





import qiskit




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Union[str, Any]						=		qiskit.Aer.get_backend('''aer_simulator'''   )

       _lowerCamelCase				:  List[str]						=		qiskit.QuantumCircuit(4			,     2   )
       # encode inputs in qubits 0 and 1
       if bita == 1:
              qc_ha.x(0   )
       if bita == 1:
              qc_ha.x(1   )
       qc_ha.barrier()

       # use cnots to write XOR of the inputs on qubit2
       qc_ha.cx(0			,     2   )
       qc_ha.cx(1			,     2   )

       # use ccx / toffoli gate to write AND of the inputs on qubit3
       qc_ha.ccx(0			,     1			,     3   )
       qc_ha.barrier()

       # extract outputs
       qc_ha.measure(2			,     0   )  # extract XOR value
       qc_ha.measure(3			,     1   )  # extract AND value

       # Execute the circuit on the qasm simulator
       _lowerCamelCase				:  Union[str, Any]						=		qiskit.execute(lowercase_			,     lowercase_			,     shots=10_00   )

       # Return the histogram data of the results of the experiment
       return job.result().get_counts(lowercase_   )


if __name__ == "__main__":
       _lowerCamelCase      							=   half_adder(1, 1)
       print(F'''Half Adder Output Qubit Counts: {counts}''')








import unittest

import numpy as np
import requests

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

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       _lowerCamelCase      							=   False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor







class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__=7   ,				a__=3   ,				a__=18   ,				a__=30   ,				a__=400   ,				a__=None   ,				a__=True   ,				a__=True   ,				a__=None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		size if size is not None else {'''height''': 20, '''width''': 20}
             _lowerCamelCase				:  int						=		parent
             _lowerCamelCase				:  Tuple						=		batch_size
             _lowerCamelCase				:  Union[str, Any]						=		num_channels
             _lowerCamelCase				:  Tuple						=		image_size
             _lowerCamelCase				:  Dict						=		min_resolution
             _lowerCamelCase				:  int						=		max_resolution
             _lowerCamelCase				:  Dict						=		size
             _lowerCamelCase				:  Dict						=		do_normalize
             _lowerCamelCase				:  List[Any]						=		do_convert_rgb
             _lowerCamelCase				:  Optional[int]						=		[512, 1024, 2048, 4096]
             _lowerCamelCase				:  Optional[int]						=		patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}


      def 					__snake_case      (		self):



             """simple docstring"""





             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
             _lowerCamelCase				:  Union[str, Any]						=		Image.open(requests.get(a__   ,				stream=a__).raw).convert('''RGB''')
             return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		PixaStructImageProcessingTester(self)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.image_processor_tester.prepare_dummy_image()

             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             _lowerCamelCase				:  Optional[int]						=		2048

             _lowerCamelCase				:  Any						=		image_processor(a__   ,				return_tensors='''pt'''   ,				max_patches=a__)
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,				torch.tensor(0.0606)   ,				atol=1e-3   ,				rtol=1e-3))


      def 					__snake_case      (		self):



             """simple docstring"""





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

             # Test not batched input
             _lowerCamelCase				:  Optional[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  Optional[int]						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				Image.Image)

             # Test not batched input
             _lowerCamelCase				:  List[str]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             _lowerCamelCase				:  Dict						=		True

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    with self.assertRaises(a__):
                           _lowerCamelCase				:  List[Any]						=		image_processor(
                               image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches

                    _lowerCamelCase				:  List[Any]						=		'''Hello'''

                    _lowerCamelCase				:  int						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             # create random numpy tensors
             _lowerCamelCase				:  Tuple						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__   ,				numpify=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				np.ndarray)

             _lowerCamelCase				:  Any						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Tuple						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             _lowerCamelCase				:  str						=		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
             _lowerCamelCase				:  Tuple						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		PixaStructImageProcessingTester(self   ,				num_channels=4)
             _lowerCamelCase				:  Union[str, Any]						=		3


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  str						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  Any						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				Image.Image)

             # Test not batched input
             _lowerCamelCase				:  List[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * (self.image_processor_tester.num_channels - 1)
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)

