






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,
)


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

lowercase  :							Optional[int]			  =    '''
    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      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[Any]  ,      _a       :			Any=8    ):
		snake_case_				:							Dict						 =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		snake_case_				:							Optional[int]						 =       width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				) ->				int:
								super().__init__()

								self.register_modules(
								    unet=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE		,				movq=_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							Union[str, Any]						 =       2 ** (len(self.movq.config.block_out_channels				) - 1)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								if latents is None:
										snake_case_				:							List[Any]						 =       randn_tensor(_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				device=_SCREAMING_SNAKE_CASE		,				dtype=_SCREAMING_SNAKE_CASE				)
								else:
										if latents.shape != shape:
												raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
										snake_case_				:							int						 =       latents.to(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       latents * scheduler.init_noise_sigma
								return latents






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=0				) ->				List[str]:
								if is_accelerate_available():
										from accelerate import cpu_offload
								else:
										raise ImportError("Please install accelerate via `pip install accelerate`"				)

								snake_case_				:							Optional[Any]						 =       torch.device(f'''cuda:{gpu_id}'''				)

								snake_case_				:							Optional[Any]						 =       [
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
										if cpu_offloaded_model is not None:
												cpu_offload(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=0				) ->				List[str]:
								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."				)

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

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

								snake_case_				:							Union[str, Any]						 =       None
								for cpu_offloaded_model in [self.unet, self.movq]:
										snake_case_ ,       snake_case_				:							List[Any]						 =       cpu_offload_with_hook(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				prev_module_hook=_SCREAMING_SNAKE_CASE				)

								# We'll offload the last model manually.
								snake_case_				:							Dict						 =       hook






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								if not hasattr(self.unet		,				"_hf_hook"				):
										return self.device
								for module in self.unet.modules():
										if (
										    hasattr(_SCREAMING_SNAKE_CASE		,				"_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(_SCREAMING_SNAKE_CASE				)
						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 512		,				_SCREAMING_SNAKE_CASE = 512		,				_SCREAMING_SNAKE_CASE = 100		,				_SCREAMING_SNAKE_CASE = 4.0		,				_SCREAMING_SNAKE_CASE = 1		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = "pil"		,				_SCREAMING_SNAKE_CASE = True		,				) ->				Optional[Any]:
								snake_case_				:							Union[str, Any]						 =       self._execution_device

								snake_case_				:							Tuple						 =       guidance_scale > 1.0

								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							Optional[Any]						 =       torch.cat(_SCREAMING_SNAKE_CASE		,				dim=0				)
								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							Any						 =       torch.cat(_SCREAMING_SNAKE_CASE		,				dim=0				)
								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							Optional[Any]						 =       torch.cat(_SCREAMING_SNAKE_CASE		,				dim=0				)

								snake_case_				:							List[str]						 =       image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
										snake_case_				:							Dict						 =       image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE		,				dim=0				)
										snake_case_				:							Optional[Any]						 =       negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE		,				dim=0				)
										snake_case_				:							Dict						 =       hint.repeat_interleave(_SCREAMING_SNAKE_CASE		,				dim=0				)

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

								snake_case_				:							List[Any]						 =       self.movq.config.latent_channels

								snake_case_ ,       snake_case_				:							Dict						 =       downscale_height_and_width(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				self.movq_scale_factor				)

								# create initial latent
								snake_case_				:							Any						 =       self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)		,				image_embeds.dtype		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				self.scheduler		,				)

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

										snake_case_				:							Tuple						 =       {"image_embeds": image_embeds, "hint": hint}
										snake_case_				:							Tuple						 =       self.unet(
										    sample=_SCREAMING_SNAKE_CASE		,				timestep=_SCREAMING_SNAKE_CASE		,				encoder_hidden_states=_SCREAMING_SNAKE_CASE		,				added_cond_kwargs=_SCREAMING_SNAKE_CASE		,				return_dict=_SCREAMING_SNAKE_CASE		,				)[0]

										if do_classifier_free_guidance:
												snake_case_ ,       snake_case_				:							Union[str, Any]						 =       noise_pred.split(latents.shape[1]		,				dim=1				)
												snake_case_ ,       snake_case_				:							Any						 =       noise_pred.chunk(2				)
												snake_case_ ,       snake_case_				:							Optional[int]						 =       variance_pred.chunk(2				)
												snake_case_				:							Optional[int]						 =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												snake_case_				:							Optional[int]						 =       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"]
										):
												snake_case_ ,       snake_case_				:							List[str]						 =       noise_pred.split(latents.shape[1]		,				dim=1				)

										# compute the previous noisy sample x_t -> x_t-1
										snake_case_				:							List[Any]						 =       self.scheduler.step(
										    _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				)[0]
								# post-processing
								snake_case_				:							List[str]						 =       self.movq.decode(_SCREAMING_SNAKE_CASE		,				force_not_quantize=_SCREAMING_SNAKE_CASE				)["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"]:
										snake_case_				:							List[Any]						 =       image * 0.5 + 0.5
										snake_case_				:							Any						 =       image.clamp(0		,				1				)
										snake_case_				:							Optional[int]						 =       image.cpu().permute(0		,				2		,				3		,				1				).float().numpy()

								if output_type == "pil":
										snake_case_				:							Optional[int]						 =       self.numpy_to_pil(_SCREAMING_SNAKE_CASE				)

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE				)








import qiskit

def      lowerCAmelCase__	(    _a       :			int  ,      _a       :			int    ):
		snake_case_				:							Tuple						 =       qiskit.Aer.get_backend("aer_simulator"    )

		# Create a Quantum Circuit acting on the q register
		snake_case_				:							Any						 =       qiskit.QuantumCircuit(_a  ,      _a    )

		# Apply X (NOT) Gate to Qubits 0 & 1
		circuit.x(0    )
		circuit.x(1    )

		# Map the quantum measurement to the classical bits
		circuit.measure([0, 1]  ,      [0, 1]    )

		# Execute the circuit on the qasm simulator
		snake_case_				:							Any						 =       qiskit.execute(_a  ,      _a  ,      shots=10_00    )

		# Return the histogram data of the results of the experiment.
		return job.result().get_counts(_a    )


if __name__ == "__main__":
							lowercase  :							List[Any]			  =    single_qubit_measure(2, 2)
							print(F"""Total count for various states are: {counts}""")
