




"""simple docstring"""
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a_					:     List[str]					    =  logging.get_logger(__name__)  # pylint: disable=invalid-name

a_					:     str					    =  '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''





def     UpperCAmelCase					(      A__:     Optional[int]				,					A__:     Optional[Any]				,					A__:     str=8  )	->  List[Any]:
			__lowerCamelCase       :						Any													=					height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			__lowerCamelCase       :						Tuple													=					width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor






class __lowercase(   SCREAMING_SNAKE_CASE_					):




					'''simple docstring'''


					def __init__(     self    ,						__a    ,						__a    ,						__a    ,						):
								super().__init__()

								self.register_modules(
								    unet=__a    ,						scheduler=__a    ,						movq=__a    ,						)
								__lowerCamelCase       :						Optional[Any]													=					2 ** (len(self.movq.config.block_out_channels  ) - 1)
					def      snake_case_					(     self    ,						__a    ,						__a    ,						__a    ,						__a    ,						__a    ,						__a  ):
								if latents is None:
											__lowerCamelCase       :						Union[str, Any]													=					randn_tensor(__a    ,						generator=__a    ,						device=__a    ,						dtype=__a  )
								else:
											if latents.shape != shape:
														raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''  )
											__lowerCamelCase       :						Optional[int]													=					latents.to(__a  )

								__lowerCamelCase       :						Tuple													=					latents * scheduler.init_noise_sigma
								return latents
					def      snake_case_					(     self    ,						__a=0  ):
								if is_accelerate_available():
											from accelerate import cpu_offload
								else:
											raise ImportError('Please install accelerate via `pip install accelerate`'  )

								__lowerCamelCase       :						int													=					torch.device(f'''cuda:{gpu_id}'''  )

								__lowerCamelCase       :						Optional[Any]													=					[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
														cpu_offload(__a    ,						__a  )
					def      snake_case_					(     self    ,						__a=0  ):
								if is_accelerate_available() and is_accelerate_version('>='    ,						'0.17.0.dev0'  ):
											from accelerate import cpu_offload_with_hook
								else:
											raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'  )

								__lowerCamelCase       :						Union[str, Any]													=					torch.device(f'''cuda:{gpu_id}'''  )

								if self.device.type != "cpu":
											self.to('cpu'    ,						silence_dtype_warnings=__a  )
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

								__lowerCamelCase       :						str													=					None
								for cpu_offloaded_model in [self.unet, self.movq]:
											__lowerCamelCase       :						Dict													=					cpu_offload_with_hook(__a    ,						__a    ,						prev_module_hook=__a  )

								# We'll offload the last model manually.
								__lowerCamelCase       :						Optional[int]													=					hook
					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def      snake_case_					(     self  ):
								if not hasattr(self.unet    ,						'_hf_hook'  ):
											return self.device
								for module in self.unet.modules():
											if (
											    hasattr(__a    ,						'_hf_hook'  )
											    and hasattr(module._hf_hook    ,						'execution_device'  )
											    and module._hf_hook.execution_device is not None
											):
														return torch.device(module._hf_hook.execution_device  )
								return self.device





					@torch.no_grad()
					@replace_example_docstring(__a  )
					def __call__(     self    ,						__a    ,						__a    ,						__a    ,						__a = 512    ,						__a = 512    ,						__a = 100    ,						__a = 4.0    ,						__a = 1    ,						__a = None    ,						__a = None    ,						__a = "pil"    ,						__a = True    ,						):
								__lowerCamelCase       :						Union[str, Any]													=					self._execution_device

								__lowerCamelCase       :						Any													=					guidance_scale > 1.0

								if isinstance(__a    ,						__a  ):
											__lowerCamelCase       :						Tuple													=					torch.cat(__a    ,						dim=0  )
								if isinstance(__a    ,						__a  ):
											__lowerCamelCase       :						Optional[Any]													=					torch.cat(__a    ,						dim=0  )
								if isinstance(__a    ,						__a  ):
											__lowerCamelCase       :						Union[str, Any]													=					torch.cat(__a    ,						dim=0  )

								__lowerCamelCase       :						int													=					image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
											__lowerCamelCase       :						int													=					image_embeds.repeat_interleave(__a    ,						dim=0  )
											__lowerCamelCase       :						Union[str, Any]													=					negative_image_embeds.repeat_interleave(__a    ,						dim=0  )
											__lowerCamelCase       :						List[Any]													=					hint.repeat_interleave(__a    ,						dim=0  )

								__lowerCamelCase       :						Optional[Any]													=					torch.cat([negative_image_embeds, image_embeds]    ,						dim=0  ).to(dtype=self.unet.dtype    ,						device=__a  )
								__lowerCamelCase       :						str													=					torch.cat([hint, hint]    ,						dim=0  ).to(dtype=self.unet.dtype    ,						device=__a  )
								self.scheduler.set_timesteps(__a    ,						device=__a  )
								__lowerCamelCase       :						Tuple													=					self.scheduler.timesteps

								__lowerCamelCase       :						List[str]													=					self.movq.config.latent_channels

								__lowerCamelCase       :						List[Any]													=					downscale_height_and_width(__a    ,						__a    ,						self.movq_scale_factor  )

								# create initial latent
								__lowerCamelCase       :						Dict													=					self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)    ,						image_embeds.dtype    ,						__a    ,						__a    ,						__a    ,						self.scheduler    ,						)

								for i, t in enumerate(self.progress_bar(__a  )  ):
											# expand the latents if we are doing classifier free guidance
											__lowerCamelCase       :						Any													=					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

											__lowerCamelCase       :						Dict													=					{'image_embeds': image_embeds, 'hint': hint}
											__lowerCamelCase       :						List[Any]													=					self.unet(
											    sample=__a    ,						timestep=__a    ,						encoder_hidden_states=__a    ,						added_cond_kwargs=__a    ,						return_dict=__a    ,						)[0]

											if do_classifier_free_guidance:
														__lowerCamelCase       :						Tuple													=					noise_pred.split(latents.shape[1]    ,						dim=1  )
														__lowerCamelCase       :						Any													=					noise_pred.chunk(2  )
														__lowerCamelCase       :						Dict													=					variance_pred.chunk(2  )
														__lowerCamelCase       :						Union[str, Any]													=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
														__lowerCamelCase       :						Tuple													=					torch.cat([noise_pred, variance_pred_text]    ,						dim=1  )

											if not (
											    hasattr(self.scheduler.config    ,						'variance_type'  )
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
														__lowerCamelCase       :						Optional[int]													=					noise_pred.split(latents.shape[1]    ,						dim=1  )

											# compute the previous noisy sample x_t -> x_t-1
											__lowerCamelCase       :						List[Any]													=					self.scheduler.step(
											    __a    ,						__a    ,						__a    ,						generator=__a    ,						)[0]
								# post-processing
								__lowerCamelCase       :						List[str]													=					self.movq.decode(__a    ,						force_not_quantize=__a  )['sample']

								if output_type not in ["pt", "np", "pil"]:
											raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''  )

								if output_type in ["np", "pil"]:
											__lowerCamelCase       :						str													=					image * 0.5 + 0.5
											__lowerCamelCase       :						int													=					image.clamp(0    ,						1  )
											__lowerCamelCase       :						str													=					image.cpu().permute(0    ,						2    ,						3    ,						1  ).float().numpy()

								if output_type == "pil":
											__lowerCamelCase       :						List[str]													=					self.numpy_to_pil(__a  )

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=__a  )








'''simple docstring'''





import qiskit





def   a_   (		_UpperCAmelCase    :      int = 2				)    ->  qiskit.result.counts.Counts:
      __snake_case :   Union[str, Any]													=    qubits

      # Using Aer's simulator
      __snake_case :   List[Any]													=    qiskit.Aer.get_backend('aer_simulator'				)

      # Creating a Quantum Circuit acting on the q register
      __snake_case :   Dict													=    qiskit.QuantumCircuit(_UpperCAmelCase ,_UpperCAmelCase				)

      # Adding a H gate on qubit 0 (now q0 in superposition)
      circuit.h(0				)

      for i in range(1 ,_UpperCAmelCase				):
            # Adding CX (CNOT) gate
            circuit.cx(i - 1 ,_UpperCAmelCase				)

      # Mapping the quantum measurement to the classical bits
      circuit.measure(list(range(_UpperCAmelCase				)				) ,list(range(_UpperCAmelCase				)				)				)

      # Now measuring any one qubit would affect other qubits to collapse
      # their super position and have same state as the measured one.

      # Executing the circuit on the simulator
      __snake_case :   Optional[Any]													=    qiskit.execute(_UpperCAmelCase ,_UpperCAmelCase ,shots=10_00				)

      return job.result().get_counts(_UpperCAmelCase				)


if __name__ == "__main__":
    print(F"""Total count for various states are: {quantum_entanglement(3)}""")



