code: 
  '''simple docstring'''
  
  import doctest
  
  import projectq
  from projectq.ops import H, Measure
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      int:
  		_UpperCamelCase       : Optional[int]    =      quantum_engine.allocate_qubit()
  		H | qubit
  		Measure | qubit
  		return int(UpperCamelCase	)
  
  
  if __name__ == "__main__":
  	doctest.testmod()
  
  	# initialises a new quantum backend
  	_UpperCAmelCase   :      Optional[Any] 						=  projectq.MainEngine()
  
  	# Generate a list of 10 random numbers
  	_UpperCAmelCase   :      int 						=  [get_random_number(quantum_engine) for _ in range(10)]
  
  	# Flushes the quantum engine from memory
  	quantum_engine.flush()
  
  	print("""Random numbers""", random_numbers_list)
  
  
style_context: 
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _UpperCAmelCase   :      List[Any] 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      List[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"""
  
  
  
  
  
  		A__  :   str          =    'ernie'
  		def __init__(    self  ,					_snake_case=30522  ,					_snake_case=768  ,					_snake_case=12  ,					_snake_case=12  ,					_snake_case=3072  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.1  ,					_snake_case=512  ,					_snake_case=2  ,					_snake_case=3  ,					_snake_case=False  ,					_snake_case=0.02  ,					_snake_case=1E-12  ,					_snake_case=0  ,					_snake_case="absolute"  ,					_snake_case=True  ,					_snake_case=None  ,					**_snake_case  ,					)				->    List[Any]:
  				super().__init__(pad_token_id=_snake_case  ,					**_snake_case     )
  
  				_UpperCamelCase       : Dict    =      vocab_size
  				_UpperCamelCase       : int    =      hidden_size
  				_UpperCamelCase       : List[Any]    =      num_hidden_layers
  				_UpperCamelCase       : Tuple    =      num_attention_heads
  				_UpperCamelCase       : List[str]    =      hidden_act
  				_UpperCamelCase       : Optional[int]    =      intermediate_size
  				_UpperCamelCase       : Tuple    =      hidden_dropout_prob
  				_UpperCamelCase       : Optional[int]    =      attention_probs_dropout_prob
  				_UpperCamelCase       : List[str]    =      max_position_embeddings
  				_UpperCamelCase       : Tuple    =      type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      task_type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      use_task_id
  				_UpperCamelCase       : Any    =      initializer_range
  				_UpperCamelCase       : Optional[int]    =      layer_norm_eps
  				_UpperCamelCase       : str    =      position_embedding_type
  				_UpperCamelCase       : Any    =      use_cache
  				_UpperCamelCase       : str    =      classifier_dropout
  
  
  
  
  
  
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task == "multiple-choice":
  						_UpperCamelCase       : str    =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
  				else:
  						_UpperCamelCase       : Dict    =      {0: '''batch''', 1: '''sequence'''}
  				return OrderedDict(
  				    [
  				        ('''input_ids''', dynamic_axis),
  				        ('''attention_mask''', dynamic_axis),
  				        ('''token_type_ids''', dynamic_axis),
  				        ('''task_type_ids''', dynamic_axis),
  				    ]     )
  
  
  
  
  
  