

import os

import pytest
from attr import dataclass


__UpperCAmelCase								=			"""us-east-1"""  # defaults region



@dataclass
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      str
  lowerCamelCase   :      Optional[Any]										="arn:aws:iam::558105141721:role/sagemaker_execution_role"
  lowerCamelCase   :      Optional[int]										={
      "task_name": "mnli",
      "per_device_train_batch_size": 16,
      "per_device_eval_batch_size": 16,
      "do_train": True,
      "do_eval": True,
      "do_predict": True,
      "output_dir": "/opt/ml/model",
      "overwrite_output_dir": True,
      "max_steps": 500,
      "save_steps": 5500,
  }
  lowerCamelCase   :      List[str]										={**hyperparameters, "max_steps": 1000}







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     if self.framework == "pytorch":
        return [
            {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
            {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
            {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
        ]
     else:
        return [
            {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
            {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
            {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
        ]







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     str:






     """simple docstring"""







     return f'''{self.framework}-transfromers-test'''







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     str:






     """simple docstring"""







     return f'''./tests/sagemaker/scripts/{self.framework}'''







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






     """simple docstring"""







     if self.framework == "pytorch":
        return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
     else:
        return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"






@pytest.fixture(scope="""class"""					)
def      snake_case_				(__A					:						List[Any]					)      ->     Optional[Any]:
   __lowerCAmelCase  :      int    =						SageMakerTestEnvironment(framework=request.cls.framework					)





import numpy as np
import qiskit





def      snake_case_				(__A					:						int = 8 ,       __A					:						int | None = None					)      ->     str:
   __lowerCAmelCase  :      List[Any]    =						np.random.default_rng(seed=__A					)

   # 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  :      List[Any]    =						rng.integers(2 ,       size=__A					)
   # The set of states Alice will prepare.
   __lowerCAmelCase  :      List[str]    =						rng.integers(2 ,       size=__A					)
   # Measurement basis for Bob's qubits.
   __lowerCAmelCase  :      List[Any]    =						rng.integers(2 ,       size=__A					)

   # Quantum Circuit to simulate BB84
   __lowerCAmelCase  :      int    =						qiskit.QuantumCircuit(__A ,       name="""BB84"""					)

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

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

   bbaa_circ.barrier()
   bbaa_circ.measure_all()

   # Simulate the quantum circuit.
   __lowerCAmelCase  :      Optional[Any]    =						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  :      Optional[Any]    =						qiskit.execute(__A ,       __A ,       shots=1 ,       seed_simulator=__A					)
   # Returns the result of measurement.
   __lowerCAmelCase  :      List[Any]    =						job.result().get_counts(__A					).most_frequent()

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

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


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

   testmod()


