




'''simple docstring'''







import numpy as np
import qiskit







def 	_A			(   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 	_A			(   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 	_A			(   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=1000	)

   # 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 warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


lowerCAmelCase_    	=							logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf

@dataclass
class   _snake_case( UpperCAmelCase			):
 __snake_case:       Optional[Any]									=				'''tf'''
 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Name of TPU'''}					,   )

 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Zone of TPU'''}					,   )

 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Name of Cloud TPU-enabled project'''}					,   )

 __snake_case:       float									=				field(
     default=1.0					,   metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}					,   )

 __snake_case:       bool									=				field(default=UpperCAmelCase					,   metadata={'''help''': '''Whether to activate the XLA compilation or not'''}			)






 @cached_property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      Tuple["tf.distribute.Strategy", int]:






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    logger.info('Tensorflow: setting up strategy'						)

    A__         =    tf.config.list_physical_devices('GPU'						)

    # Set to float16 at first
    if self.fpaa:
       tf.keras.mixed_precision.set_global_policy('mixed_float16'						)

    if self.no_cuda:
       A__         =    tf.distribute.OneDeviceStrategy(device='/cpu:0'						)
    else:
       try:
          if self.tpu_name:
             A__         =    tf.distribute.cluster_resolver.TPUClusterResolver(
                 self.tpu_name						,		zone=self.tpu_zone						,		project=self.gcp_project						)
          else:
             A__         =    tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
          if self.tpu_name:
             raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""						)
          else:
             A__         =    None

       if tpu:
          # Set to bfloat16 in case of TPU
          if self.fpaa:
             tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'						)

          tf.config.experimental_connect_to_cluster(a						)
          tf.tpu.experimental.initialize_tpu_system(a						)

          A__         =    tf.distribute.TPUStrategy(a						)

       elif len(a						) == 0:
          A__         =    tf.distribute.OneDeviceStrategy(device='/cpu:0'						)
       elif len(a						) == 1:
          A__         =    tf.distribute.OneDeviceStrategy(device='/gpu:0'						)
       elif len(a						) > 1:
          # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
          A__         =    tf.distribute.MirroredStrategy()
       else:
          raise ValueError('Cannot find the proper strategy, please check your environment properties.'						)

    return strategy






 @property
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      "tf.distribute.Strategy":






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    return self._setup_strategy






 @property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      int:






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    return self._setup_strategy.num_replicas_in_sync






 @property
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      int:






    """simple docstring"""


    return False  # TF Logging is handled by Keras not the Trainer






 @property
 def 	_UpperCamelCase   (self					:     Union[str, Any]						)					->      int:






    """simple docstring"""


    if self.per_gpu_train_batch_size:
       logger.warning(
           'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
           'version. Using `--per_device_train_batch_size` is preferred.'						)
    A__         =    self.per_gpu_train_batch_size or self.per_device_train_batch_size
    return per_device_batch_size * self.n_replicas






 @property
 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      int:






    """simple docstring"""


    if self.per_gpu_eval_batch_size:
       logger.warning(
           'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
           'version. Using `--per_device_eval_batch_size` is preferred.'						)
    A__         =    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
    return per_device_batch_size * self.n_replicas






 @property
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      int:






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    warnings.warn(
        'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'						,		a						,		)
    return self._setup_strategy.num_replicas_in_sync


