



"""simple docstring"""
import qiskit
from qiskit.providers import Backend





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	str            =bin(lowercase	)[2:], bin(lowercase	)[2:]  # Remove leading '0b'

   # Ensure that both strings are of the same length
   if len(lowercase	) > len(lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =y.zfill(len(lowercase	)	)
   else:
      SCREAMING_SNAKE_CASE_:	List[str]            =x.zfill(len(lowercase	)	)

   # 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
   SCREAMING_SNAKE_CASE_:	Dict            =qiskit.QuantumCircuit((len(lowercase	) * 3) + 1							,     len(lowercase	) + 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(lowercase	)	):
      if x[::-1][i] == "1":
         circuit.x(lowercase	)
   for j in range(len(lowercase	)	):
      if y[::-1][j] == "1":
         circuit.x(len(lowercase	) + j	)

   return circuit, x, y





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase							,     ):
   circuit.ccx(lowercase							,     lowercase							,     lowercase	)
   circuit.cx(lowercase							,     lowercase	)
   circuit.ccx(lowercase							,     lowercase							,     lowercase	)
   circuit.cx(lowercase							,     lowercase	)
   circuit.cx(lowercase							,     lowercase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase = qiskit.Aer.get_backend("""aer_simulator"""	)							,     ):

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

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

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

   SCREAMING_SNAKE_CASE_:	Tuple            =qiskit.execute(lowercase							,     lowercase							,     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()








"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class    a		(					UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Dict         =   PaintByExamplePipeline
  UpperCamelCase		:			str         =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  UpperCamelCase		:			Tuple         =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase		:			List[str]         =   frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



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


     '''simple docstring'''




     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	Any            =UNetaDConditionModel(
         block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=9    ,						out_channels=4    ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,						cross_attention_dim=32    ,						)
     SCREAMING_SNAKE_CASE_:	int            =PNDMScheduler(skip_prk_steps=lowerCAmelCase						)
     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	str            =AutoencoderKL(
         block_out_channels=[32, 64]    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,						latent_channels=4    ,						)
     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	Tuple            =CLIPVisionConfig(
         hidden_size=32    ,						projection_dim=32    ,						intermediate_size=37    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						image_size=32    ,						patch_size=4    ,						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =PaintByExampleImageEncoder(lowerCAmelCase    ,						proj_size=32						)
     SCREAMING_SNAKE_CASE_:	Tuple            =CLIPImageProcessor(crop_size=32    ,						size=32						)

     SCREAMING_SNAKE_CASE_:	Any            ={
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """image_encoder""": image_encoder,
         """safety_checker""": None,
         """feature_extractor""": feature_extractor,
     }
     return components



  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =np.array(image.convert("""RGB"""						)						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =image[None].transpose(0    ,						3    ,						1    ,						2						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.from_numpy(lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
     return image



  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   int="cpu"    ,						lowerCAmelCase	:   Dict=0						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Tuple            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	Any            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	List[str]            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((32, 32)						)

     if str(lowerCAmelCase						).startswith("""mps"""						):
        SCREAMING_SNAKE_CASE_:	Dict            =torch.manual_seed(lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	str            ={
         """example_image""": example_image,
         """image""": init_image,
         """mask_image""": mask_image,
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs



  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =PaintByExamplePipeline(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Any            =pipe.to("""cpu"""						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_dummy_inputs()
     SCREAMING_SNAKE_CASE_:	int            =pipe(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	List[str]            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)

     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            ="""cpu"""
     SCREAMING_SNAKE_CASE_:	Any            =self.get_dummy_inputs()
     inputs.pop("""mask_image"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.convert_to_pt(inputs.pop("""image"""						)						)
     SCREAMING_SNAKE_CASE_:	int            =image.clamp(0    ,						1						) / 2

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =PaintByExamplePipeline(**self.get_dummy_components()						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(image=lowerCAmelCase    ,						mask_image=mask_image[:, 0]    ,						**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	str            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	List[Any]            =mask_image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]

     SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(**self.get_dummy_inputs()						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =output.images

     assert out_a.shape == (1, 64, 64, 3)
     assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2





  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Dict:


     '''simple docstring'''




     super().test_inference_batch_single_identical(expected_max_diff=3E-3						)







@slow
@require_torch_gpu
class    a		(					unittest.TestCase    ):



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Any:


     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/dog_in_bucket.png"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/mask.png"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/panda.jpg"""						)

     SCREAMING_SNAKE_CASE_:	Tuple            =PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	str            =torch.manual_seed(321						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =pipe(
         image=lowerCAmelCase    ,						mask_image=lowerCAmelCase    ,						example_image=lowerCAmelCase    ,						generator=lowerCAmelCase    ,						guidance_scale=5.0    ,						num_inference_steps=50    ,						output_type="""np"""    ,						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =output.images

     SCREAMING_SNAKE_CASE_:	Any            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE_:	Any            =np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)

     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2



