




import unittest

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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VivitImageProcessor


class  A		(  unittest.TestCase     ):



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__=7,	UpperCamelCase__=3,	UpperCamelCase__=10,	UpperCamelCase__=18,	UpperCamelCase__=30,	UpperCamelCase__=400,	UpperCamelCase__=True,	UpperCamelCase__=None,	UpperCamelCase__=True,	UpperCamelCase__=[0.5, 0.5, 0.5],	UpperCamelCase__=[0.5, 0.5, 0.5],	UpperCamelCase__=None,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  size if size is not None else {'''shortest_edge''': 18}
								lowerCAmelCase_      			=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

								lowerCAmelCase_      			=  parent
								lowerCAmelCase_      			=  batch_size
								lowerCAmelCase_      			=  num_channels
								lowerCAmelCase_      			=  num_frames
								lowerCAmelCase_      			=  image_size
								lowerCAmelCase_      			=  min_resolution
								lowerCAmelCase_      			=  max_resolution
								lowerCAmelCase_      			=  do_resize
								lowerCAmelCase_      			=  size
								lowerCAmelCase_      			=  do_normalize
								lowerCAmelCase_      			=  image_mean
								lowerCAmelCase_      			=  image_std
								lowerCAmelCase_      			=  crop_size







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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,
								    "crop_size": self.crop_size,
								}







@require_torch
@require_vision
class  A		(  __UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   VivitImageProcessor if is_vision_available() else None



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  VivitImageProcessingTester(self   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self.image_processor_tester.prepare_image_processor_dict()



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''image_mean'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''image_std'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''do_normalize'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''do_resize'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''do_center_crop'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''size'''   )   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class.from_dict(self.image_processor_dict   )
								self.assertEqual(image_processor.size,	{'''shortest_edge''': 18}   )
								self.assertEqual(image_processor.crop_size,	{'''height''': 18, '''width''': 18}   )

								lowerCAmelCase_      			=  self.image_processing_class.from_dict(self.image_processor_dict,	size=42,	crop_size=84   )
								self.assertEqual(image_processor.size,	{'''shortest_edge''': 42}   )
								self.assertEqual(image_processor.crop_size,	{'''height''': 84, '''width''': 84}   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								# create random PIL videos
								lowerCAmelCase_      			=  prepare_video_inputs(self.image_processor_tester,	equal_resolution=UpperCamelCase__   )
								for video in video_inputs:
														self.assertIsInstance(UpperCamelCase__,	UpperCamelCase__   )
														self.assertIsInstance(video[0],	Image.Image   )

								# Test not batched input
								lowerCAmelCase_      			=  image_processing(video_inputs[0],	return_tensors='''pt'''   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape,	(
								        1,
								        self.image_processor_tester.num_frames,
								        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(UpperCamelCase__,	return_tensors='''pt'''   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        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   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								lowerCAmelCase_      			=  prepare_video_inputs(self.image_processor_tester,	equal_resolution=UpperCamelCase__,	numpify=UpperCamelCase__   )
								for video in video_inputs:
														self.assertIsInstance(UpperCamelCase__,	UpperCamelCase__   )
														self.assertIsInstance(video[0],	np.ndarray   )

								# Test not batched input
								lowerCAmelCase_      			=  image_processing(video_inputs[0],	return_tensors='''pt'''   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape,	(
								        1,
								        self.image_processor_tester.num_frames,
								        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(UpperCamelCase__,	return_tensors='''pt'''   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        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   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								lowerCAmelCase_      			=  prepare_video_inputs(self.image_processor_tester,	equal_resolution=UpperCamelCase__,	torchify=UpperCamelCase__   )
								for video in video_inputs:
														self.assertIsInstance(UpperCamelCase__,	UpperCamelCase__   )
														self.assertIsInstance(video[0],	torch.Tensor   )

								# Test not batched input
								lowerCAmelCase_      			=  image_processing(video_inputs[0],	return_tensors='''pt'''   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape,	(
								        1,
								        self.image_processor_tester.num_frames,
								        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(UpperCamelCase__,	return_tensors='''pt'''   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),	)










import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def __UpperCamelCase			(       _A = 3  ):
						if isinstance(_A ,  _A  ):
												raise TypeError('''number of qubits must be a integer.'''  )
						if number_of_qubits <= 0:
												raise ValueError('''number of qubits must be > 0.'''  )
						if math.floor(_A  ) != number_of_qubits:
												raise ValueError('''number of qubits must be exact integer.'''  )
						if number_of_qubits > 10:
												raise ValueError('''number of qubits too large to simulate(>10).'''  )

						lowerCAmelCase_      			=  QuantumRegister(_A ,  '''qr'''  )
						lowerCAmelCase_      			=  ClassicalRegister(_A ,  '''cr'''  )

						lowerCAmelCase_      			=  QuantumCircuit(_A ,  _A  )

						lowerCAmelCase_      			=  number_of_qubits

						for i in range(_A  ):
												quantum_circuit.h(number_of_qubits - i - 1  )
												counter -= 1
												for j in range(_A  ):
																		quantum_circuit.cp(np.pi / 2 ** (counter - j) ,  _A ,  _A  )

						for k in range(number_of_qubits // 2  ):
												quantum_circuit.swap(_A ,  number_of_qubits - k - 1  )

						# measure all the qubits
						quantum_circuit.measure(_A ,  _A  )
						# simulate with 10000 shots
						lowerCAmelCase_      			=  Aer.get_backend('''qasm_simulator'''  )
						lowerCAmelCase_      			=  execute(_A ,  _A ,  shots=10000  )

						return job.result().get_counts(_A  )


if __name__ == "__main__":
						print(
						    f"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
						)




