






import numpy as np
import qiskit



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''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(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    =							np.random.randint(2	)
				if output == 1:
						oracle_qc.x(lowerCAmelCase__	)

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							qiskit.Aer.get_backend("aer_simulator"	)

		__A    =							dj_oracle(lowerCAmelCase__					,     lowerCAmelCase__	)
		__A    =							dj_algorithm(lowerCAmelCase__					,     lowerCAmelCase__	)

		# Execute the circuit on the simulator
		__A    =							qiskit.execute(lowerCAmelCase__					,     lowerCAmelCase__					,     shots=1000	)

		# 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)}""")









from typing import TYPE_CHECKING

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


snake_case_   :	Dict							      ={'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Any							      =['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =[
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Tuple							      =[
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

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

