

import qiskit

def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Tuple )					->				qiskit.result.counts.Counts:
 A__							:   Optional[Any]					       =		qiskit.Aer.get_backend("""aer_simulator""" )

 # Create a Quantum Circuit acting on the q register
 A__							:   str					       =		qiskit.QuantumCircuit(UpperCAmelCase__   ,				UpperCAmelCase__ )

 # Apply X (NOT) Gate to Qubits 0 & 1
 circuit.x(0 )
 circuit.x(1 )

 # Map the quantum measurement to the classical bits
 circuit.measure([0, 1]   ,				[0, 1] )

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

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


if __name__ == "__main__":
 A_	:			str  	=      single_qubit_measure(2, 2)
 print(f'''Total count for various states are: {counts}''')






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



