
"""simple docstring"""
import numpy as np
import qiskit


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      qiskit.QuantumCircuit:


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

    # 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:    str		     =       np.random.randint(2			)
										if output == 1:
															oracle_qc.x(lowerCamelCase_			)

					__a:    List[Any]		     =       oracle_qc.to_gate()
					__a:    Dict		     =       """Oracle"""  # To show when we display the circuit
					return oracle_gate


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      qiskit.QuantumCircuit:


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

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

					return dj_circuit


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      qiskit.result.counts.Counts:


					'''simple docstring'''
					__a:    str		     =       qiskit.Aer.get_backend("""aer_simulator"""			)

					__a:    str		     =       dj_oracle(lowerCamelCase_						,					lowerCamelCase_			)
					__a:    str		     =       dj_algorithm(lowerCamelCase_						,					lowerCamelCase_			)

					# Execute the circuit on the simulator
					__a:    Dict		     =       qiskit.execute(lowerCamelCase_						,					lowerCamelCase_						,					shots=10_00			)

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


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_tokenizers_available, is_torch_available


lowercase		:    Tuple			=       {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    int			=       ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			lowercase		:    Optional[Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
