


'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=7      ,					lowerCamelCase_=3      ,					lowerCamelCase_=18      ,					lowerCamelCase_=30      ,					lowerCamelCase_=4_00      ,					lowerCamelCase_=True      ,					lowerCamelCase_=None      ,					lowerCamelCase_=True      ,					lowerCamelCase_=None      ,					lowerCamelCase_=True      ,					lowerCamelCase_=[0.5, 0.5, 0.5]      ,					lowerCamelCase_=[0.5, 0.5, 0.5]      ,					lowerCamelCase_=False      ,					) ->							Optional[Any]:
														lowerCAmelCase__      =			size if size is not None else {'''height''': 20, '''width''': 20}
														lowerCAmelCase__      =			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
														lowerCAmelCase__      =			parent
														lowerCAmelCase__      =			batch_size
														lowerCAmelCase__      =			num_channels
														lowerCAmelCase__      =			image_size
														lowerCAmelCase__      =			min_resolution
														lowerCAmelCase__      =			max_resolution
														lowerCAmelCase__      =			do_resize
														lowerCAmelCase__      =			size
														lowerCAmelCase__      =			do_center_crop
														lowerCAmelCase__      =			crop_size
														lowerCAmelCase__      =			do_normalize
														lowerCAmelCase__      =			image_mean
														lowerCAmelCase__      =			image_std
														lowerCAmelCase__      =			do_reduce_labels







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_center_crop": self.do_center_crop,
														    "crop_size": self.crop_size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_reduce_labels": self.do_reduce_labels,
														}


def       _snake_case     (  )		->					int:
							lowerCAmelCase__      =			load_dataset('''hf-internal-testing/fixtures_ade20k'''				,					split='''test''' )

							lowerCAmelCase__      =			Image.open(dataset[0]['''file'''] )
							lowerCAmelCase__      =			Image.open(dataset[1]['''file'''] )

							return image, map


def       _snake_case     (  )		->					List[Any]:
							lowerCAmelCase__      =			load_dataset('''hf-internal-testing/fixtures_ade20k'''				,					split='''test''' )

							lowerCAmelCase__      =			Image.open(ds[0]['''file'''] )
							lowerCAmelCase__      =			Image.open(ds[1]['''file'''] )
							lowerCAmelCase__      =			Image.open(ds[2]['''file'''] )
							lowerCAmelCase__      =			Image.open(ds[3]['''file'''] )

							return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 	a__       (							a__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Optional[int]					=   BeitImageProcessor if is_vision_available() else None







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			BeitImageProcessingTester(self     )







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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__      =			self.image_processing_class(**self.image_processor_dict     )
														self.assertTrue(hasattr(lowerCamelCase_      ,					'''do_resize'''     )     )
														self.assertTrue(hasattr(lowerCamelCase_      ,					'''size'''     )     )
														self.assertTrue(hasattr(lowerCamelCase_      ,					'''do_center_crop'''     )     )
														self.assertTrue(hasattr(lowerCamelCase_      ,					'''center_crop'''     )     )
														self.assertTrue(hasattr(lowerCamelCase_      ,					'''do_normalize'''     )     )
														self.assertTrue(hasattr(lowerCamelCase_      ,					'''image_mean'''     )     )
														self.assertTrue(hasattr(lowerCamelCase_      ,					'''image_std'''     )     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			self.image_processing_class.from_dict(self.image_processor_dict     )
														self.assertEqual(image_processor.size      ,					{'''height''': 20, '''width''': 20}     )
														self.assertEqual(image_processor.crop_size      ,					{'''height''': 18, '''width''': 18}     )
														self.assertEqual(image_processor.do_reduce_labels      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			self.image_processing_class.from_dict(
														    self.image_processor_dict      ,					size=42      ,					crop_size=84      ,					reduce_labels=lowerCamelCase_     )
														self.assertEqual(image_processor.size      ,					{'''height''': 42, '''width''': 42}     )
														self.assertEqual(image_processor.crop_size      ,					{'''height''': 84, '''width''': 84}     )
														self.assertEqual(image_processor.do_reduce_labels      ,					lowerCamelCase_     )







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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														# Initialize image_processing
														lowerCAmelCase__      =			self.image_processing_class(**self.image_processor_dict     )
														# create random PIL images
														lowerCAmelCase__      =			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__      =			image_processing(image_inputs[0]      ,					return_tensors='''pt'''     ).pixel_values
														self.assertEqual(
														    encoded_images.shape      ,					(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)

														# Test batched
														lowerCAmelCase__      =			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,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														# Initialize image_processing
														lowerCAmelCase__      =			self.image_processing_class(**self.image_processor_dict     )
														# create random numpy tensors
														lowerCAmelCase__      =			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__      =			image_processing(image_inputs[0]      ,					return_tensors='''pt'''     ).pixel_values
														self.assertEqual(
														    encoded_images.shape      ,					(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)

														# Test batched
														lowerCAmelCase__      =			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,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														# Initialize image_processing
														lowerCAmelCase__      =			self.image_processing_class(**self.image_processor_dict     )
														# create random PyTorch tensors
														lowerCAmelCase__      =			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__      =			image_processing(image_inputs[0]      ,					return_tensors='''pt'''     ).pixel_values
														self.assertEqual(
														    encoded_images.shape      ,					(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)

														# Test batched
														lowerCAmelCase__      =			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,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														# Initialize image_processing
														lowerCAmelCase__      =			self.image_processing_class(**self.image_processor_dict     )
														# create random PyTorch tensors
														lowerCAmelCase__      =			prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=lowerCamelCase_      ,					torchify=lowerCamelCase_     )
														lowerCAmelCase__      =			[]
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase_      ,					torch.Tensor     )
																					maps.append(torch.zeros(image.shape[-2:]     ).long()     )

														# Test not batched input
														lowerCAmelCase__      =			image_processing(image_inputs[0]      ,					maps[0]      ,					return_tensors='''pt'''     )
														self.assertEqual(
														    encoding['''pixel_values'''].shape      ,					(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(
														    encoding['''labels'''].shape      ,					(
														        1,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(encoding['''labels'''].dtype      ,					torch.long     )
														self.assertTrue(encoding['''labels'''].min().item() >= 0     )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55     )

														# Test batched
														lowerCAmelCase__      =			image_processing(lowerCamelCase_      ,					lowerCamelCase_      ,					return_tensors='''pt'''     )
														self.assertEqual(
														    encoding['''pixel_values'''].shape      ,					(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(
														    encoding['''labels'''].shape      ,					(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(encoding['''labels'''].dtype      ,					torch.long     )
														self.assertTrue(encoding['''labels'''].min().item() >= 0     )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55     )

														# Test not batched input (PIL images)
														lowerCAmelCase__     ,      lowerCAmelCase__      =			prepare_semantic_single_inputs()

														lowerCAmelCase__      =			image_processing(lowerCamelCase_      ,					lowerCamelCase_      ,					return_tensors='''pt'''     )
														self.assertEqual(
														    encoding['''pixel_values'''].shape      ,					(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(
														    encoding['''labels'''].shape      ,					(
														        1,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(encoding['''labels'''].dtype      ,					torch.long     )
														self.assertTrue(encoding['''labels'''].min().item() >= 0     )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55     )

														# Test batched input (PIL images)
														lowerCAmelCase__     ,      lowerCAmelCase__      =			prepare_semantic_batch_inputs()

														lowerCAmelCase__      =			image_processing(lowerCamelCase_      ,					lowerCamelCase_      ,					return_tensors='''pt'''     )
														self.assertEqual(
														    encoding['''pixel_values'''].shape      ,					(
														        2,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(
														    encoding['''labels'''].shape      ,					(
														        2,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )      ,					)
														self.assertEqual(encoding['''labels'''].dtype      ,					torch.long     )
														self.assertTrue(encoding['''labels'''].min().item() >= 0     )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														# Initialize image_processing
														lowerCAmelCase__      =			self.image_processing_class(**self.image_processor_dict     )

														# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
														lowerCAmelCase__     ,      lowerCAmelCase__      =			prepare_semantic_single_inputs()
														lowerCAmelCase__      =			image_processing(lowerCamelCase_      ,					lowerCamelCase_      ,					return_tensors='''pt'''     )
														self.assertTrue(encoding['''labels'''].min().item() >= 0     )
														self.assertTrue(encoding['''labels'''].max().item() <= 1_50     )

														lowerCAmelCase__      =			True
														lowerCAmelCase__      =			image_processing(lowerCamelCase_      ,					lowerCamelCase_      ,					return_tensors='''pt'''     )
														self.assertTrue(encoding['''labels'''].min().item() >= 0     )
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55     )



'''simple docstring'''






import numpy as np
import qiskit


def       _snake_case     (  A = 8				,					A = None )		->					str:
							lowerCAmelCase__      =			np.random.default_rng(seed=A )

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

							# Quantum Circuit to simulate BB84
							lowerCAmelCase__      =			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__      =			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__      =			qiskit.execute(A				,					A				,					shots=1				,					seed_simulator=A )
							# Returns the result of measurement.
							lowerCAmelCase__      =			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__      =			''''''.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__      =			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()