




import qiskit



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   int	):




 """simple docstring"""







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

 # Create a Quantum Circuit acting on the q register
 UpperCAmelCase__							 =						qiskit.QuantumCircuit(_lowerCAmelCase						,							_lowerCAmelCase	)

 # Apply X (NOT) Gate to Qubits 0 & 1
 circuit.x(0	)
 circuit.x(1	)

 # Map the quantum measurement to the classical bits
 circuit.measure([0, 1]						,							[0, 1]	)

 # Execute the circuit on the qasm simulator
 UpperCAmelCase__							 =						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__":
  _lowerCAmelCase						:   int		=   single_qubit_measure(2, 2)
  print(F'''Total count for various states are: {counts}''')











import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						BigBirdConfig.from_json_file(_lowerCAmelCase	)
 print(F'''Building PyTorch model from configuration: {config}'''	)

 if is_trivia_qa:
  UpperCAmelCase__							 =						BigBirdForQuestionAnswering(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						BigBirdForPreTraining(_lowerCAmelCase	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_lowerCAmelCase						,							_lowerCAmelCase						,							is_trivia_qa=_lowerCAmelCase	)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
 model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   List[Any]		=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--big_bird_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained BERT model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
  )
  _lowerCAmelCase						:   Dict		=   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )





