






'''simple docstring'''
import qiskit





def  _a(  UpperCamelCase__				:						Union[str, Any] = 2							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				str									=qubits

						# Using Aer's simulator
						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=qiskit.Aer.get_backend('''aer_simulator'''							)

						# Creating a Quantum Circuit acting on the q register
						SCREAMING_SNAKE_CASE__     :				Tuple									=qiskit.QuantumCircuit(__lowerCAmelCase,	__lowerCAmelCase							)

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

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

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

						# 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__     :				str									=qiskit.execute(__lowerCAmelCase,	__lowerCAmelCase,	shots=1_0_0_0							)

						return job.result().get_counts(__lowerCAmelCase							)


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







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)