

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Any=7	,							lowerCAmelCase			:   Any=3	,							lowerCAmelCase			:   List[str]=30	,							lowerCAmelCase			:   List[str]=4_00	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[Any]=[0.5, 0.5, 0.5]	,							lowerCAmelCase			:   Any=[0.5, 0.5, 0.5]	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   int=1 / 2_55	,							lowerCAmelCase			:   int=True	,							)							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
     __lowerCAmelCase  :      int    =						parent
     __lowerCAmelCase  :      str    =						batch_size
     __lowerCAmelCase  :      Optional[int]    =						num_channels
     __lowerCAmelCase  :      Tuple    =						min_resolution
     __lowerCAmelCase  :      Optional[int]    =						max_resolution
     __lowerCAmelCase  :      Optional[int]    =						do_resize
     __lowerCAmelCase  :      Optional[Any]    =						size
     __lowerCAmelCase  :      Optional[Any]    =						do_normalize
     __lowerCAmelCase  :      List[Any]    =						image_mean
     __lowerCAmelCase  :      Dict    =						image_std
     __lowerCAmelCase  :      Optional[int]    =						do_rescale
     __lowerCAmelCase  :      Any    =						rescale_factor
     __lowerCAmelCase  :      List[Any]    =						do_pad







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   str=False   )							->     int:






     """simple docstring"""







     if not batched:
        __lowerCAmelCase  :      Optional[int]    =						image_inputs[0]
        if isinstance(lowerCAmelCase	,							Image.Image   ):
           __lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						image.size
        else:
           __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						image.shape[1], image.shape[2]
        if w < h:
           __lowerCAmelCase  :      Optional[Any]    =						int(self.size["""shortest_edge"""] * h / w   )
           __lowerCAmelCase  :      Tuple    =						self.size["""shortest_edge"""]
        elif w > h:
           __lowerCAmelCase  :      Any    =						self.size["""shortest_edge"""]
           __lowerCAmelCase  :      int    =						int(self.size["""shortest_edge"""] * w / h   )
        else:
           __lowerCAmelCase  :      List[str]    =						self.size["""shortest_edge"""]
           __lowerCAmelCase  :      Union[str, Any]    =						self.size["""shortest_edge"""]

     else:
        __lowerCAmelCase  :      int    =						[]
        for image in image_inputs:
           __lowerCAmelCase   ,__lowerCAmelCase  :      int    =						self.get_expected_values([image]   )
           expected_values.append((expected_height, expected_width)   )
        __lowerCAmelCase  :      Dict    =						max(lowerCAmelCase	,							key=lambda lowerCAmelCase   : item[0]   )[0]
        __lowerCAmelCase  :      Optional[int]    =						max(lowerCAmelCase	,							key=lambda lowerCAmelCase   : item[1]   )[1]

     return expected_height, expected_width






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=DetaImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						DetaImageProcessingTester(self   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[Any]:






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""image_mean"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""image_std"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_normalize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_rescale"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_pad"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""size"""   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 18, """longest_edge""": 13_33}   )
     self.assertEqual(image_processor.do_pad	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      str    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							Image.Image   )

     # Test not batched input
     __lowerCAmelCase  :      str    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values

     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						self.image_processor_tester.get_expected_values(lowerCAmelCase   )

     self.assertEqual(
         encoded_images.shape	,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,							)

     # Test batched
     __lowerCAmelCase   ,__lowerCAmelCase  :      str    =						self.image_processor_tester.get_expected_values(lowerCAmelCase	,							batched=lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values
     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowerCAmelCase  :      Optional[Any]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							numpify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							np.ndarray   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[Any]    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values

     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						self.image_processor_tester.get_expected_values(lowerCAmelCase   )

     self.assertEqual(
         encoded_images.shape	,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,							)

     # Test batched
     __lowerCAmelCase  :      List[str]    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values

     __lowerCAmelCase   ,__lowerCAmelCase  :      int    =						self.image_processor_tester.get_expected_values(lowerCAmelCase	,							batched=lowerCAmelCase   )

     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowerCAmelCase  :      List[str]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							torchify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							torch.Tensor   )

     # Test not batched input
     __lowerCAmelCase  :      Dict    =						image_processing(image_inputs[0]	,							return_tensors="""pt"""   ).pixel_values

     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						self.image_processor_tester.get_expected_values(lowerCAmelCase   )

     self.assertEqual(
         encoded_images.shape	,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,							)

     # Test batched
     __lowerCAmelCase  :      str    =						image_processing(lowerCAmelCase	,							return_tensors="""pt"""   ).pixel_values

     __lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						self.image_processor_tester.get_expected_values(lowerCAmelCase	,							batched=lowerCAmelCase   )

     self.assertEqual(
         encoded_images.shape	,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,							)







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
     with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""	,							"""r"""   ) as f:
        __lowerCAmelCase  :      Optional[int]    =						json.loads(f.read()   )

     __lowerCAmelCase  :      int    =						{"""image_id""": 3_97_69, """annotations""": target}

     # encode them
     __lowerCAmelCase  :      Optional[int]    =						DetaImageProcessor()
     __lowerCAmelCase  :      Optional[int]    =						image_processing(images=lowerCAmelCase	,							annotations=lowerCAmelCase	,							return_tensors="""pt"""   )

     # verify pixel values
     __lowerCAmelCase  :      Optional[int]    =						torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["""pixel_values"""].shape	,							lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						torch.tensor([0.2796, 0.3138, 0.3481]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )

     # verify area
     __lowerCAmelCase  :      Tuple    =						torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,							lowerCAmelCase   )   )
     # verify boxes
     __lowerCAmelCase  :      List[str]    =						torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,							lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,							lowerCAmelCase	,							atol=1e-3   )   )
     # verify image_id
     __lowerCAmelCase  :      Tuple    =						torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,							lowerCAmelCase   )   )
     # verify is_crowd
     __lowerCAmelCase  :      Union[str, Any]    =						torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,							lowerCAmelCase   )   )
     # verify class_labels
     __lowerCAmelCase  :      int    =						torch.tensor([75, 75, 63, 65, 17, 17]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,							lowerCAmelCase   )   )
     # verify orig_size
     __lowerCAmelCase  :      Dict    =						torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,							lowerCAmelCase   )   )
     # verify size
     __lowerCAmelCase  :      Optional[int]    =						torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,							lowerCAmelCase   )   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
     with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""	,							"""r"""   ) as f:
        __lowerCAmelCase  :      Optional[Any]    =						json.loads(f.read()   )

     __lowerCAmelCase  :      List[str]    =						{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

     __lowerCAmelCase  :      Union[str, Any]    =						pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

     # encode them
     __lowerCAmelCase  :      int    =						DetaImageProcessor(format="""coco_panoptic"""   )
     __lowerCAmelCase  :      str    =						image_processing(images=lowerCAmelCase	,							annotations=lowerCAmelCase	,							masks_path=lowerCAmelCase	,							return_tensors="""pt"""   )

     # verify pixel values
     __lowerCAmelCase  :      List[Any]    =						torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["""pixel_values"""].shape	,							lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						torch.tensor([0.2796, 0.3138, 0.3481]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]	,							lowerCAmelCase	,							atol=1e-4   )   )

     # verify area
     __lowerCAmelCase  :      str    =						torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,							lowerCAmelCase   )   )
     # verify boxes
     __lowerCAmelCase  :      Tuple    =						torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,							lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,							lowerCAmelCase	,							atol=1e-3   )   )
     # verify image_id
     __lowerCAmelCase  :      List[str]    =						torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,							lowerCAmelCase   )   )
     # verify is_crowd
     __lowerCAmelCase  :      int    =						torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,							lowerCAmelCase   )   )
     # verify class_labels
     __lowerCAmelCase  :      Any    =						torch.tensor([17, 17, 63, 75, 75, 93]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,							lowerCAmelCase   )   )
     # verify masks
     __lowerCAmelCase  :      Any    =						82_28_73
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()	,							lowerCAmelCase   )
     # verify orig_size
     __lowerCAmelCase  :      Dict    =						torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,							lowerCAmelCase   )   )
     # verify size
     __lowerCAmelCase  :      Dict    =						torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,							lowerCAmelCase   )   )





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()


