act
              lowercase			:   Optional[Any]					   =		intermediate_size
              lowercase			:   List[Any]					   =		hidden_dropout_prob
              lowercase			:   Optional[Any]					   =		attention_probs_dropout_prob
              lowercase			:   Any					   =		max_position_embeddings
              lowercase			:   Optional[int]					   =		type_vocab_size
              lowercase			:   Optional[int]					   =		task_type_vocab_size
              lowercase			:   List[str]					   =		use_task_id
              lowercase			:   Optional[int]					   =		initializer_range
              lowercase			:   Any					   =		layer_norm_eps
              lowercase			:   int					   =		position_embedding_type
              lowercase			:   Optional[int]					   =		use_cache
              lowercase			:   Union[str, Any]					   =		classifier_dropout
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        @property
        def        __lowerCamelCase			(							self		):
              if self.task == "multiple-choice":
                    lowercase			:   Dict					   =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
              else:
                    lowercase			:   Optional[int]					   =		{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: 
  
  
  from __future__ import annotations
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->list[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        assert (
            isinstance(_UpperCamelCase,    _UpperCamelCase     ) and mask > 0
        ), f"""mask needs to be positive integer, your input {mask}"""
        lowercase			:   Optional[int]					   =		[]
        lowercase			:   Optional[int]					   =		mask
  
        while submask:
              all_submasks.append(_UpperCamelCase     )
              lowercase			:   Dict					   =		(submask - 1) & mask
  
        return all_submasks
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
  