



'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BridgeTowerImageProcessor






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def __init__(self				,     A				,     A = True				,     A = None				,     A = 32				,     A = True				,     A = 1 / 255				,     A = True				,     A = True				,     A = [0.48145466, 0.4578275, 0.40821073]				,     A = [0.26862954, 0.26130258, 0.27577711]				,     A = True				,     A=7				,     A=30				,     A=400				,     A=3				,     )					->  int:


      """simple docstring"""



      _a											=						parent
      _a											=						do_resize
      _a											=						size if size is not None else {'''shortest_edge''': 288}
      _a											=						size_divisor
      _a											=						do_rescale
      _a											=						rescale_factor
      _a											=						do_normalize
      _a											=						do_center_crop
      _a											=						image_mean
      _a											=						image_std
      _a											=						do_pad
      _a											=						batch_size
      _a											=						num_channels
      _a											=						min_resolution
      _a											=						max_resolution



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


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }



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


      """simple docstring"""



      if not batched:
           _a											=						self.size['''shortest_edge''']
           _a											=						image_inputs[0]
           if isinstance(A				,     Image.Image ):
                _a						,					_a											=						image.size
           else:
                _a						,					_a											=						image.shape[1], image.shape[2]
           _a											=						size / min(A				,     A )
           if h < w:
                _a						,					_a											=						size, scale * w
           else:
                _a						,					_a											=						scale * h, size

           _a											=						int((1_333 / 800) * size )
           if max(A				,     A ) > max_size:
                _a											=						max_size / max(A				,     A )
                _a											=						newh * scale
                _a											=						neww * scale

           _a						,					_a											=						int(newh + 0.5 ), int(neww + 0.5 )
           _a						,					_a											=						(
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

      else:
           _a											=						[]
           for image in image_inputs:
                _a						,					_a											=						self.get_expected_values([image] )
                expected_values.append((expected_height, expected_width) )
           _a											=						max(A				,     key=lambda A : item[0] )[0]
           _a											=						max(A				,     key=lambda A : item[1] )[1]

      return expected_height, expected_width






@require_torch
@require_vision
class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Any    = BridgeTowerImageProcessor if is_vision_available() else None



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						BridgeTowerImageProcessingTester(self )



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


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(A				,     '''image_mean''' ) )
      self.assertTrue(hasattr(A				,     '''image_std''' ) )
      self.assertTrue(hasattr(A				,     '''do_normalize''' ) )
      self.assertTrue(hasattr(A				,     '''do_resize''' ) )
      self.assertTrue(hasattr(A				,     '''size''' ) )
      self.assertTrue(hasattr(A				,     '''size_divisor''' ) )



 def       a__						(self )					->  Any:


      """simple docstring"""



      pass



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     Image.Image )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						self.image_processor_tester.get_expected_values(A )
      self.assertEqual(
          encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						self.image_processor_tester.get_expected_values(A				,     batched=A )
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )				,     )



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     numpify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     np.ndarray )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						self.image_processor_tester.get_expected_values(A )
      self.assertEqual(
          encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						self.image_processor_tester.get_expected_values(A				,     batched=A )
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )				,     )



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


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     torchify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     torch.Tensor )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						self.image_processor_tester.get_expected_values(A )
      self.assertEqual(
          encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						self.image_processor_tester.get_expected_values(A				,     batched=A )
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )				,     )








'''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()



