




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				:	str			,      __lowercase				:	str			,      __lowercase				:	Optional[int]=7			,      __lowercase				:	List[str]=3			,      __lowercase				:	List[str]=30			,      __lowercase				:	List[str]=4_00			,      __lowercase				:	Any=True			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	List[str]=True			,      __lowercase				:	Optional[Any]=[0.5, 0.5, 0.5]			,      __lowercase				:	List[str]=[0.5, 0.5, 0.5]			,      __lowercase				:	Union[str, Any]=True			,      __lowercase				:	str=1 / 2_55			,      __lowercase				:	Optional[Any]=True			,      ):






							"""simple docstring"""






							snake_case_						       =	size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	num_channels
							snake_case_						       =	min_resolution
							snake_case_						       =	max_resolution
							snake_case_						       =	do_resize
							snake_case_						       =	size
							snake_case_						       =	do_normalize
							snake_case_						       =	image_mean
							snake_case_						       =	image_std
							snake_case_						       =	do_rescale
							snake_case_						       =	rescale_factor
							snake_case_						       =	do_pad






						def 			snake_case__		(		self				:	Any		):






							"""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 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	int=False		):






							"""simple docstring"""






							if not batched:
								snake_case_						       =	image_inputs[0]
								if isinstance(__lowercase			,      Image.Image		):
									snake_case_       , snake_case_						       =	image.size
								else:
									snake_case_       , snake_case_						       =	image.shape[1], image.shape[2]
								if w < h:
									snake_case_						       =	int(self.size["shortest_edge"] * h / w		)
									snake_case_						       =	self.size["shortest_edge"]
								elif w > h:
									snake_case_						       =	self.size["shortest_edge"]
									snake_case_						       =	int(self.size["shortest_edge"] * w / h		)
								else:
									snake_case_						       =	self.size["shortest_edge"]
									snake_case_						       =	self.size["shortest_edge"]

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

							return expected_height, expected_width







@require_torch
@require_vision
class 						UpperCAmelCase  (							UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  DetaImageProcessor if is_vision_available() else None






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	DetaImageProcessingTester(self		)






						@property
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(__lowercase			,      "image_mean"		)		)
							self.assertTrue(hasattr(__lowercase			,      "image_std"		)		)
							self.assertTrue(hasattr(__lowercase			,      "do_normalize"		)		)
							self.assertTrue(hasattr(__lowercase			,      "do_resize"		)		)
							self.assertTrue(hasattr(__lowercase			,      "do_rescale"		)		)
							self.assertTrue(hasattr(__lowercase			,      "do_pad"		)		)
							self.assertTrue(hasattr(__lowercase			,      "size"		)		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	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			,      __lowercase		)






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							pass






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






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

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

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(__lowercase		)

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

							# Test batched
							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(__lowercase			,      batched=__lowercase		)

							snake_case_						       =	image_processing(__lowercase			,      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 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






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

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

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(__lowercase		)

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

							# Test batched
							snake_case_						       =	image_processing(__lowercase			,      return_tensors="pt"		).pixel_values

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(__lowercase			,      batched=__lowercase		)

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






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






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

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

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(__lowercase		)

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

							# Test batched
							snake_case_						       =	image_processing(__lowercase			,      return_tensors="pt"		).pixel_values

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(__lowercase			,      batched=__lowercase		)

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






						@slow
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"			,      "r"		) as f:
								snake_case_						       =	json.loads(f.read()		)

							snake_case_						       =	{"image_id": 3_97_69, "annotations": target}

							# encode them
							snake_case_						       =	DetaImageProcessor()
							snake_case_						       =	image_processing(images=__lowercase			,      annotations=__lowercase			,      return_tensors="pt"		)

							# verify pixel values
							snake_case_						       =	torch.Size([1, 3, 8_00, 10_66]		)
							self.assertEqual(encoding["pixel_values"].shape			,      __lowercase		)

							snake_case_						       =	torch.tensor([0.2796, 0.3138, 0.3481]		)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,      __lowercase			,      atol=1E-4		)		)

							# verify area
							snake_case_						       =	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"]			,      __lowercase		)		)
							# verify boxes
							snake_case_						       =	torch.Size([6, 4]		)
							self.assertEqual(encoding["labels"][0]["boxes"].shape			,      __lowercase		)
							snake_case_						       =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,      __lowercase			,      atol=1E-3		)		)
							# verify image_id
							snake_case_						       =	torch.tensor([3_97_69]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,      __lowercase		)		)
							# verify is_crowd
							snake_case_						       =	torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,      __lowercase		)		)
							# verify class_labels
							snake_case_						       =	torch.tensor([75, 75, 63, 65, 17, 17]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,      __lowercase		)		)
							# verify orig_size
							snake_case_						       =	torch.tensor([4_80, 6_40]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,      __lowercase		)		)
							# verify size
							snake_case_						       =	torch.tensor([8_00, 10_66]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,      __lowercase		)		)






						@slow
						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"			,      "r"		) as f:
								snake_case_						       =	json.loads(f.read()		)

							snake_case_						       =	{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

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

							# verify pixel values
							snake_case_						       =	torch.Size([1, 3, 8_00, 10_66]		)
							self.assertEqual(encoding["pixel_values"].shape			,      __lowercase		)

							snake_case_						       =	torch.tensor([0.2796, 0.3138, 0.3481]		)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,      __lowercase			,      atol=1E-4		)		)

							# verify area
							snake_case_						       =	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"]			,      __lowercase		)		)
							# verify boxes
							snake_case_						       =	torch.Size([6, 4]		)
							self.assertEqual(encoding["labels"][0]["boxes"].shape			,      __lowercase		)
							snake_case_						       =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,      __lowercase			,      atol=1E-3		)		)
							# verify image_id
							snake_case_						       =	torch.tensor([3_97_69]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,      __lowercase		)		)
							# verify is_crowd
							snake_case_						       =	torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,      __lowercase		)		)
							# verify class_labels
							snake_case_						       =	torch.tensor([17, 17, 63, 75, 75, 93]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,      __lowercase		)		)
							# verify masks
							snake_case_						       =	82_28_73
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()			,      __lowercase		)
							# verify orig_size
							snake_case_						       =	torch.tensor([4_80, 6_40]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,      __lowercase		)		)
							# verify size
							snake_case_						       =	torch.tensor([8_00, 10_66]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,      __lowercase		)		)






import numpy as np
import qiskit


def 		lowerCamelCase__       (						_A = 8	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	np.random.default_rng(seed=_A				)

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

	# Quantum Circuit to simulate BB84
	snake_case_						       =	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.
	snake_case_						       =	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.
	snake_case_						       =	qiskit.execute(_A	,      _A	,      shots=1	,      seed_simulator=_A				)
	# Returns the result of measurement.
	snake_case_						       =	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.
	snake_case_						       =	"".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.
	snake_case_						       =	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()
