

import numpy as np
import qiskit






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  int						)    ->					qiskit.QuantumCircuit:




	'''simple docstring'''




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

    # 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)
		_UpperCamelCase :		Dict								=    np.random.randint(2						)
		if output == 1:
			oracle_qc.x(_UpperCAmelCase						)

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






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




	'''simple docstring'''




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

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

	return dj_circuit






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




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    qiskit.Aer.get_backend('aer_simulator'						)

	_UpperCamelCase :		Optional[int]								=    dj_oracle(_UpperCAmelCase				,							_UpperCAmelCase						)
	_UpperCamelCase :		int								=    dj_algorithm(_UpperCAmelCase				,							_UpperCAmelCase						)

	# Execute the circuit on the simulator
	_UpperCamelCase :		int								=    qiskit.execute(_UpperCAmelCase				,							_UpperCAmelCase				,							shots=1_0_0_0						)

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


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


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaubertForMultipleChoice""",
					    """FlaubertForQuestionAnswering""",
					    """FlaubertForQuestionAnsweringSimple""",
					    """FlaubertForSequenceClassification""",
					    """FlaubertForTokenClassification""",
					    """FlaubertModel""",
					    """FlaubertWithLMHeadModel""",
					    """FlaubertPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFFlaubertForMultipleChoice""",
					    """TFFlaubertForQuestionAnsweringSimple""",
					    """TFFlaubertForSequenceClassification""",
					    """TFFlaubertForTokenClassification""",
					    """TFFlaubertModel""",
					    """TFFlaubertPreTrainedModel""",
					    """TFFlaubertWithLMHeadModel""",
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

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





