




'''simple docstring'''

import numpy as np
import qiskit




def    snake_case__     (  a       ,      a	)			->       Tuple:





			'''simple docstring'''






			snake_case__          =						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:
						snake_case__          =						np.random.randint(1       ,      2**num_qubits	)
						# Next, format 'b' as a binary string of length 'n', padded with zeros:
						snake_case__          =						format(A__       ,      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(A__	):
									if bit == "1":
												oracle_qc.x(A__	)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
						for index in range(A__	):
									oracle_qc.cx(A__       ,      A__	)
						# Next, place the final X-gates
						for index, bit in enumerate(A__	):
									if bit == "1":
												oracle_qc.x(A__	)

    # 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)
						snake_case__          =						np.random.randint(2	)
						if output == 1:
									oracle_qc.x(A__	)

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




def    snake_case__     (  a       ,      a	)			->       List[Any]:





			'''simple docstring'''






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

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

			return dj_circuit




def    snake_case__     (  a       ,      a	)			->       List[Any]:





			'''simple docstring'''






			snake_case__          =						qiskit.Aer.get_backend("""aer_simulator"""	)

			snake_case__          =						dj_oracle(A__       ,      A__	)
			snake_case__          =						dj_algorithm(A__       ,      A__	)

			# Execute the circuit on the simulator
			snake_case__          =						qiskit.execute(A__       ,      A__       ,      shots=1000	)

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


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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case				=			{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "NystromformerForMaskedLM",
          "NystromformerForMultipleChoice",
          "NystromformerForQuestionAnswering",
          "NystromformerForSequenceClassification",
          "NystromformerForTokenClassification",
          "NystromformerLayer",
          "NystromformerModel",
          "NystromformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nystromformer import (
                NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                NystromformerForMaskedLM,
                NystromformerForMultipleChoice,
                NystromformerForQuestionAnswering,
                NystromformerForSequenceClassification,
                NystromformerForTokenClassification,
                NystromformerLayer,
                NystromformerModel,
                NystromformerPreTrainedModel,
            )


else:
      import sys

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



