





'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase							:     List[str]		     ={
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase							:     Dict		     =["DistilBertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase							:     Any		     =[
		    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DistilBertForMaskedLM",
		    "DistilBertForMultipleChoice",
		    "DistilBertForQuestionAnswering",
		    "DistilBertForSequenceClassification",
		    "DistilBertForTokenClassification",
		    "DistilBertModel",
		    "DistilBertPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase							:     int		     =[
		    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDistilBertForMaskedLM",
		    "TFDistilBertForMultipleChoice",
		    "TFDistilBertForQuestionAnswering",
		    "TFDistilBertForSequenceClassification",
		    "TFDistilBertForTokenClassification",
		    "TFDistilBertMainLayer",
		    "TFDistilBertModel",
		    "TFDistilBertPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase							:     Optional[Any]		     =[
		    "FlaxDistilBertForMaskedLM",
		    "FlaxDistilBertForMultipleChoice",
		    "FlaxDistilBertForQuestionAnswering",
		    "FlaxDistilBertForSequenceClassification",
		    "FlaxDistilBertForTokenClassification",
		    "FlaxDistilBertModel",
		    "FlaxDistilBertPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_distilbert import (
		    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DistilBertConfig,
		    DistilBertOnnxConfig,
		)
		from .tokenization_distilbert import DistilBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_distilbert_fast import DistilBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_distilbert import (
				    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DistilBertForMaskedLM,
				    DistilBertForMultipleChoice,
				    DistilBertForQuestionAnswering,
				    DistilBertForSequenceClassification,
				    DistilBertForTokenClassification,
				    DistilBertModel,
				    DistilBertPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_distilbert import (
				    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDistilBertForMaskedLM,
				    TFDistilBertForMultipleChoice,
				    TFDistilBertForQuestionAnswering,
				    TFDistilBertForSequenceClassification,
				    TFDistilBertForTokenClassification,
				    TFDistilBertMainLayer,
				    TFDistilBertModel,
				    TFDistilBertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				    FlaxDistilBertPreTrainedModel,
				)

else:
		import sys

		__lowerCAmelCase							:     List[str]		     =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''

import qiskit





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int	):
    A__    =      qiskit.Aer.get_backend("aer_simulator"	)

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

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

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

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


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



