"""simple docstring"""


import numpy as np
import qiskit







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> qiskit.QuantumCircuit:
     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(SCREAMING_SNAKE_CASE__		,   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(SCREAMING_SNAKE_CASE__   ):
               if bit == "1":
                    oracle_qc.x(SCREAMING_SNAKE_CASE__   )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
          for index in range(SCREAMING_SNAKE_CASE__   ):
               oracle_qc.cx(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__   )
          # Next, place the final X-gates
          for index, bit in enumerate(SCREAMING_SNAKE_CASE__   ):
               if bit == "1":
                    oracle_qc.x(SCREAMING_SNAKE_CASE__   )

    # 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(SCREAMING_SNAKE_CASE__   )

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







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

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

     return dj_circuit







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> qiskit.result.counts.Counts:
     snake_case_   			=   qiskit.Aer.get_backend('aer_simulator'   )

     snake_case_   			=   dj_oracle(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__   )
     snake_case_   			=   dj_algorithm(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__   )

     # Execute the circuit on the simulator
     snake_case_   			=   qiskit.execute(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__		,   shots=1000   )

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


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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_  :							Any            =	{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							str            =	[
					    "DPRContextEncoderTokenizerFast",
					    "DPRQuestionEncoderTokenizerFast",
					    "DPRReaderTokenizerFast",
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPRContextEncoder",
					    "DPRPretrainedContextEncoder",
					    "DPRPreTrainedModel",
					    "DPRPretrainedQuestionEncoder",
					    "DPRPretrainedReader",
					    "DPRQuestionEncoder",
					    "DPRReader",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Dict            =	[
					    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDPRContextEncoder",
					    "TFDPRPretrainedContextEncoder",
					    "TFDPRPretrainedQuestionEncoder",
					    "TFDPRPretrainedReader",
					    "TFDPRQuestionEncoder",
					    "TFDPRReader",
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

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





