



"""simple docstring"""
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,
)


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

__a           =							"\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   A_    (     _lowercase,   _lowercase,   _lowercase=8      ):







						'''simple docstring'''


						snake_case_					:Optional[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
def   A_    (     _lowercase,   _lowercase=512,   _lowercase=512      ):







						'''simple docstring'''


						snake_case_					:Dict		=     pil_image.resize((w, h),   resample=Image.BICUBIC,   reducing_gap=1      )
						snake_case_					:Optional[int]		=     np.array(pil_image.convert("""RGB"""      )      )
						snake_case_					:int		=     arr.astype(np.floataa      ) / 127.5 - 1
						snake_case_					:Union[str, Any]		=     np.transpose(_lowercase,   [2, 0, 1]      )
						snake_case_					:int		=     torch.from_numpy(_lowercase      ).unsqueeze(0      )
						return image

class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					def __init__(			self:				Optional[Any]		,	snake_case:				UNetaDConditionModel		,	snake_case:				DDPMScheduler		,	snake_case:				VQModel		,	)  ->      Tuple:
											super().__init__()

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






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				List[Any]		,	snake_case:				List[str]		,	snake_case:				Any		)  ->      str:
											# get the original timestep using init_timestep
											snake_case_					:str		=     min(int(num_inference_steps * strength		)		,	snake_case		)

											snake_case_					:Union[str, Any]		=     max(num_inference_steps - init_timestep		,	0		)
											snake_case_					:Optional[Any]		=     self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				int		,	snake_case:				Optional[Any]		,	snake_case:				Optional[int]		,	snake_case:				List[Any]		,	snake_case:				str		,	snake_case:				List[str]		,	snake_case:				Optional[Any]=None		)  ->      Dict:
											if not isinstance(snake_case		,	(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(snake_case		)}"""		)

											snake_case_					:int		=     image.to(device=snake_case		,	dtype=snake_case		)

											snake_case_					:List[Any]		=     batch_size * num_images_per_prompt

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

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

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

																	snake_case_					:Union[str, Any]		=     self.movq.config.scaling_factor * init_latents

											snake_case_					:List[Any]		=     torch.cat([init_latents]		,	dim=0		)

											snake_case_					:List[str]		=     init_latents.shape
											snake_case_					:Dict		=     randn_tensor(snake_case		,	generator=snake_case		,	device=snake_case		,	dtype=snake_case		)

											# get latents
											snake_case_					:List[Any]		=     self.scheduler.add_noise(snake_case		,	snake_case		,	snake_case		)

											snake_case_					:List[str]		=     init_latents

											return latents






					def   lowerCAmelCase_							(			self:				Any		,	snake_case:				Tuple=0		)  ->      Union[str, Any]:
											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

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

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






					def   lowerCAmelCase_							(			self:				List[Any]		,	snake_case:				str=0		)  ->      Optional[int]:
											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[int]		=     torch.device(f"""cuda:{gpu_id}"""		)

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

											snake_case_					:int		=     None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	snake_case_,			snake_case_					:Dict		=     cpu_offload_with_hook(snake_case		,	snake_case		,	prev_module_hook=snake_case		)

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






					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   lowerCAmelCase_							(			self:				int		)  ->      str:
											if not hasattr(self.unet		,	"""_hf_hook"""		):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(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(snake_case		)
					def __call__(			self:				Union[str, Any]		,	snake_case:				Union[torch.FloatTensor, List[torch.FloatTensor]]		,	snake_case:				Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]		,	snake_case:				Union[torch.FloatTensor, List[torch.FloatTensor]]		,	snake_case:				torch.FloatTensor		,	snake_case:				int = 512		,	snake_case:				int = 512		,	snake_case:				int = 100		,	snake_case:				float = 4.0		,	snake_case:				float = 0.3		,	snake_case:				int = 1		,	snake_case:				Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	snake_case:				Optional[str] = "pil"		,	snake_case:				bool = True		,	)  ->      str:
											snake_case_					:Union[str, Any]		=     self._execution_device

											snake_case_					:str		=     guidance_scale > 1.0

											if isinstance(snake_case		,	snake_case		):
																	snake_case_					:Dict		=     torch.cat(snake_case		,	dim=0		)
											if isinstance(snake_case		,	snake_case		):
																	snake_case_					:Optional[Any]		=     torch.cat(snake_case		,	dim=0		)
											if isinstance(snake_case		,	snake_case		):
																	snake_case_					:Union[str, Any]		=     torch.cat(snake_case		,	dim=0		)

											snake_case_					:Union[str, Any]		=     image_embeds.shape[0]

											if do_classifier_free_guidance:
																	snake_case_					:int		=     image_embeds.repeat_interleave(snake_case		,	dim=0		)
																	snake_case_					:str		=     negative_image_embeds.repeat_interleave(snake_case		,	dim=0		)
																	snake_case_					:int		=     hint.repeat_interleave(snake_case		,	dim=0		)

											snake_case_					:List[Any]		=     torch.cat([negative_image_embeds, image_embeds]		,	dim=0		).to(dtype=self.unet.dtype		,	device=snake_case		)
											snake_case_					:Tuple		=     torch.cat([hint, hint]		,	dim=0		).to(dtype=self.unet.dtype		,	device=snake_case		)

											if not isinstance(snake_case		,	snake_case		):
																	snake_case_					:Optional[Any]		=     [image]
											if not all(isinstance(snake_case		,	(PIL.Image.Image, torch.Tensor)		) for i in image		):
																	raise ValueError(
																	    f"""Input is in incorrect format: {[type(snake_case		) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""		)

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

											snake_case_					:Any		=     self.movq.encode(snake_case		)["""latents"""]
											snake_case_					:int		=     latents.repeat_interleave(snake_case		,	dim=0		)
											self.scheduler.set_timesteps(snake_case		,	device=snake_case		)
											snake_case_,			snake_case_					:Union[str, Any]		=     self.get_timesteps(snake_case		,	snake_case		,	snake_case		)
											snake_case_					:Tuple		=     timesteps[:1].repeat(batch_size * num_images_per_prompt		)
											snake_case_,			snake_case_					:List[Any]		=     downscale_height_and_width(snake_case		,	snake_case		,	self.movq_scale_factor		)
											snake_case_					:Tuple		=     self.prepare_latents(
											    snake_case		,	snake_case		,	snake_case		,	snake_case		,	image_embeds.dtype		,	snake_case		,	snake_case		)
											for i, t in enumerate(self.progress_bar(snake_case		)		):
																	# expand the latents if we are doing classifier free guidance
																	snake_case_					:Any		=     torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

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

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

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

																	snake_case_					:Union[str, Any]		=     self.scheduler.step(
																	    snake_case		,	snake_case		,	snake_case		,	generator=snake_case		,	)[0]

											# post-processing
											snake_case_					:Optional[Any]		=     self.movq.decode(snake_case		,	force_not_quantize=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[str]		=     image * 0.5 + 0.5
																	snake_case_					:int		=     image.clamp(0		,	1		)
																	snake_case_					:Optional[Any]		=     image.cpu().permute(0		,	2		,	3		,	1		).float().numpy()

											if output_type == "pil":
																	snake_case_					:int		=     self.numpy_to_pil(snake_case		)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=snake_case		)






from math import sqrt


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Dict      =     [1]
  for i in range(2       ,  int(sqrt(UpperCamelCase				)				) + 1       ,  1				):
    if number % i == 0:
      values.append(UpperCamelCase				)
      if int(number // i				) != i:
        values.append(int(number // i				)				)
  return sorted(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return sum(factors(UpperCamelCase				)				) > n


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  _lowercase:			Dict      =     factors(UpperCamelCase				)
  _lowercase:			int      =     len(UpperCamelCase				)
  _lowercase:			Optional[int]      =     [[0 for i in range(number + 1				)] for j in range(r + 1				)]
  for i in range(r + 1				):
    _lowercase:			List[Any]      =     True

  for i in range(1       ,  number + 1				):
    _lowercase:			Optional[int]      =     False

  for i in range(1       ,  r + 1				):
    for j in range(1       ,  number + 1				):
      if j < values[i - 1]:
        _lowercase:			Optional[int]      =     subset[i - 1][j]
      else:
        _lowercase:			Optional[Any]      =     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

  return subset[r][number] != 0


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return abundant(UpperCamelCase				) and not semi_perfect(UpperCamelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(f'''{number} is {'' if weird(number) else 'not '}weird.''')
