
'''simple docstring'''
import numpy as np
import qiskit







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							qiskit.QuantumCircuit:





  """simple docstring"""

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

    # 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		:  List[Any]       				=		np.random.randint(2			)
    if output == 1:
      oracle_qc.x(__SCREAMING_SNAKE_CASE			)

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	qiskit.QuantumCircuit      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							qiskit.QuantumCircuit:





  """simple docstring"""

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

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

  return dj_circuit







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							qiskit.result.counts.Counts:





  """simple docstring"""

  __A		:  List[str]       				=		qiskit.Aer.get_backend("""aer_simulator"""			)

  __A		:  Dict       				=		dj_oracle(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		dj_algorithm(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)

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

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


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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__ :		str					={'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

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

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         '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:
     A__ :		List[str]					=[
         'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFT5EncoderModel',
         'TFT5ForConditionalGeneration',
         'TFT5Model',
         'TFT5PreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         '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

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



