





import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def      _A       (		SCREAMING_SNAKE_CASE     :   int = 3						):





  """simple docstring"""



  if isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    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(SCREAMING_SNAKE_CASE						) != 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)."						)

  a__	:					Optional[int]										=QuantumRegister(SCREAMING_SNAKE_CASE       ,       "qr"						)
  a__	:					List[Any]										=ClassicalRegister(SCREAMING_SNAKE_CASE       ,       "cr"						)

  a__	:					int										=QuantumCircuit(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  a__	:					Optional[Any]										=number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  # simulate with 10000 shots
  a__	:					Optional[Any]										=Aer.get_backend("qasm_simulator"						)
  a__	:					List[str]										=execute(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       shots=10_000						)

  return job.result().get_counts(SCREAMING_SNAKE_CASE						)


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













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 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      )   ->						Optional[int]:



        '''simple docstring'''




        a__	:					str										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					Union[str, Any]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Optional[int]										=batch_size
        a__	:					Any										=num_channels
        a__	:					List[str]										=image_size
        a__	:					Dict										=min_resolution
        a__	:					List[Any]										=max_resolution
        a__	:					Dict										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					str										=do_center_crop
        a__	:					List[str]										=crop_size







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        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 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Optional[Any]						=			MobileNetVaImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=MobileNetVaImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=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__		,      "crop_size"							)							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Any										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 2_0}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Dict										=self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2		,      crop_size=8_4							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 8_4, "width": 8_4}							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Dict										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					Optional[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
        a__	:					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
        a__	:					Dict										=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    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					str										=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
        a__	:					List[str]										=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
        a__	:					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    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					int										=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
        a__	:					Optional[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
        a__	:					str										=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"],
            )		,      )






