

"""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)}""")












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

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





