



import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=1_024				, _lowerCamelCase=1_024				, _lowerCamelCase=False				, **_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    AutoTokenizer.from_pretrained(_lowerCamelCase  )
							_lowerCAmelCase     :							List[str]    =    SeqaSeqDataset(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, type_path="train"				, **_lowerCamelCase  )
							_lowerCAmelCase     :							List[Any]    =    tok.pad_token_id

							def get_lens(_lowerCamelCase  ):
														_lowerCAmelCase     :							List[Any]    =    tqdm(
														    DataLoader(_lowerCamelCase				, batch_size=512				, num_workers=8				, shuffle=_lowerCamelCase				, collate_fn=ds.collate_fn  )				, desc=str(ds.len_file  )				, )
														_lowerCAmelCase     :							str    =    []
														for batch in dl:
																					_lowerCAmelCase     :							List[Any]    =    batch["input_ids"].ne(_lowerCamelCase  ).sum(1  ).tolist()
																					_lowerCAmelCase     :							Union[str, Any]    =    batch["labels"].ne(_lowerCamelCase  ).sum(1  ).tolist()
																					if consider_target:
																												for src, tgt in zip(_lowerCamelCase				, _lowerCamelCase  ):
																																			max_lens.append(max(_lowerCamelCase				, _lowerCamelCase  )  )
																					else:
																												max_lens.extend(_lowerCamelCase  )
														return max_lens

							_lowerCAmelCase     :							Optional[int]    =    get_lens(_lowerCamelCase  )
							_lowerCAmelCase     :							Any    =    SeqaSeqDataset(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, type_path="val"				, **_lowerCamelCase  )
							_lowerCAmelCase     :							str    =    get_lens(_lowerCamelCase  )
							pickle_save(_lowerCamelCase				, train_ds.len_file  )
							pickle_save(_lowerCamelCase				, val_ds.len_file  )


if __name__ == "__main__":
						fire.Fire(save_len_file)

"""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()




