



'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase_	 =						False






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 pass






@nightly
@require_torch_gpu
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				,     torch_dtype=torch.floataa )
      pipe.to(A )
      pipe.set_progress_bar_config(disable=A )

      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )

      _a											=						torch.manual_seed(0 )
      _a											=						pipe.dual_guided(
          prompt='''first prompt'''				,     image=A				,     text_to_image_strength=0.75				,     generator=A				,     guidance_scale=7.5				,     num_inference_steps=2				,     output_type='''numpy'''				,     ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(A )
           _a											=						VersatileDiffusionPipeline.from_pretrained(A				,     torch_dtype=torch.floataa )
      pipe.to(A )
      pipe.set_progress_bar_config(disable=A )

      _a											=						generator.manual_seed(0 )
      _a											=						pipe.dual_guided(
          prompt='''first prompt'''				,     image=A				,     text_to_image_strength=0.75				,     generator=A				,     guidance_scale=7.5				,     num_inference_steps=2				,     output_type='''numpy'''				,     ).images

      assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				,     torch_dtype=torch.floataa )
      pipe.to(A )
      pipe.set_progress_bar_config(disable=A )

      _a											=						'''cyberpunk 2077'''
      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
      _a											=						torch.manual_seed(0 )
      _a											=						pipe.dual_guided(
          prompt=A				,     image=A				,     text_to_image_strength=0.75				,     generator=A				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type='''numpy'''				,     ).images

      _a											=						image[0, 253:256, 253:256, -1]

      assert image.shape == (1, 512, 512, 3)
      _a											=						np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1

      _a											=						'''A painting of a squirrel eating a burger '''
      _a											=						torch.manual_seed(0 )
      _a											=						pipe.text_to_image(
          prompt=A				,     generator=A				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type='''numpy''' ).images

      _a											=						image[0, 253:256, 253:256, -1]

      assert image.shape == (1, 512, 512, 3)
      _a											=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1

      _a											=						pipe.image_variation(A				,     generator=A				,     output_type='''numpy''' ).images

      _a											=						image[0, 253:256, 253:256, -1]

      assert image.shape == (1, 512, 512, 3)
      _a											=						np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1








'''simple docstring'''





import numpy as np
import qiskit




def lowerCAmelCase						(__A = 8						,			__A = None):



     """simple docstring"""



     _a											=						np.random.default_rng(seed=__A)

     # 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=__A)
     # The set of states Alice will prepare.
     _a											=						rng.integers(2						,			size=__A)
     # Measurement basis for Bob's qubits.
     _a											=						rng.integers(2						,			size=__A)

     # Quantum Circuit to simulate BB84
     _a											=						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.
     _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(__A						,			__A						,			shots=1						,			seed_simulator=__A)
     # Returns the result of measurement.
     _a											=						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.
     _a											=						''''''.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.
     _a											=						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()



