

"""simple docstring"""




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

    # 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(lowercase__ )

					A     =   oracle_qc.to_gate()
					A     =   "Oracle"  # To show when we display the circuit
					return oracle_gate
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   qiskit.QuantumCircuit(num_qubits + 1     ,				lowercase__ )
					# Set up the output qubit:
					dj_circuit.x(lowercase__ )
					dj_circuit.h(lowercase__ )
					# And set up the input register:
					for qubit in range(lowercase__ ):
										dj_circuit.h(lowercase__ )
					# Let's append the oracle gate to our circuit:
					dj_circuit.append(lowercase__     ,				range(num_qubits + 1 ) )
					# Finally, perform the H-gates again and measure:
					for qubit in range(lowercase__ ):
										dj_circuit.h(lowercase__ )

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

					return dj_circuit
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   qiskit.Aer.get_backend("aer_simulator" )

					A     =   dj_oracle(lowercase__     ,				lowercase__ )
					A     =   dj_algorithm(lowercase__     ,				lowercase__ )

					# Execute the circuit on the simulator
					A     =   qiskit.execute(lowercase__     ,				lowercase__     ,				shots=1_000 )

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


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,
)


lowerCAmelCase__        =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''DebertaV2TokenizerFast''']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDebertaV2ForMaskedLM''',
						    '''TFDebertaV2ForQuestionAnswering''',
						    '''TFDebertaV2ForSequenceClassification''',
						    '''TFDebertaV2ForTokenClassification''',
						    '''TFDebertaV2Model''',
						    '''TFDebertaV2PreTrainedModel''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DebertaV2ForMaskedLM''',
						    '''DebertaV2ForMultipleChoice''',
						    '''DebertaV2ForQuestionAnswering''',
						    '''DebertaV2ForSequenceClassification''',
						    '''DebertaV2ForTokenClassification''',
						    '''DebertaV2Model''',
						    '''DebertaV2PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

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






