






"""simple docstring"""


import numpy as np
import qiskit



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					qiskit.QuantumCircuit(num_qubits + 1					)

  # First, let's deal with the case in which oracle is balanced
  if case == "balanced":
    # First generate a random number that tells us which CNOTs to
    # wrap in X-gates:
    __A		     =					np.random.randint(1						,			2**num_qubits					)
    # Next, format 'b' as a binary string of length 'n', padded with zeros:
    __A		     =					format(__UpperCamelCase						,			f'0{num_qubits}b'					)
    # Next, we place the first X-gates. Each digit in our binary string
    # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
    # we apply an X-gate to that qubit:
    for index, bit in enumerate(__UpperCamelCase					):
      if bit == "1":
        oracle_qc.x(__UpperCamelCase					)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
    for index in range(__UpperCamelCase					):
      oracle_qc.cx(__UpperCamelCase						,			__UpperCamelCase					)
    # Next, place the final X-gates
    for index, bit in enumerate(__UpperCamelCase					):
      if bit == "1":
        oracle_qc.x(__UpperCamelCase					)

    # Case in which oracle is constant
  if case == "constant":
    # First decide what the fixed output of the oracle will be
    # (either always 0 or always 1)
    __A		     =					np.random.randint(2					)
    if output == 1:
      oracle_qc.x(__UpperCamelCase					)

  __A		     =					oracle_qc.to_gate()
  __A		     =					'''Oracle'''  # To show when we display the circuit
  return oracle_gate



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					qiskit.QuantumCircuit(num_qubits + 1						,			__UpperCamelCase					)
  # Set up the output qubit:
  dj_circuit.x(__UpperCamelCase					)
  dj_circuit.h(__UpperCamelCase					)
  # And set up the input register:
  for qubit in range(__UpperCamelCase					):
    dj_circuit.h(__UpperCamelCase					)
  # Let's append the oracle gate to our circuit:
  dj_circuit.append(__UpperCamelCase						,			range(num_qubits + 1					)					)
  # Finally, perform the H-gates again and measure:
  for qubit in range(__UpperCamelCase					):
    dj_circuit.h(__UpperCamelCase					)

  for i in range(__UpperCamelCase					):
    dj_circuit.measure(__UpperCamelCase						,			__UpperCamelCase					)

  return dj_circuit



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					qiskit.Aer.get_backend('''aer_simulator'''					)

  __A		     =					dj_oracle(__UpperCamelCase						,			__UpperCamelCase					)
  __A		     =					dj_algorithm(__UpperCamelCase						,			__UpperCamelCase					)

  # Execute the circuit on the simulator
  __A		     =					qiskit.execute(__UpperCamelCase						,			__UpperCamelCase						,			shots=1_0_0_0					)

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


if __name__ == "__main__":
       print(F'''Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}''')
       print(F'''Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}''')












"""simple docstring"""


from typing import TYPE_CHECKING

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


lowercase_								=	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       lowercase_								=	[
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

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




