'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowerCamelCase :				Optional[int]														= {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase :				int														= [
          """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """Speech2Text2ForCausalLM""",
          """Speech2Text2PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
      from .processing_speech_to_text_a import SpeechaTextaProcessor
      from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text_a import (
                SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextaForCausalLM,
                SpeechaTextaPreTrainedModel,
            )

else:
      import sys

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


"""simple docstring"""




import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute



def 						UpperCAmelCase__ (					A__ = 1						,     A__ = 1 )   ->   qiskit.result.counts.Counts:




   """simple docstring"""
   if isinstance(A__						,     A__ ) or isinstance(A__						,     A__ ):
      raise TypeError("inputs must be integers." )
   if (bit_a < 0) or (bit_a < 0):
      raise ValueError("inputs must be positive." )
   if (math.floor(A__ ) != bit_a) or (math.floor(A__ ) != bit_a):
      raise ValueError("inputs must be exact integers." )
   if (bit_a > 1) or (bit_a > 1):
      raise ValueError("inputs must be less or equal to 1." )

   # build registers
   lowerCamelCase__						       =      QuantumRegister(2						,     "qr" )
   lowerCamelCase__						       =      ClassicalRegister(2						,     "cr" )

   lowerCamelCase__						       =      QuantumCircuit(A__						,     A__ )

   # entanglement the qubits
   quantum_circuit.h(1 )
   quantum_circuit.cx(1						,     0 )

   # send the information
   lowerCamelCase__						       =      str(A__ ) + str(A__ )

   if c_information == "11":
      quantum_circuit.x(1 )
      quantum_circuit.z(1 )
   elif c_information == "10":
      quantum_circuit.z(1 )
   elif c_information == "01":
      quantum_circuit.x(1 )
   else:
      quantum_circuit.i(1 )

   # unentangled the circuit
   quantum_circuit.cx(1						,     0 )
   quantum_circuit.h(1 )

   # measure the circuit
   quantum_circuit.measure(A__						,     A__ )

   lowerCamelCase__						       =      Aer.get_backend("aer_simulator" )
   lowerCamelCase__						       =      execute(A__						,     A__						,     shots=1000 )

   return job.result().get_counts(A__ )


if __name__ == "__main__":
     print(F'''Counts for classical state send: {superdense_coding(1,1)}''')




