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





def  lowerCAmelCase__     (      _UpperCamelCase							:					str			,      _UpperCamelCase							:					int       )       ->					qiskit.QuantumCircuit:





						"""simple docstring"""






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

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





def  lowerCAmelCase__     (      _UpperCamelCase							:					qiskit.QuantumCircuit			,      _UpperCamelCase							:					int       )       ->					qiskit.QuantumCircuit:





						"""simple docstring"""






						snake_case             =      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"""






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

						snake_case             =      dj_oracle(_UpperCamelCase			,      _UpperCamelCase       )
						snake_case             =      dj_algorithm(_UpperCamelCase			,      _UpperCamelCase       )

						# Execute the circuit on the simulator
						snake_case             =      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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

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


