


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       _UpperCamelCase    (					unittest.TestCase				):

    """simple docstring"""







    def __init__(						self				,   a__				,   a__=7				,   a__=3				,   a__=30				,   a__=400				,   a__=True				,   a__=None				,   a__=True				,   a__=[0.5, 0.5, 0.5]				,   a__=[0.5, 0.5, 0.5]				,   a__=True				,   a__=1 / 255				,   a__=True				,   )	->				Tuple:
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     A								=				size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
     A								=				parent
     A								=				batch_size
     A								=				num_channels
     A								=				min_resolution
     A								=				max_resolution
     A								=				do_resize
     A								=				size
     A								=				do_normalize
     A								=				image_mean
     A								=				image_std
     A								=				do_rescale
     A								=				rescale_factor
     A								=				do_pad

    def      _UpperCAmelCase					(						self   )	->				Any:
     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      _UpperCAmelCase					(						self				,   a__				,   a__=False   )	->				Tuple:
     if not batched:
      A								=				image_inputs[0]
      if isinstance(a__				,   Image.Image   ):
       A					,			A								=				image.size
      else:
       A					,			A								=				image.shape[1], image.shape[2]
      if w < h:
       A								=				int(self.size["""shortest_edge"""] * h / w   )
       A								=				self.size["""shortest_edge"""]
      elif w > h:
       A								=				self.size["""shortest_edge"""]
       A								=				int(self.size["""shortest_edge"""] * w / h   )
      else:
       A								=				self.size["""shortest_edge"""]
       A								=				self.size["""shortest_edge"""]

     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       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

    """simple docstring"""







    lowerCAmelCase 		= DetaImageProcessor if is_vision_available() else None

    def      _UpperCAmelCase					(						self   )	->				Dict:
     A								=				DetaImageProcessingTester(self   )

    @property
    def      _UpperCAmelCase					(						self   )	->				List[Any]:
     return self.image_processor_tester.prepare_image_processor_dict()

    def      _UpperCAmelCase					(						self   )	->				List[Any]:
     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__				,   """do_rescale"""   )   )
     self.assertTrue(hasattr(a__				,   """do_pad"""   )   )
     self.assertTrue(hasattr(a__				,   """size"""   )   )

    def      _UpperCAmelCase					(						self   )	->				str:
     A								=				self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size				,   {"""shortest_edge""": 18, """longest_edge""": 1333}   )
     self.assertEqual(image_processor.do_pad				,   a__   )

    def      _UpperCAmelCase					(						self   )	->				int:
     pass

    def      _UpperCAmelCase					(						self   )	->				Optional[int]:
     # Initialize image_processing
     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					,			A								=				self.image_processor_tester.get_expected_values(a__				,   batched=a__   )

     A								=				image_processing(a__				,   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      _UpperCAmelCase					(						self   )	->				str:
     # Initialize image_processing
     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      _UpperCAmelCase					(						self   )	->				List[str]:
     # Initialize image_processing
     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,
         )				,   )

    @slow
    def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
     # prepare image and target
     A								=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
     with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""				,   """r"""   ) as f:
      A								=				json.loads(f.read()   )

     A								=				{"""image_id""": 3_9769, """annotations""": target}

     # encode them
     A								=				DetaImageProcessor()
     A								=				image_processing(images=a__				,   annotations=a__				,   return_tensors="""pt"""   )

     # verify pixel values
     A								=				torch.Size([1, 3, 800, 1066]   )
     self.assertEqual(encoding["""pixel_values"""].shape				,   a__   )

     A								=				torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,   a__				,   atol=1e-4   )   )

     # verify area
     A								=				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"""]				,   a__   )   )
     # verify boxes
     A								=				torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,   a__   )
     A								=				torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,   a__				,   atol=1e-3   )   )
     # verify image_id
     A								=				torch.tensor([3_9769]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,   a__   )   )
     # verify is_crowd
     A								=				torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,   a__   )   )
     # verify class_labels
     A								=				torch.tensor([75, 75, 63, 65, 17, 17]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,   a__   )   )
     # verify orig_size
     A								=				torch.tensor([480, 640]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,   a__   )   )
     # verify size
     A								=				torch.tensor([800, 1066]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,   a__   )   )

    @slow
    def      _UpperCAmelCase					(						self   )	->				List[Any]:
     # prepare image, target and masks_path
     A								=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
     with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""				,   """r"""   ) as f:
      A								=				json.loads(f.read()   )

     A								=				{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

     A								=				pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

     # encode them
     A								=				DetaImageProcessor(format="""coco_panoptic"""   )
     A								=				image_processing(images=a__				,   annotations=a__				,   masks_path=a__				,   return_tensors="""pt"""   )

     # verify pixel values
     A								=				torch.Size([1, 3, 800, 1066]   )
     self.assertEqual(encoding["""pixel_values"""].shape				,   a__   )

     A								=				torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,   a__				,   atol=1e-4   )   )

     # verify area
     A								=				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"""]				,   a__   )   )
     # verify boxes
     A								=				torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,   a__   )
     A								=				torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,   a__				,   atol=1e-3   )   )
     # verify image_id
     A								=				torch.tensor([3_9769]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,   a__   )   )
     # verify is_crowd
     A								=				torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,   a__   )   )
     # verify class_labels
     A								=				torch.tensor([17, 17, 63, 75, 75, 93]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,   a__   )   )
     # verify masks
     A								=				82_2873
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,   a__   )
     # verify orig_size
     A								=				torch.tensor([480, 640]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,   a__   )   )
     # verify size
     A								=				torch.tensor([800, 1066]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,   a__   )   )







import qiskit





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 2						) ->   qiskit.result.counts.Counts:





	"""simple docstring"""







	A								=				qubits

	# Using Aer's simulator
	A								=				qiskit.Aer.get_backend("""aer_simulator"""						)

	# Creating a Quantum Circuit acting on the q register
	A								=				qiskit.QuantumCircuit(UpperCamelCase__							,					UpperCamelCase__						)

	# Adding a H gate on qubit 0 (now q0 in superposition)
	circuit.h(0						)

	for i in range(1							,					UpperCamelCase__						):
		# Adding CX (CNOT) gate
		circuit.cx(i - 1							,					UpperCamelCase__						)

	# Mapping the quantum measurement to the classical bits
	circuit.measure(list(range(UpperCamelCase__						)						)							,					list(range(UpperCamelCase__						)						)						)

	# Now measuring any one qubit would affect other qubits to collapse
	# their super position and have same state as the measured one.

	# Executing the circuit on the simulator
	A								=				qiskit.execute(UpperCamelCase__							,					UpperCamelCase__							,					shots=10_00						)

	return job.result().get_counts(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''Total count for various states are: {quantum_entanglement(3)}''')



