



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 ConvNextImageProcessor

class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def __init__(							self			:				Dict  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[str]=7  ,	UpperCamelCase			:				Union[str, Any]=3  ,	UpperCamelCase			:				Tuple=1_8  ,	UpperCamelCase			:				Tuple=3_0  ,	UpperCamelCase			:				List[Any]=4_0_0  ,	UpperCamelCase			:				str=True  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				List[str]=0.8_7_5  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Dict=[0.5, 0.5, 0.5]  ,	UpperCamelCase			:				int=[0.5, 0.5, 0.5]  ,	)->str:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						size if size is not None else {"shortest_edge": 2_0}
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						parent
      __SCREAMING_SNAKE_CASE							:     Tuple							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_size
      __SCREAMING_SNAKE_CASE							:     str							=						min_resolution
      __SCREAMING_SNAKE_CASE							:     Dict							=						max_resolution
      __SCREAMING_SNAKE_CASE							:     Any							=						do_resize
      __SCREAMING_SNAKE_CASE							:     Tuple							=						size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						crop_pct
      __SCREAMING_SNAKE_CASE							:     str							=						do_normalize
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_mean
      __SCREAMING_SNAKE_CASE							:     Any							=						image_std






     def 						__snake_case     (							self			:				List[str]					)->List[str]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "crop_pct": self.crop_pct,
      }






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      ConvNextImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				List[str]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Any							=						ConvNextImageProcessingTester(self					)





     @property
     def 						__snake_case     (							self			:				int					)->int:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_resize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"size"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"crop_pct"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_normalize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_mean"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_std"					)					)





     def 						__snake_case     (							self			:				Tuple					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 2_0}					)

      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_dict(self.image_processor_dict  ,	size=4_2					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 4_2}					)





     def 						__snake_case     (							self			:				int					)->List[Any]:
      pass





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

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Dict							=						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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)





     def 						__snake_case     (							self			:				Dict					)->int:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE							:     int							=						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
      __SCREAMING_SNAKE_CASE							:     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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)






     def 						__snake_case     (							self			:				str					)->List[Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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
      __SCREAMING_SNAKE_CASE							:     int							=						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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)







"""simple docstring"""


import qiskit
from qiskit.providers import Backend







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				bin(A   )[2:], bin(A   )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(A   ) > len(A   ):
          lowercase__								=				y.zfill(len(A   )   )
     else:
          lowercase__								=				x.zfill(len(A   )   )

     # We need (3 * number of bits in the larger number)+1 qBits
     # The second parameter is the number of classical registers, to measure the result
     lowercase__								=				qiskit.QuantumCircuit((len(A   ) * 3) + 1     ,					len(A   ) + 1   )

     # We are essentially "not-ing" the bits that are 1
     # Reversed because it's easier to perform ops on more significant bits
     for i in range(len(A   )   ):
          if x[::-1][i] == "1":
               circuit.x(A   )
     for j in range(len(A   )   ):
          if y[::-1][j] == "1":
               circuit.x(len(A   ) + j   )

     return circuit, x, y







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					):






     '''simple docstring'''



     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.cx(A     ,					A   )







def        __a (		A     ,					A     ,					A = qiskit.Aer.get_backend("aer_simulator"   )     ,					):






     '''simple docstring'''



     if vala < 0 or vala < 0:
          raise ValueError("Both Integers must be positive!"   )

     # Store the Integers
     lowercase__			,    lowercase__			,    lowercase__								=				store_two_classics(A     ,					A   )
     for i in range(len(A   )   ):
          full_adder(A     ,					A     ,					len(A   ) + i     ,					len(A   ) + len(A   ) + i     ,					len(A   ) + len(A   ) + i + 1   )
          circuit.barrier()  # Optional, just for aesthetics

     # Measure the resultant qBits
     for i in range(len(A   ) + 1   ):
          circuit.measure([(len(A   ) * 2) + i]     ,					[i]   )

     lowercase__								=				qiskit.execute(A     ,					A     ,					shots=1   ).result()

     # The result is in binary. Convert it back to int
     return int(list(res.get_counts()   )[0]     ,					2   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




