






"""simple docstring"""


import numpy as np
import qiskit



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """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(__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)
    __A		     =					np.random.randint(2					)
    if output == 1:
      oracle_qc.x(__UpperCamelCase					)

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



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					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						,			__UpperCamelCase					):




  """simple docstring"""







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

  __A		     =					dj_oracle(__UpperCamelCase						,			__UpperCamelCase					)
  __A		     =					dj_algorithm(__UpperCamelCase						,			__UpperCamelCase					)

  # Execute the circuit on the simulator
  __A		     =					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'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


