






from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

__lowerCamelCase						= '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase=8     )     ->		List[Any]:







							"""simple docstring"""





							_a  :    int			=						height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							_a  :    List[str]			=						width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase=512    ,	UpperCAmelCase=512     )     ->		Tuple:







							"""simple docstring"""





							_a  :    Dict			=						pil_image.resize((w, h)    ,	resample=Image.BICUBIC    ,	reducing_gap=1     )
							_a  :    List[str]			=						np.array(pil_image.convert('''RGB'''     )     )
							_a  :    Tuple			=						arr.astype(np.floataa     ) / 1_2_7.5 - 1
							_a  :    str			=						np.transpose(UpperCAmelCase    ,	[2, 0, 1]     )
							_a  :    Union[str, Any]			=						torch.from_numpy(UpperCAmelCase     ).unsqueeze(0     )
							return image







class      UpperCamelCase_							(				UpperCamelCase       ):




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

													self.register_modules(
													    unet=lowercase		,	scheduler=lowercase		,	movq=lowercase		,	)
													_a  :    Optional[Any]			=						2 ** (len(self.movq.config.block_out_channels       ) - 1)




						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase       )			->						Tuple:
													# get the original timestep using init_timestep
													_a  :    Tuple			=						min(int(num_inference_steps * strength       )		,	lowercase       )

													_a  :    int			=						max(num_inference_steps - init_timestep		,	0       )
													_a  :    str			=						self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start




						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase=None       )			->						Optional[int]:
													if not isinstance(lowercase		,	(torch.Tensor, PIL.Image.Image, list)       ):
																				raise ValueError(
																				    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase       )}'       )

													_a  :    Optional[int]			=						image.to(device=lowercase		,	dtype=lowercase       )

													_a  :    Tuple			=						batch_size * num_images_per_prompt

													if image.shape[1] == 4:
																				_a  :    List[Any]			=						image

													else:
																				if isinstance(lowercase		,	lowercase       ) and len(lowercase       ) != batch_size:
																											raise ValueError(
																											    F'You have passed a list of generators of length {len(lowercase       )}, but requested an effective batch'
																											    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'       )

																				elif isinstance(lowercase		,	lowercase       ):
																											_a  :    Any			=						[
																											    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(lowercase       )
																											]
																											_a  :    Any			=						torch.cat(lowercase		,	dim=0       )
																				else:
																											_a  :    str			=						self.movq.encode(lowercase       ).latent_dist.sample(lowercase       )

																				_a  :    int			=						self.movq.config.scaling_factor * init_latents

													_a  :    Union[str, Any]			=						torch.cat([init_latents]		,	dim=0       )

													_a  :    Optional[Any]			=						init_latents.shape
													_a  :    Dict			=						randn_tensor(lowercase		,	generator=lowercase		,	device=lowercase		,	dtype=lowercase       )

													# get latents
													_a  :    Union[str, Any]			=						self.scheduler.add_noise(lowercase		,	lowercase		,	lowercase       )

													_a  :    Dict			=						init_latents

													return latents




						def      snake_case__(    self		,	lowercase=0       )			->						str:
													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

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

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




						def      snake_case__(    self		,	lowercase=0       )			->						Dict:
													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.'''       )

													_a  :    str			=						torch.device(F'cuda:{gpu_id}'       )

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

													_a  :    Union[str, Any]			=						None
													for cpu_offloaded_model in [self.unet, self.movq]:
																				_a		,					_a  :    int			=						cpu_offload_with_hook(lowercase		,	lowercase		,	prev_module_hook=lowercase       )

													# We'll offload the last model manually.
													_a  :    Any			=						hook




						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def      snake_case__(    self       )			->						Dict:
													if not hasattr(self.unet		,	'''_hf_hook'''       ):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(lowercase		,	'''_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(lowercase       )
						def __call__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase = 512		,	lowercase = 512		,	lowercase = 100		,	lowercase = 4.0		,	lowercase = 0.3		,	lowercase = 1		,	lowercase = None		,	lowercase = "pil"		,	lowercase = True		,	)			->						Tuple:
													_a  :    Dict			=						self._execution_device

													_a  :    Union[str, Any]			=						guidance_scale > 1.0

													if isinstance(lowercase		,	lowercase       ):
																				_a  :    Any			=						torch.cat(lowercase		,	dim=0       )
													if isinstance(lowercase		,	lowercase       ):
																				_a  :    Optional[Any]			=						torch.cat(lowercase		,	dim=0       )
													if isinstance(lowercase		,	lowercase       ):
																				_a  :    Union[str, Any]			=						torch.cat(lowercase		,	dim=0       )

													_a  :    List[Any]			=						image_embeds.shape[0]

													if do_classifier_free_guidance:
																				_a  :    Tuple			=						image_embeds.repeat_interleave(lowercase		,	dim=0       )
																				_a  :    Tuple			=						negative_image_embeds.repeat_interleave(lowercase		,	dim=0       )
																				_a  :    Optional[int]			=						hint.repeat_interleave(lowercase		,	dim=0       )

													_a  :    str			=						torch.cat([negative_image_embeds, image_embeds]		,	dim=0       ).to(dtype=self.unet.dtype		,	device=lowercase       )
													_a  :    Tuple			=						torch.cat([hint, hint]		,	dim=0       ).to(dtype=self.unet.dtype		,	device=lowercase       )

													if not isinstance(lowercase		,	lowercase       ):
																				_a  :    Dict			=						[image]
													if not all(isinstance(lowercase		,	(PIL.Image.Image, torch.Tensor)       ) for i in image       ):
																				raise ValueError(
																				    F'Input is in incorrect format: {[type(lowercase       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'       )

													_a  :    Any			=						torch.cat([prepare_image(lowercase		,	lowercase		,	lowercase       ) for i in image]		,	dim=0       )
													_a  :    Union[str, Any]			=						image.to(dtype=image_embeds.dtype		,	device=lowercase       )

													_a  :    Optional[int]			=						self.movq.encode(lowercase       )['''latents''']
													_a  :    List[Any]			=						latents.repeat_interleave(lowercase		,	dim=0       )
													self.scheduler.set_timesteps(lowercase		,	device=lowercase       )
													_a		,					_a  :    List[Any]			=						self.get_timesteps(lowercase		,	lowercase		,	lowercase       )
													_a  :    Optional[Any]			=						timesteps[:1].repeat(batch_size * num_images_per_prompt       )
													_a		,					_a  :    Any			=						downscale_height_and_width(lowercase		,	lowercase		,	self.movq_scale_factor       )
													_a  :    List[str]			=						self.prepare_latents(
													    lowercase		,	lowercase		,	lowercase		,	lowercase		,	image_embeds.dtype		,	lowercase		,	lowercase       )
													for i, t in enumerate(self.progress_bar(lowercase       )       ):
																				# expand the latents if we are doing classifier free guidance
																				_a  :    Union[str, Any]			=						torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

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

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

																				# compute the previous noisy sample x_t -> x_t-1

																				_a  :    Optional[int]			=						self.scheduler.step(
																				    lowercase		,	lowercase		,	lowercase		,	generator=lowercase		,	)[0]

													# post-processing
													_a  :    List[Any]			=						self.movq.decode(lowercase		,	force_not_quantize=lowercase       )['''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"]:
																				_a  :    Any			=						image * 0.5 + 0.5
																				_a  :    Union[str, Any]			=						image.clamp(0		,	1       )
																				_a  :    List[Any]			=						image.cpu().permute(0		,	2		,	3		,	1       ).float().numpy()

													if output_type == "pil":
																				_a  :    int			=						self.numpy_to_pil(lowercase       )

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowercase       )







from __future__ import annotations






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list[int]:







							"""simple docstring"""





							_a  :    Optional[Any]			=						[0] * max_one

							for i in range(0    ,	UpperCAmelCase     ):
														_a  :    int			=						i

							for i in range(2    ,	UpperCAmelCase     ):
														if totients[i] == i:
																					for j in range(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     ):
																												totients[j] -= totients[j] // i

							return totients






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		bool:







							"""simple docstring"""





							return sorted(str(UpperCAmelCase     )     ) == sorted(str(UpperCAmelCase     )     )






def    UpperCamelCase__  (   UpperCAmelCase = 10000000     )     ->		int:







							"""simple docstring"""





							_a  :    List[Any]			=						1  # i
							_a  :    Any			=						0  # φ(i)
							_a  :    Tuple			=						get_totients(max_n + 1     )

							for i in range(2    ,	max_n + 1     ):
														_a  :    List[str]			=						totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase    ,	UpperCAmelCase     ):
																					_a  :    Any			=						i
																					_a  :    Union[str, Any]			=						t

							return min_numerator


if __name__ == "__main__":
						print(F'''{solution() = }''')