





"""simple docstring"""




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:				List[str]  			=    logging.get_logger(__name__)

a		:				Union[str, Any]  			=    {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      str         ="""xlnet"""
							lowerCamelCase     :      Dict         =["""mems"""]
							lowerCamelCase     :      Optional[int]         ={
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(     self   ,      lowerCAmelCase__=3_2000   ,      lowerCAmelCase__=1024   ,      lowerCAmelCase__=24   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=4096   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__="bi"   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=1E-12   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=512   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=-1   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__="last"   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__="tanh"   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=5   ,      lowerCAmelCase__=5   ,      lowerCAmelCase__=5   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=2   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Any			=       vocab_size
								a   :	Tuple			=       d_model
								a   :	Tuple			=       n_layer
								a   :	Tuple			=       n_head
								if d_model % n_head != 0:
									raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""   )
								if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    f"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"""   )
								a   :	Tuple			=       d_model // n_head
								a   :	Optional[Any]			=       ff_activation
								a   :	List[Any]			=       d_inner
								a   :	Any			=       untie_r
								a   :	str			=       attn_type

								a   :	List[Any]			=       initializer_range
								a   :	Any			=       layer_norm_eps

								a   :	List[Any]			=       dropout
								a   :	List[Any]			=       mem_len
								a   :	Optional[Any]			=       reuse_len
								a   :	List[str]			=       bi_data
								a   :	Dict			=       clamp_len
								a   :	Any			=       same_length

								a   :	int			=       summary_type
								a   :	str			=       summary_use_proj
								a   :	Tuple			=       summary_activation
								a   :	Tuple			=       summary_last_dropout
								a   :	List[Any]			=       start_n_top
								a   :	Tuple			=       end_n_top

								a   :	Dict			=       bos_token_id
								a   :	List[Any]			=       pad_token_id
								a   :	Dict			=       eos_token_id

								if "use_cache" in kwargs:
									warnings.warn(
									    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
									    " instead."   ,      lowerCAmelCase__   ,      )
									a   :	Union[str, Any]			=       kwargs["use_cache"]

								a   :	Optional[Any]			=       use_mems_eval
								a   :	Dict			=       use_mems_train
								super().__init__(pad_token_id=lowerCAmelCase__   ,      bos_token_id=lowerCAmelCase__   ,      eos_token_id=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@property
							def 					__a     (     self   )			->				Union[str, Any]:
								logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""   )
								return -1






							@max_position_embeddings.setter
							def 					__a     (     self   ,      lowerCAmelCase__   )			->				List[str]:
								# Message copied from Transformer-XL documentation
								raise NotImplementedError(
								    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""   )









"""simple docstring"""




import qiskit






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int      )				->qiskit.result.counts.Counts:






	'''simple docstring'''






	a   :	Union[str, Any]			=       qiskit.Aer.get_backend("aer_simulator"      )

	# Create a Quantum Circuit acting on the q register
	a   :	Optional[Any]			=       qiskit.QuantumCircuit(_lowercase					, _lowercase      )

	# Map the quantum measurement to the classical bits
	circuit.measure([0]					, [0]      )

	# Execute the circuit on the simulator
	a   :	Optional[int]			=       qiskit.execute(_lowercase					, _lowercase					, shots=1000      )

	# Return the histogram data of the results of the experiment.
	return job.result().get_counts(_lowercase      )


if __name__ == "__main__":
		print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')


