


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
		import torch

if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


lowerCamelCase						=		logging.get_logger(__name__)





@dataclass
class 					_a							(				SCREAMING_SNAKE_CASE_):
	_a  :  List[str]          =							[
	    "no_inference",
	    "no_cuda",
	    "no_tpu",
	    "no_speed",
	    "no_memory",
	    "no_env_print",
	    "no_multi_process",
	]




	def __init__(					self						:		Dict		,					**_SCREAMING_SNAKE_CASE						:		List[Any]		)->       List[Any]:
			for deprecated_arg in self.deprecated_args:
					if deprecated_arg in kwargs:
							lowerCAmelCase__   :			Tuple         =					deprecated_arg[3:]
							setattr(self		,					a__		,					not kwargs.pop(a__		)		)
							logger.warning(
							    F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
							    F' {positive_arg}={kwargs[positive_arg]}'		)

			lowerCAmelCase__   :			List[Any]         =					kwargs.pop('''torchscript'''		,					self.torchscript		)
			lowerCAmelCase__   :			List[str]         =					kwargs.pop('''torch_xla_tpu_print_metrics'''		,					self.torch_xla_tpu_print_metrics		)
			lowerCAmelCase__   :			List[str]         =					kwargs.pop('''fp16_opt_level'''		,					self.fpaa_opt_level		)
			super().__init__(**a__		)

	_a  :  bool          =							field(default=SCREAMING_SNAKE_CASE_     ,						metadata={'''help''': '''Trace the models using torchscript'''})
	_a  :  bool          =							field(default=SCREAMING_SNAKE_CASE_     ,						metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''})
	_a  :  str          =							field(
	    default='''O1'''     ,						metadata={
	        '''help''': (
	            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
	            '''See details at https://nvidia.github.io/apex/amp.html'''
	        )
	    }     ,						)




	@cached_property
	def 							UpperCAmelCase__(					self						:		Tuple		)->       Optional[int]:
			requires_backends(self		,					['''torch''']		)
			logger.info('''PyTorch: setting up devices'''		)
			if not self.cuda:
					lowerCAmelCase__   :			int         =					torch.device('''cpu'''		)
					lowerCAmelCase__   :			Union[str, Any]         =					0
			elif is_torch_tpu_available():
					lowerCAmelCase__   :			str         =					xm.xla_device()
					lowerCAmelCase__   :			Optional[Any]         =					0
			else:
					lowerCAmelCase__   :			Union[str, Any]         =					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''		)
					lowerCAmelCase__   :			Optional[Any]         =					torch.cuda.device_count()
			return device, n_gpu




	@property
	def 							UpperCAmelCase__(					self						:		Dict		)->       Union[str, Any]:
			return is_torch_tpu_available() and self.tpu




	@property
	def 							UpperCAmelCase__(					self						:		Tuple		)->       str:
			requires_backends(self		,					['''torch''']		)
			# TODO(PVP): currently only single GPU is supported
			return torch.cuda.current_device()




	@property
	def 							UpperCAmelCase__(					self						:		Optional[int]		)->       str:
			requires_backends(self		,					['''torch''']		)
			return self._setup_devices[0]




	@property
	def 							UpperCAmelCase__(					self						:		Dict		)->       Optional[Any]:
			requires_backends(self		,					['''torch''']		)
			return self._setup_devices[1]




	@property
	def 							UpperCAmelCase__(					self						:		Dict		)->       Union[str, Any]:
			return self.n_gpu > 0





"""simple docstring"""




import numpy as np
import qiskit





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 8			,_lowerCamelCase						:       int | None = None	)       ->      str:
  _lowerCAmelCase						:       int  			=     np.random.default_rng(seed=_lowerCamelCase	)

  # Roughly 25% of the qubits will contribute to the key.
  # So we take more than we need.
  _lowerCAmelCase						:       Tuple  			=     6 * key_len
  # Measurement basis for Alice's qubits.
  _lowerCAmelCase						:       Dict  			=     rng.integers(2			,size=_lowerCamelCase	)
  # The set of states Alice will prepare.
  _lowerCAmelCase						:       Tuple  			=     rng.integers(2			,size=_lowerCamelCase	)
  # Measurement basis for Bob's qubits.
  _lowerCAmelCase						:       Union[str, Any]  			=     rng.integers(2			,size=_lowerCamelCase	)

  # Quantum Circuit to simulate BB84
  _lowerCAmelCase						:       Dict  			=     qiskit.QuantumCircuit(_lowerCamelCase			,name="""BB84"""	)

  # Alice prepares her qubits according to rules above.
  for index, _ in enumerate(_lowerCamelCase	):
    if alice_state[index] == 1:
      bbaa_circ.x(_lowerCamelCase	)
    if alice_basis[index] == 1:
      bbaa_circ.h(_lowerCamelCase	)
  bbaa_circ.barrier()

  # Bob measures the received qubits according to rules above.
  for index, _ in enumerate(_lowerCamelCase	):
    if bob_basis[index] == 1:
      bbaa_circ.h(_lowerCamelCase	)

  bbaa_circ.barrier()
  bbaa_circ.measure_all()

  # Simulate the quantum circuit.
  _lowerCAmelCase						:       int  			=     qiskit.Aer.get_backend("""aer_simulator"""	)
  # We only need to run one shot because the key is unique.
  # Multiple shots will produce the same key.
  _lowerCAmelCase						:       List[str]  			=     qiskit.execute(_lowerCamelCase			,_lowerCamelCase			,shots=1			,seed_simulator=_lowerCamelCase	)
  # Returns the result of measurement.
  _lowerCAmelCase						:       List[Any]  			=     job.result().get_counts(_lowerCamelCase	).most_frequent()

  # Extracting the generated key from the simulation results.
  # Only keep measurement results where Alice and Bob chose the same basis.
  _lowerCAmelCase						:       str  			=     """""".join(
      [
          result_bit
          for alice_basis_bit, bob_basis_bit, result_bit in zip(
              _lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
          if alice_basis_bit == bob_basis_bit
      ]	)

  # Get final key. Pad with 0 if too short, otherwise truncate.
  _lowerCAmelCase						:       List[Any]  			=     gen_key[:key_len] if len(_lowerCamelCase	) >= key_len else gen_key.ljust(_lowerCamelCase			,"""0"""	)
  return key


if __name__ == "__main__":
     print(F"""The generated key is : {bbaa(8, seed=0)}""")
     from doctest import testmod

     testmod()




