

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase   :	Union[str, Any]												=						{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase   :	str												=						[
				    'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ElectraForCausalLM',
				    'ElectraForMaskedLM',
				    'ElectraForMultipleChoice',
				    'ElectraForPreTraining',
				    'ElectraForQuestionAnswering',
				    'ElectraForSequenceClassification',
				    'ElectraForTokenClassification',
				    'ElectraModel',
				    'ElectraPreTrainedModel',
				    'load_tf_weights_in_electra',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase   :	Optional[int]												=						[
				    'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFElectraForMaskedLM',
				    'TFElectraForMultipleChoice',
				    'TFElectraForPreTraining',
				    'TFElectraForQuestionAnswering',
				    'TFElectraForSequenceClassification',
				    'TFElectraForTokenClassification',
				    'TFElectraModel',
				    'TFElectraPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase   :	Any												=						[
				    'FlaxElectraForCausalLM',
				    'FlaxElectraForMaskedLM',
				    'FlaxElectraForMultipleChoice',
				    'FlaxElectraForPreTraining',
				    'FlaxElectraForQuestionAnswering',
				    'FlaxElectraForSequenceClassification',
				    'FlaxElectraForTokenClassification',
				    'FlaxElectraModel',
				    'FlaxElectraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
				from .tokenization_electra import ElectraTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_electra_fast import ElectraTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_electra import (
								    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ElectraForCausalLM,
								    ElectraForMaskedLM,
								    ElectraForMultipleChoice,
								    ElectraForPreTraining,
								    ElectraForQuestionAnswering,
								    ElectraForSequenceClassification,
								    ElectraForTokenClassification,
								    ElectraModel,
								    ElectraPreTrainedModel,
								    load_tf_weights_in_electra,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_electra import (
								    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFElectraForMaskedLM,
								    TFElectraForMultipleChoice,
								    TFElectraForPreTraining,
								    TFElectraForQuestionAnswering,
								    TFElectraForSequenceClassification,
								    TFElectraForTokenClassification,
								    TFElectraModel,
								    TFElectraPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_electra import (
								    FlaxElectraForCausalLM,
								    FlaxElectraForMaskedLM,
								    FlaxElectraForMultipleChoice,
								    FlaxElectraForPreTraining,
								    FlaxElectraForQuestionAnswering,
								    FlaxElectraForSequenceClassification,
								    FlaxElectraForTokenClassification,
								    FlaxElectraModel,
								    FlaxElectraPreTrainedModel,
								)

else:
				import sys

				_lowercase   :	Dict												=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import qiskit

def lowercase    (  SCREAMING_SNAKE_CASE = 2 )			->							qiskit.result.counts.Counts:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						qubits

			# Using Aer's simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.Aer.get_backend('aer_simulator' )

			# Creating a Quantum Circuit acting on the q register
			SCREAMING_SNAKE_CASE_								=						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Adding a H gate on qubit 0 (now q0 in superposition)
			circuit.h(0 )

			for i in range(1       ,    SCREAMING_SNAKE_CASE ):
						# Adding CX (CNOT) gate
						circuit.cx(i - 1       ,    SCREAMING_SNAKE_CASE )

			# Mapping the quantum measurement to the classical bits
			circuit.measure(list(range(SCREAMING_SNAKE_CASE ) )       ,    list(range(SCREAMING_SNAKE_CASE ) ) )

			# Now measuring any one qubit would affect other qubits to collapse
			# their super position and have same state as the measured one.

			# Executing the circuit on the simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.execute(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    shots=10_00 )

			return job.result().get_counts(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				print(f"""Total count for various states are: {quantum_entanglement(3)}""")
