






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_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor

class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   List[str]=7					,    __lowerCamelCase							:   Union[str, Any]=3					,    __lowerCamelCase							:   List[Any]=18					,    __lowerCamelCase							:   Optional[int]=30					,    __lowerCamelCase							:   List[Any]=400					,    __lowerCamelCase							:   Optional[Any]=True					,    __lowerCamelCase							:   int=None					,    __lowerCamelCase							:   Dict=True					,    __lowerCamelCase							:   Optional[int]=None					,    ):
		UpperCamelCase	:Tuple		=      size if size is not None else {"""shortest_edge""": 20}
		UpperCamelCase	:Dict		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase	:Dict		=      parent
		UpperCamelCase	:Tuple		=      batch_size
		UpperCamelCase	:Tuple		=      num_channels
		UpperCamelCase	:int		=      image_size
		UpperCamelCase	:Optional[Any]		=      min_resolution
		UpperCamelCase	:int		=      max_resolution
		UpperCamelCase	:Optional[int]		=      do_resize
		UpperCamelCase	:str		=      size
		UpperCamelCase	:str		=      do_center_crop
		UpperCamelCase	:List[str]		=      crop_size





	def 		_A					(						self							:   Dict      ):
		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		}

@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE			(     _a						,  unittest.TestCase ):
	snake_case__				:	Optional[int]	      =    MobileNetVaImageProcessor if is_vision_available() else None





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:Tuple		=      MobileNetVaImageProcessingTester(self      )





	@property
	def 		_A					(						self							:   Union[str, Any]      ):
		return self.image_processor_tester.prepare_image_processor_dict()





	def 		_A					(						self							:   Any      ):
		UpperCamelCase	:Union[str, Any]		=      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"""      )      )





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Union[str, Any]		=      self.image_processing_class.from_dict(self.image_processor_dict      )
		self.assertEqual(image_processor.size					,    {"""shortest_edge""": 20}      )
		self.assertEqual(image_processor.crop_size					,    {"""height""": 18, """width""": 18}      )

		UpperCamelCase	:Optional[Any]		=      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 		_A					(						self							:   List[str]      ):
		pass





	def 		_A					(						self							:   Union[str, Any]      ):
		# Initialize image_processing
		UpperCamelCase	:Union[str, Any]		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PIL images
		UpperCamelCase	:List[Any]		=      prepare_image_inputs(self.image_processor_tester					,    equal_resolution=__lowerCamelCase      )
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase					,    Image.Image      )

		# Test not batched input
		UpperCamelCase	:Any		=      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
		UpperCamelCase	:List[Any]		=      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 		_A					(						self							:   int      ):
		# Initialize image_processing
		UpperCamelCase	:Optional[int]		=      self.image_processing_class(**self.image_processor_dict      )
		# create random numpy tensors
		UpperCamelCase	: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
		UpperCamelCase	:List[Any]		=      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
		UpperCamelCase	:Union[str, Any]		=      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 		_A					(						self							:   Any      ):
		# Initialize image_processing
		UpperCamelCase	:List[Any]		=      self.image_processing_class(**self.image_processor_dict      )
		# create random PyTorch tensors
		UpperCamelCase	:List[Any]		=      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
		UpperCamelCase	:Tuple		=      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
		UpperCamelCase	:Union[str, Any]		=      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"""],
		    )					,    )








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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2   )						->     qiskit.result.counts.Counts:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      QuantumRegister(3      ,     """qr"""   )  # Define the number of quantum bits
	UpperCamelCase	:Optional[Any]		=      ClassicalRegister(1      ,     """cr"""   )  # Define the number of classical bits

	UpperCamelCase	:List[Any]		=      QuantumCircuit(__magic_name__      ,     __magic_name__   )  # Define the quantum circuit.

	# Build the circuit
	quantum_circuit.u(__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     0   )  # Quantum State to teleport
	quantum_circuit.h(1   )  # add hadamard gate
	quantum_circuit.cx(
	    1      ,     2   )  # add control gate with qubit 1 as control and 2 as target.
	quantum_circuit.cx(0      ,     1   )
	quantum_circuit.h(0   )
	quantum_circuit.cz(0      ,     2   )  # add control z gate.
	quantum_circuit.cx(1      ,     2   )

	quantum_circuit.measure([2]      ,     [0]   )  # measure the qubit.

	# Simulate the circuit using qasm simulator
	UpperCamelCase	:Union[str, Any]		=      Aer.get_backend("""aer_simulator"""   )
	UpperCamelCase	:Dict		=      execute(__magic_name__      ,     __magic_name__      ,     shots=1000   )

	return job.result().get_counts(__magic_name__   )


if __name__ == "__main__":
	print(
	    '''Total count for teleported state is: '''
	    F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
	)
