





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

__lowerCamelCase : 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_		,							lowerCAmelCase_		,							lowerCAmelCase_=8							):

				"""simple docstring"""






				lowercase         = height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowercase         = 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      :	str							,			A__      :	UNetaDConditionModel							,			A__      :	DDPMScheduler							,			A__      :	VQModel							,			)					-> Optional[int]:
						super().__init__()

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




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

						lowercase         = latents * scheduler.init_noise_sigma
						return latents




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Optional[int]=0				)					-> Any:
						if is_accelerate_available():
										from accelerate import cpu_offload
						else:
										raise ImportError("Please install accelerate via `pip install accelerate`"				)

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

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




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	str=0				)					-> str:
						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."				)

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

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

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

						# We'll offload the last model manually.
						lowercase         = hook




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

						lowercase         = guidance_scale > 1.0

						if isinstance(A__							,			A__				):
										lowercase         = torch.cat(A__							,			dim=0				)
						if isinstance(A__							,			A__				):
										lowercase         = torch.cat(A__							,			dim=0				)
						if isinstance(A__							,			A__				):
										lowercase         = torch.cat(A__							,			dim=0				)

						lowercase         = image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
										lowercase         = image_embeds.repeat_interleave(A__							,			dim=0				)
										lowercase         = negative_image_embeds.repeat_interleave(A__							,			dim=0				)
										lowercase         = hint.repeat_interleave(A__							,			dim=0				)

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

						lowercase         = self.movq.config.latent_channels

						lowercase   ,							lowercase         = downscale_height_and_width(A__							,			A__							,			self.movq_scale_factor				)

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

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

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

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

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

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

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=A__				)











'''simple docstring'''

import functools







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if not isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for day in days							):
								raise ValueError("The parameter days should be a list of integers"							)

				if len(lowerCAmelCase_							) != 3 or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for cost in costs							):
								raise ValueError("The parameter costs should be a list of three integers"							)

				if len(lowerCAmelCase_							) == 0:
								return 0

				if min(lowerCAmelCase_							) <= 0:
								raise ValueError("All days elements should be greater than 0"							)

				if max(lowerCAmelCase_							) >= 366:
								raise ValueError("All days elements should be less than 366"							)

				lowercase         = set(lowerCAmelCase_							)

				@functools.cache
				def dynamic_programming(lowerCAmelCase_							) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1							)

								return min(
								    costs[0] + dynamic_programming(index + 1							)		,							costs[1] + dynamic_programming(index + 7							)		,							costs[2] + dynamic_programming(index + 30							)		,							)

				return dynamic_programming(1							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




