


'''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	:	Union[str, Any]     =     logging.get_logger(__name__)  # pylint: disable=invalid-name

__A	:	Any     =     '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Optional[Any]=8 ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		snake_case_		:							List[str]			       =						width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor






class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:UNetaDConditionModel  ,_UpperCamelCase		:DDPMScheduler  ,_UpperCamelCase		:VQModel  ,):
									super().__init__()

									self.register_modules(
									    unet=_UpperCamelCase  ,scheduler=_UpperCamelCase  ,movq=_UpperCamelCase  ,)
									snake_case_		:							Dict			       =						2 ** (len(self.movq.config.block_out_channels			) - 1)





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Any			):
									if latents is None:
											snake_case_		:							Optional[Any]			       =						randn_tensor(_UpperCamelCase  ,generator=_UpperCamelCase  ,device=_UpperCamelCase  ,dtype=_UpperCamelCase			)
									else:
											if latents.shape != shape:
													raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
											snake_case_		:							Optional[int]			       =						latents.to(_UpperCamelCase			)

									snake_case_		:							int			       =						latents * scheduler.init_noise_sigma
									return latents





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Optional[int]=0			):
									if is_accelerate_available():
											from accelerate import cpu_offload
									else:
											raise ImportError("""Please install accelerate via `pip install accelerate`"""			)

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

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





							def 		a__   (  self		:Any  ,_UpperCamelCase		:str=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."""			)

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

									if self.device.type != "cpu":
											self.to("""cpu"""  ,silence_dtype_warnings=_UpperCamelCase			)
											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_		:							Tuple			       =						cpu_offload_with_hook(_UpperCamelCase  ,_UpperCamelCase  ,prev_module_hook=_UpperCamelCase			)

									# We'll offload the last model manually.
									snake_case_		:							Optional[Any]			       =						hook





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 		a__   (  self		:Union[str, Any]			):
									if not hasattr(self.unet  ,"""_hf_hook"""			):
											return self.device
									for module in self.unet.modules():
											if (
											    hasattr(_UpperCamelCase  ,"""_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(_UpperCamelCase			)
							def __call__(  self		:Optional[int]  ,_UpperCamelCase		:Union[torch.FloatTensor, List[torch.FloatTensor]]  ,_UpperCamelCase		:Union[torch.FloatTensor, List[torch.FloatTensor]]  ,_UpperCamelCase		:torch.FloatTensor  ,_UpperCamelCase		:int = 5_1_2  ,_UpperCamelCase		:int = 5_1_2  ,_UpperCamelCase		:int = 1_0_0  ,_UpperCamelCase		:float = 4.0  ,_UpperCamelCase		:int = 1  ,_UpperCamelCase		:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,_UpperCamelCase		:Optional[torch.FloatTensor] = None  ,_UpperCamelCase		:Optional[str] = "pil"  ,_UpperCamelCase		:bool = True  ,):
									snake_case_		:							List[str]			       =						self._execution_device

									snake_case_		:							List[Any]			       =						guidance_scale > 1.0

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

									snake_case_		:							Any			       =						image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
											snake_case_		:							Tuple			       =						image_embeds.repeat_interleave(_UpperCamelCase  ,dim=0			)
											snake_case_		:							str			       =						negative_image_embeds.repeat_interleave(_UpperCamelCase  ,dim=0			)
											snake_case_		:							Optional[Any]			       =						hint.repeat_interleave(_UpperCamelCase  ,dim=0			)

									snake_case_		:							Any			       =						torch.cat([negative_image_embeds, image_embeds]  ,dim=0			).to(dtype=self.unet.dtype  ,device=_UpperCamelCase			)
									snake_case_		:							int			       =						torch.cat([hint, hint]  ,dim=0			).to(dtype=self.unet.dtype  ,device=_UpperCamelCase			)
									self.scheduler.set_timesteps(_UpperCamelCase  ,device=_UpperCamelCase			)
									snake_case_		:							List[str]			       =						self.scheduler.timesteps

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

									snake_case_       ,      snake_case_		:							List[str]			       =						downscale_height_and_width(_UpperCamelCase  ,_UpperCamelCase  ,self.movq_scale_factor			)

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

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

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

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

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

									if output_type == "pil":
											snake_case_		:							Any			       =						self.numpy_to_pil(_UpperCamelCase			)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=_UpperCamelCase			)



'''simple docstring'''






import random
from typing import Any





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		for _ in range(len(lowerCamelCase_ ) ):
				snake_case_		:							Union[str, Any]			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_		:							Any			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_       ,      snake_case_		:							List[str]			       =						data[b], data[a]
		return data


if __name__ == "__main__":
							__A	:	Optional[int]     =     [0, 1, 2, 3, 4, 5, 6, 7]
							__A	:	Optional[int]     =     ['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))