






from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		["""RemBertTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		["""RemBertTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RemBertForCausalLM""",
       """RemBertForMaskedLM""",
       """RemBertForMultipleChoice""",
       """RemBertForQuestionAnswering""",
       """RemBertForSequenceClassification""",
       """RemBertForTokenClassification""",
       """RemBertLayer""",
       """RemBertModel""",
       """RemBertPreTrainedModel""",
       """load_tf_weights_in_rembert""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRemBertForCausalLM""",
       """TFRemBertForMaskedLM""",
       """TFRemBertForMultipleChoice""",
       """TFRemBertForQuestionAnswering""",
       """TFRemBertForSequenceClassification""",
       """TFRemBertForTokenClassification""",
       """TFRemBertLayer""",
       """TFRemBertModel""",
       """TFRemBertPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert import RemBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert_fast import RemBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rembert import (
          REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RemBertForCausalLM,
          RemBertForMaskedLM,
          RemBertForMultipleChoice,
          RemBertForQuestionAnswering,
          RemBertForSequenceClassification,
          RemBertForTokenClassification,
          RemBertLayer,
          RemBertModel,
          RemBertPreTrainedModel,
          load_tf_weights_in_rembert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rembert import (
          TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRemBertForCausalLM,
          TFRemBertForMaskedLM,
          TFRemBertForMultipleChoice,
          TFRemBertForQuestionAnswering,
          TFRemBertForSequenceClassification,
          TFRemBertForTokenClassification,
          TFRemBertLayer,
          TFRemBertModel,
          TFRemBertPreTrainedModel,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














import qiskit


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    qiskit.Aer.get_backend("aer_simulator"				)

  # Create a Quantum Circuit acting on the q register
  SCREAMING_SNAKE_CASE       :      Dict							=    qiskit.QuantumCircuit(lowercase						,     lowercase				)

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

  # Execute the circuit on the simulator
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    qiskit.execute(lowercase						,     lowercase						,     shots=1000				)

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


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






