


'''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	=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 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      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=8					):
			UpperCAmelCase__  :					str        =     height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCAmelCase__  :					Dict        =     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					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							):
								super().__init__()

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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								if latents is None:
											UpperCAmelCase__  :					Dict        =     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__  :					Any        =     latents.to(_lowerCamelCase)

								UpperCAmelCase__  :					Tuple        =     latents * scheduler.init_noise_sigma
								return latents




					def 				snake_case__						(      self							,							_lowerCamelCase=0):
								if is_accelerate_available():
											from accelerate import cpu_offload
								else:
											raise ImportError("""Please install accelerate via `pip install accelerate`""")

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

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




					def 				snake_case__						(      self							,							_lowerCamelCase=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__  :					Optional[int]        =     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__  :					List[Any]        =     None
								for cpu_offloaded_model in [self.unet, self.movq]:
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Union[str, Any]        =     cpu_offload_with_hook(_lowerCamelCase							,							_lowerCamelCase							,							prev_module_hook=_lowerCamelCase)

								# We'll offload the last model manually.
								UpperCAmelCase__  :					str        =     hook




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 				snake_case__						(      self):
								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							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = 512							,							_lowerCamelCase = 512							,							_lowerCamelCase = 100							,							_lowerCamelCase = 4.0							,							_lowerCamelCase = 1							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = "pil"							,							_lowerCamelCase = True							,							):
								UpperCAmelCase__  :					Any        =     self._execution_device

								UpperCAmelCase__  :					int        =     guidance_scale > 1.0

								if isinstance(_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__  :					Optional[int]        =     torch.cat(_lowerCamelCase							,							dim=0)
								if isinstance(_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__  :					List[Any]        =     torch.cat(_lowerCamelCase							,							dim=0)
								if isinstance(_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__  :					str        =     torch.cat(_lowerCamelCase							,							dim=0)

								UpperCAmelCase__  :					Tuple        =     image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
											UpperCAmelCase__  :					List[str]        =     image_embeds.repeat_interleave(_lowerCamelCase							,							dim=0)
											UpperCAmelCase__  :					Optional[int]        =     negative_image_embeds.repeat_interleave(_lowerCamelCase							,							dim=0)
											UpperCAmelCase__  :					int        =     hint.repeat_interleave(_lowerCamelCase							,							dim=0)

								UpperCAmelCase__  :					List[Any]        =     torch.cat([negative_image_embeds, image_embeds]							,							dim=0).to(dtype=self.unet.dtype							,							device=_lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     torch.cat([hint, hint]							,							dim=0).to(dtype=self.unet.dtype							,							device=_lowerCamelCase)
								self.scheduler.set_timesteps(_lowerCamelCase							,							device=_lowerCamelCase)
								UpperCAmelCase__  :					Tuple        =     self.scheduler.timesteps

								UpperCAmelCase__  :					str        =     self.movq.config.latent_channels

								UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     downscale_height_and_width(_lowerCamelCase							,							_lowerCamelCase							,							self.movq_scale_factor)

								# create initial latent
								UpperCAmelCase__  :					Any        =     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__  :					Dict        =     torch.cat([latents] * 2) if do_classifier_free_guidance else latents

											UpperCAmelCase__  :					Union[str, Any]        =     {"""image_embeds""": image_embeds, """hint""": hint}
											UpperCAmelCase__  :					Optional[int]        =     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__  :					List[Any]        =     noise_pred.split(latents.shape[1]							,							dim=1)
														UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     noise_pred.chunk(2)
														UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     variance_pred.chunk(2)
														UpperCAmelCase__  :					str        =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
														UpperCAmelCase__  :					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"]
											):
														UpperCAmelCase__   ,       UpperCAmelCase__  :					Union[str, Any]        =     noise_pred.split(latents.shape[1]							,							dim=1)

											# compute the previous noisy sample x_t -> x_t-1
											UpperCAmelCase__  :					str        =     self.scheduler.step(
											    _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							generator=_lowerCamelCase							,							)[0]
								# post-processing
								UpperCAmelCase__  :					str        =     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__  :					Optional[int]        =     image * 0.5 + 0.5
											UpperCAmelCase__  :					Tuple        =     image.clamp(0							,							1)
											UpperCAmelCase__  :					Dict        =     image.cpu().permute(0							,							2							,							3							,							1).float().numpy()

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

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=_lowerCamelCase)



'''simple docstring'''



from itertools import permutations







def     _UpperCamelCase      (		UpperCamelCase__					):
			if num[3] % 2 != 0:
						return False

			if (num[2] + num[3] + num[4]) % 3 != 0:
						return False

			if num[5] % 5 != 0:
						return False

			UpperCAmelCase__  :					Optional[int]        =     [7, 1_1, 1_3, 1_7]
			for i, test in enumerate(UpperCamelCase__					):
						if (num[i + 4] * 1_0_0 + num[i + 5] * 1_0 + num[i + 6]) % test != 0:
									return False
			return True







def     _UpperCamelCase      (		UpperCamelCase__ = 1_0					):
			return sum(
			    int("""""".join(map(UpperCamelCase__ ,      UpperCamelCase__					)					)					)
			    for num in permutations(range(UpperCamelCase__					)					)
			    if is_substring_divisible(UpperCamelCase__					)					)


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