"""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,
)


lowerCAmelCase__  :  Optional[Any]						     =	logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase__  :  List[str]						     =	'\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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=8       ):
				UpperCAmelCase__             =							height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				UpperCAmelCase__             =							width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	UNetaDConditionModel    ,lowerCamelCase__   :	DDPMScheduler    ,lowerCamelCase__   :	VQModel    ,):
									super().__init__()

									self.register_modules(
									    unet=lowerCamelCase__    ,scheduler=lowerCamelCase__    ,movq=lowerCamelCase__    ,)
									UpperCAmelCase__             =							2 ** (len(self.movq.config.block_out_channels			) - 1)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	int			):
									if latents is None:
													UpperCAmelCase__             =							randn_tensor(lowerCamelCase__    ,generator=lowerCamelCase__    ,device=lowerCamelCase__    ,dtype=lowerCamelCase__			)
									else:
													if latents.shape != shape:
																	raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
													UpperCAmelCase__             =							latents.to(lowerCamelCase__			)

									UpperCAmelCase__             =							latents * scheduler.init_noise_sigma
									return latents







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	List[str]=0			):
									if is_accelerate_available():
													from accelerate import cpu_offload
									else:
													raise ImportError('Please install accelerate via `pip install accelerate`'			)

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

									UpperCAmelCase__             =							[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																	cpu_offload(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	Tuple=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.'			)

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

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

									UpperCAmelCase__             =							None
									for cpu_offloaded_model in [self.unet, self.movq]:
													UpperCAmelCase__      ,	UpperCAmelCase__             =							cpu_offload_with_hook(lowerCamelCase__    ,lowerCamelCase__    ,prev_module_hook=lowerCamelCase__			)

									# We'll offload the last model manually.
									UpperCAmelCase__             =							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(lowerCamelCase__    ,'_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(lowerCamelCase__			)
					def __call__(				self   :	str    ,lowerCamelCase__   :	Union[torch.FloatTensor, List[torch.FloatTensor]]    ,lowerCamelCase__   :	Union[torch.FloatTensor, List[torch.FloatTensor]]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	int = 512    ,lowerCamelCase__   :	int = 512    ,lowerCamelCase__   :	int = 100    ,lowerCamelCase__   :	float = 4.0    ,lowerCamelCase__   :	int = 1    ,lowerCamelCase__   :	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,lowerCamelCase__   :	Optional[torch.FloatTensor] = None    ,lowerCamelCase__   :	Optional[str] = "pil"    ,lowerCamelCase__   :	bool = True    ,):
									UpperCAmelCase__             =							self._execution_device

									UpperCAmelCase__             =							guidance_scale > 1.0

									if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=0			)
									if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=0			)
									if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=0			)

									UpperCAmelCase__             =							image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
													UpperCAmelCase__             =							image_embeds.repeat_interleave(lowerCamelCase__    ,dim=0			)
													UpperCAmelCase__             =							negative_image_embeds.repeat_interleave(lowerCamelCase__    ,dim=0			)
													UpperCAmelCase__             =							hint.repeat_interleave(lowerCamelCase__    ,dim=0			)

									UpperCAmelCase__             =							torch.cat([negative_image_embeds, image_embeds]    ,dim=0			).to(dtype=self.unet.dtype    ,device=lowerCamelCase__			)
									UpperCAmelCase__             =							torch.cat([hint, hint]    ,dim=0			).to(dtype=self.unet.dtype    ,device=lowerCamelCase__			)
									self.scheduler.set_timesteps(lowerCamelCase__    ,device=lowerCamelCase__			)
									UpperCAmelCase__             =							self.scheduler.timesteps

									UpperCAmelCase__             =							self.movq.config.latent_channels

									UpperCAmelCase__      ,	UpperCAmelCase__             =							downscale_height_and_width(lowerCamelCase__    ,lowerCamelCase__    ,self.movq_scale_factor			)

									# create initial latent
									UpperCAmelCase__             =							self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)    ,image_embeds.dtype    ,lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,self.scheduler    ,)

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

													UpperCAmelCase__             =							{'image_embeds': image_embeds, 'hint': hint}
													UpperCAmelCase__             =							self.unet(
													    sample=lowerCamelCase__    ,timestep=lowerCamelCase__    ,encoder_hidden_states=lowerCamelCase__    ,added_cond_kwargs=lowerCamelCase__    ,return_dict=lowerCamelCase__    ,)[0]

													if do_classifier_free_guidance:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							noise_pred.split(latents.shape[1]    ,dim=1			)
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							noise_pred.chunk(2			)
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							variance_pred.chunk(2			)
																	UpperCAmelCase__             =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCAmelCase__             =							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"]
													):
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							noise_pred.split(latents.shape[1]    ,dim=1			)

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

									if output_type == "pil":
													UpperCAmelCase__             =							self.numpy_to_pil(lowerCamelCase__			)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=lowerCamelCase__			)




"""simple docstring"""






import argparse


lowerCAmelCase__  :  List[str]						     =	'docs/source/_static/js/custom.js'



def   a_						(					lowerCamelCase       ):
				with open(lowerCamelCase					,			encoding='utf-8'					,			newline='\n'       ) as f:
								UpperCAmelCase__             =							f.readlines()
				UpperCAmelCase__             =							0

				# First let's put the right version
				while not lines[index].startswith('const stableVersion ='       ):
								index += 1
				UpperCAmelCase__             =							f'''const stableVersion = "v{version}"\n'''

				# Then update the dictionary
				while not lines[index].startswith('const versionMapping = {'       ):
								index += 1

				# We go until the end
				while not lines[index].startswith('}'       ):
								index += 1
				# We add the new version at the end
				lines[index - 1] += f'''    "v{version}": "v{version}",\n'''

				with open(lowerCamelCase					,			'w'					,			encoding='utf-8'					,			newline='\n'       ) as f:
								f.writelines(lowerCamelCase       )


if __name__ == "__main__":
						lowerCAmelCase__  :  List[Any]						     =	argparse.ArgumentParser()
						parser.add_argument('--version', help='Release version.')
						lowerCAmelCase__  :  Optional[int]						     =	parser.parse_args()
						update_custom_js(args.version)



