




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 ViTImageProcessor




class 			a__       (					unittest.TestCase      ):







     def __init__(	self  ,		A  ,		A=7  ,		A=3  ,		A=18  ,		A=30  ,		A=400  ,		A=True  ,		A=None  ,		A=True  ,		A=[0.5, 0.5, 0.5]  ,		A=[0.5, 0.5, 0.5]  ,		)  ->    Optional[Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''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,
          }







@require_torch
@require_vision
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Optional[Any]							=	ViTImageProcessor if is_vision_available() else None







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      ViTImageProcessingTester(self )







     @property
     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          return self.image_processor_tester.prepare_image_processor_dict()







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(A  ,		"image_mean" ) )
          self.assertTrue(hasattr(A  ,		"image_std" ) )
          self.assertTrue(hasattr(A  ,		"do_normalize" ) )
          self.assertTrue(hasattr(A  ,		"do_resize" ) )
          self.assertTrue(hasattr(A  ,		"size" ) )







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



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

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







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          pass







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



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

          # Test not batched input
          a      =      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.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)

          # Test batched
          a      =      image_processing(A  ,		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.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



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

          # Test not batched input
          a      =      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.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)

          # Test batched
          a      =      image_processing(A  ,		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.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          a      =      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
          a      =      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.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)

          # Test batched
          a      =      image_processing(A  ,		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.size["height"],
                  self.image_processor_tester.size["width"],
              )  ,		)







import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

   # Generate a list of 10 random numbers
   lowercase__      :			List[str]           =					[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print("Random numbers", random_numbers_list)

