"""simple docstring"""


import numpy as np
import qiskit







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				qiskit.QuantumCircuit(num_qubits + 1   )

     # First, let's deal with the case in which oracle is balanced
     if case == "balanced":
          # First generate a random number that tells us which CNOTs to
          # wrap in X-gates:
          lowercase__								=				np.random.randint(1     ,					2**num_qubits   )
          # Next, format 'b' as a binary string of length 'n', padded with zeros:
          lowercase__								=				format(A     ,					f'''0{num_qubits}b'''   )
          # Next, we place the first X-gates. Each digit in our binary string
          # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
          # we apply an X-gate to that qubit:
          for index, bit in enumerate(A   ):
               if bit == "1":
                    oracle_qc.x(A   )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
          for index in range(A   ):
               oracle_qc.cx(A     ,					A   )
          # Next, place the final X-gates
          for index, bit in enumerate(A   ):
               if bit == "1":
                    oracle_qc.x(A   )

    # Case in which oracle is constant
     if case == "constant":
          # First decide what the fixed output of the oracle will be
          # (either always 0 or always 1)
          lowercase__								=				np.random.randint(2   )
          if output == 1:
               oracle_qc.x(A   )

     lowercase__								=				oracle_qc.to_gate()
     lowercase__								=				"Oracle"  # To show when we display the circuit
     return oracle_gate







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				qiskit.QuantumCircuit(num_qubits + 1     ,					A   )
     # Set up the output qubit:
     dj_circuit.x(A   )
     dj_circuit.h(A   )
     # And set up the input register:
     for qubit in range(A   ):
          dj_circuit.h(A   )
     # Let's append the oracle gate to our circuit:
     dj_circuit.append(A     ,					range(num_qubits + 1   )   )
     # Finally, perform the H-gates again and measure:
     for qubit in range(A   ):
          dj_circuit.h(A   )

     for i in range(A   ):
          dj_circuit.measure(A     ,					A   )

     return dj_circuit







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				qiskit.Aer.get_backend("aer_simulator"   )

     lowercase__								=				dj_oracle(A     ,					A   )
     lowercase__								=				dj_algorithm(A     ,					A   )

     # Execute the circuit on the simulator
     lowercase__								=				qiskit.execute(A     ,					A     ,					shots=10_00   )

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


if __name__ == "__main__":
 print(F'Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}')
 print(F'Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}')





"""simple docstring"""


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 OwlViTImageProcessor






class  a__     ( unittest.TestCase						):


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=7,					_UpperCAmelCase=3,					_UpperCAmelCase=18,					_UpperCAmelCase=30,					_UpperCAmelCase=400,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073],					_UpperCAmelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711],					_UpperCAmelCase=True,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				num_channels
        lowercase__								=				image_size
        lowercase__								=				min_resolution
        lowercase__								=				max_resolution
        lowercase__								=				do_resize
        lowercase__								=				size if size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_center_crop
        lowercase__								=				crop_size if crop_size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_normalize
        lowercase__								=				image_mean
        lowercase__								=				image_std
        lowercase__								=				do_convert_rgb


   def 						snake_case__						(  self	):
        '''simple docstring'''



        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_convert_rgb": self.do_convert_rgb,
        }






@require_torch
@require_vision
class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   OwlViTImageProcessor if is_vision_available() else None


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				OwlViTImageProcessingTester(self	)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_resize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"size"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_normalize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_mean"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_std"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_convert_rgb"	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size,					{"height": 18, "width": 18}	)
        self.assertEqual(image_processor.crop_size,					{"height": 18, "width": 18}	)

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


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase	)

        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					Image.Image	)

        # Test not batched input
        lowercase__								=				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
        lowercase__								=				image_processing(_UpperCAmelCase,					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 						snake_case__						(  self	):
        '''simple docstring'''



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

        # Test not batched input
        lowercase__								=				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
        lowercase__								=				image_processing(_UpperCAmelCase,					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 						snake_case__						(  self	):
        '''simple docstring'''



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

        # Test not batched input
        lowercase__								=				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
        lowercase__								=				image_processing(_UpperCAmelCase,					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"],
            ),					)




