





"""simple docstring"""




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


a		:				Optional[Any]  			=    logging.get_logger(__name__)

a		:				List[Any]  			=    {
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      List[Any]         ="""gptsan-japanese"""
							lowerCamelCase     :      Any         =[
							    """past_key_values""",
							]
							lowerCamelCase     :      Optional[Any]         ={
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}






							def __init__(     self   ,      lowerCAmelCase__=3_6000   ,      lowerCAmelCase__=1280   ,      lowerCAmelCase__=1024   ,      lowerCAmelCase__=8192   ,      lowerCAmelCase__=4096   ,      lowerCAmelCase__=128   ,      lowerCAmelCase__=10   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=128   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=1E-5   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__="float32"   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=0.002   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=3_5998   ,      lowerCAmelCase__=3_5995   ,      lowerCAmelCase__=3_5999   ,      **lowerCAmelCase__   ,      )			->				Optional[Any]:
								a   :	Tuple			=       vocab_size
								a   :	Any			=       max_position_embeddings
								a   :	Tuple			=       d_model
								a   :	Dict			=       d_ff
								a   :	Tuple			=       d_ext
								a   :	List[str]			=       d_spout
								a   :	Dict			=       num_switch_layers
								a   :	Any			=       num_ext_layers
								a   :	Union[str, Any]			=       num_switch_layers + num_ext_layers
								a   :	Dict			=       num_heads
								a   :	List[Any]			=       num_experts
								a   :	Dict			=       expert_capacity
								a   :	Optional[int]			=       dropout_rate
								a   :	int			=       layer_norm_epsilon
								a   :	Union[str, Any]			=       router_bias
								a   :	Any			=       router_jitter_noise
								a   :	List[Any]			=       router_dtype
								a   :	List[str]			=       router_ignore_padding_tokens
								a   :	str			=       output_hidden_states
								a   :	Any			=       output_attentions
								a   :	Optional[int]			=       initializer_factor
								a   :	Dict			=       output_router_logits
								a   :	Tuple			=       use_cache

								super().__init__(
								    separator_token_id=lowerCAmelCase__   ,      pad_token_id=lowerCAmelCase__   ,      eos_token_id=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )









"""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)}''')


