


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCamelCase      						=    logging.get_logger(__name__)  # pylint: disable=invalid-name







class     lowerCamelCase__		(							lowerCAmelCase__ ):






			"""simple docstring"""






			_UpperCamelCase						:   List[str]					=						['safety_checker', 'feature_extractor']




			def __init__(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case = True ,  ):

						'''simple docstring'''


						super().__init__()
						if hasattr(scheduler.config ,  "steps_offset"       ) and scheduler.config.steps_offset != 1:
									UpperCamelCase__   =    (
									    F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
									    F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
									    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
									    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
									    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
									    ''' file'''
									)
									deprecate("steps_offset!=1" ,  "1.0.0" ,  _A ,  standard_warn=_A       )
									UpperCamelCase__   =    dict(scheduler.config       )
									UpperCamelCase__   =    1
									UpperCamelCase__   =    FrozenDict(_A       )

						self.register_modules(
						    vae=_A ,  text_encoder=_A ,  tokenizer=_A ,  unet=_A ,  scheduler=_A ,  safety_checker=_A ,  feature_extractor=_A ,  )
						self.register_to_config(requires_safety_checker=_A       )




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return {k: getattr(self ,  _A       ) for k in self.config.keys() if not k.startswith("_"       )}




			def 			snake_case__			(							self ,  snake_case = "auto"       ):

						'''simple docstring'''


						if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									UpperCamelCase__   =    self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(_A       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						self.enable_attention_slicing(_A       )




			@torch.no_grad()
			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = 0.8 ,  snake_case = 50 ,  snake_case = 7.5 ,  snake_case = None ,  snake_case = 1 ,  snake_case = 0.0 ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  snake_case = None ,  snake_case = 1 ,  ):

						'''simple docstring'''


						return StableDiffusionInpaintPipelineLegacy(**self.components       )(
						    prompt=_A ,  image=_A ,  mask_image=_A ,  strength=_A ,  num_inference_steps=_A ,  guidance_scale=_A ,  negative_prompt=_A ,  num_images_per_prompt=_A ,  eta=_A ,  generator=_A ,  output_type=_A ,  return_dict=_A ,  callback=_A ,  )




			@torch.no_grad()
			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case = 0.8 ,  snake_case = 50 ,  snake_case = 7.5 ,  snake_case = None ,  snake_case = 1 ,  snake_case = 0.0 ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  snake_case = None ,  snake_case = 1 ,  **snake_case ,  ):

						'''simple docstring'''


						return StableDiffusionImgaImgPipeline(**self.components       )(
						    prompt=_A ,  image=_A ,  strength=_A ,  num_inference_steps=_A ,  guidance_scale=_A ,  negative_prompt=_A ,  num_images_per_prompt=_A ,  eta=_A ,  generator=_A ,  output_type=_A ,  return_dict=_A ,  callback=_A ,  callback_steps=_A ,  )







			@torch.no_grad()
			def 			snake_case__			(							self ,  snake_case ,  snake_case = 512 ,  snake_case = 512 ,  snake_case = 50 ,  snake_case = 7.5 ,  snake_case = None ,  snake_case = 1 ,  snake_case = 0.0 ,  snake_case = None ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  snake_case = None ,  snake_case = 1 ,  ):

						'''simple docstring'''


						return StableDiffusionPipeline(**self.components       )(
						    prompt=_A ,  height=_A ,  width=_A ,  num_inference_steps=_A ,  guidance_scale=_A ,  negative_prompt=_A ,  num_images_per_prompt=_A ,  eta=_A ,  generator=_A ,  latents=_A ,  output_type=_A ,  return_dict=_A ,  callback=_A ,  callback_steps=_A ,  )






import qiskit
from qiskit.providers import Backend
def     UpperCamelCase_(			_A				:int			,       _A				:int					)->						tuple[qiskit.QuantumCircuit, str, str]:
			UpperCamelCase__, UpperCamelCase__   =    bin(_A					)[2:], bin(_A					)[2:]  # Remove leading '0b'

			# Ensure that both strings are of the same length
			if len(_A					) > len(_A					):
						UpperCamelCase__   =    y.zfill(len(_A					)					)
			else:
						UpperCamelCase__   =    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
			UpperCamelCase__   =    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     UpperCamelCase_(			_A				:qiskit.QuantumCircuit			,       _A				:int			,       _A				:int			,       _A				:int			,       _A				:int			,       )->						Union[str, Any]:
			circuit.ccx(_A			,       _A			,       _A					)
			circuit.cx(_A			,       _A					)
			circuit.ccx(_A			,       _A			,       _A					)
			circuit.cx(_A			,       _A					)
			circuit.cx(_A			,       _A					)
def     UpperCamelCase_(			_A				:int			,       _A				:int			,       _A				:Backend = qiskit.Aer.get_backend("aer_simulator"					)			,       )->						int:

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

			# Store the Integers
			UpperCamelCase__, UpperCamelCase__, UpperCamelCase__   =    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]					)

			UpperCamelCase__   =    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()


