"""simple docstring"""



from typing import TYPE_CHECKING

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


__lowercase		:		List[Any]														=							{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Any														=							["DebertaTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							[
           "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DebertaForMaskedLM",
           "DebertaForQuestionAnswering",
           "DebertaForSequenceClassification",
           "DebertaForTokenClassification",
           "DebertaModel",
           "DebertaPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		List[Any]														=							[
           "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

       __lowercase		:		Any														=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
"""simple docstring"""



import qiskit





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							qiskit.Aer.get_backend('''aer_simulator''')

     # Create a Quantum Circuit acting on the q register
     __snake_case				=							qiskit.QuantumCircuit(snake_case,      snake_case)

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

     # Execute the circuit on the simulator
     __snake_case				=							qiskit.execute(snake_case,      snake_case,      shots=10_00)

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


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