
"""simple docstring"""




import numpy as np
import qiskit



def     __magic_name__		(					UpperCamelCase     :		int = 8							,		UpperCamelCase     :		int | None = None     )     ->						str:
    a__		=			np.random.default_rng(seed=UpperCamelCase     )

    # Roughly 25% of the qubits will contribute to the key.
    # So we take more than we need.
    a__		=			6 * key_len
    # Measurement basis for Alice's qubits.
    a__		=			rng.integers(2							,		size=UpperCamelCase     )
    # The set of states Alice will prepare.
    a__		=			rng.integers(2							,		size=UpperCamelCase     )
    # Measurement basis for Bob's qubits.
    a__		=			rng.integers(2							,		size=UpperCamelCase     )

    # Quantum Circuit to simulate BB84
    a__		=			qiskit.QuantumCircuit(UpperCamelCase							,		name='BB84'     )

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

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

    bbaa_circ.barrier()
    bbaa_circ.measure_all()

    # Simulate the quantum circuit.
    a__		=			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.
    a__		=			qiskit.execute(UpperCamelCase							,		UpperCamelCase							,		shots=1							,		seed_simulator=UpperCamelCase     )
    # Returns the result of measurement.
    a__		=			job.result().get_counts(UpperCamelCase     ).most_frequent()

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

    # Get final key. Pad with 0 if too short, otherwise truncate.
    a__		=			gen_key[:key_len] if len(UpperCamelCase     ) >= key_len else gen_key.ljust(UpperCamelCase							,		'0'     )
    return key


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

    testmod()




"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


