

"""simple docstring"""

import numpy as np
import qiskit





def 					__UpperCamelCase (     a__:			str			,						a__:			int      )					->      qiskit.QuantumCircuit:




					"""simple docstring"""



					__lowercase        =      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:
										__lowercase        =      np.random.randint(1			,						2**num_qubits      )
										# Next, format 'b' as a binary string of length 'n', padded with zeros:
										__lowercase        =      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)
										__lowercase        =      np.random.randint(2      )
										if output == 1:
															oracle_qc.x(a__      )

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





def 					__UpperCamelCase (     a__:			qiskit.QuantumCircuit			,						a__:			int      )					->      qiskit.QuantumCircuit:




					"""simple docstring"""



					__lowercase        =      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 					__UpperCamelCase (     a__:			str			,						a__:			int      )					->      qiskit.result.counts.Counts:




					"""simple docstring"""



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

					__lowercase        =      dj_oracle(a__			,						a__      )
					__lowercase        =      dj_algorithm(a__			,						a__      )

					# Execute the circuit on the simulator
					__lowercase        =      qiskit.execute(a__			,						a__			,						shots=10_00      )

					# 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)}""")





"""simple docstring"""

from typing import TYPE_CHECKING

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


a__			:						Tuple							=			{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[str]							=			["""SegformerFeatureExtractor"""]
							a__			:						str							=			["""SegformerImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[Any]							=			[
							    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """SegformerDecodeHead""",
							    """SegformerForImageClassification""",
							    """SegformerForSemanticSegmentation""",
							    """SegformerLayer""",
							    """SegformerModel""",
							    """SegformerPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[Any]							=			[
							    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSegformerDecodeHead""",
							    """TFSegformerForImageClassification""",
							    """TFSegformerForSemanticSegmentation""",
							    """TFSegformerModel""",
							    """TFSegformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							a__			:						Any							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


