





"""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,
)


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

__UpperCAmelCase						=			'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''






def      lowercase__ (					lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			List[str]=8			)    ->			List[Any]:



			'''simple docstring'''
			a__  :	Dict		 =			height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			a__  :	Dict		 =			width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor





class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				def __init__(		self    :			List[str]	, a_    :			UNetaDConditionModel	, a_    :			DDPMScheduler	, a_    :			VQModel	, )	->	Optional[Any]:



							'''simple docstring'''



							super().__init__()

							self.register_modules(
							    unet=a_	, scheduler=a_	, movq=a_	, )
							a__  :	Optional[int]		 =			2 ** (len(self.movq.config.block_out_channels    ) - 1)
				def       UpperCAmelCase      (		self    :			int	, a_    :			Optional[Any]	, a_    :			int	, a_    :			Dict	, a_    :			List[Any]	, a_    :			List[str]	, a_    :			Optional[int]    )	->	List[str]:



							'''simple docstring'''



							if latents is None:
										a__  :	List[Any]		 =			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}"    )
										a__  :	str		 =			latents.to(a_    )

							a__  :	Any		 =			latents * scheduler.init_noise_sigma
							return latents
				def       UpperCAmelCase      (		self    :			Any	, a_    :			str=0    )	->	Optional[int]:



							'''simple docstring'''



							if is_accelerate_available():
										from accelerate import cpu_offload
							else:
										raise ImportError("Please install accelerate via `pip install accelerate`"    )

							a__  :	Optional[Any]		 =			torch.device(F"cuda:{gpu_id}"    )

							a__  :	List[str]		 =			[
							    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    )	->	int:



							'''simple docstring'''



							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__  :	Optional[Any]		 =			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)

							a__  :	Tuple		 =			None
							for cpu_offloaded_model in [self.unet, self.movq]:
										a__ ,	a__  :	List[Any]		 =			cpu_offload_with_hook(a_	, a_	, prev_module_hook=a_    )

							# We'll offload the last model manually.
							a__  :	Any		 =			hook
				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	int:



							'''simple docstring'''



							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    :			Optional[Any]	, a_    :			Union[torch.FloatTensor, List[torch.FloatTensor]]	, a_    :			Union[torch.FloatTensor, List[torch.FloatTensor]]	, a_    :			torch.FloatTensor	, a_    :			int = 5_12	, a_    :			int = 5_12	, a_    :			int = 1_00	, 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:



							'''simple docstring'''



							a__  :	Any		 =			self._execution_device

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

							if isinstance(a_	, a_    ):
										a__  :	Dict		 =			torch.cat(a_	, dim=0    )
							if isinstance(a_	, a_    ):
										a__  :	Dict		 =			torch.cat(a_	, dim=0    )
							if isinstance(a_	, a_    ):
										a__  :	Any		 =			torch.cat(a_	, dim=0    )

							a__  :	Any		 =			image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
										a__  :	Optional[int]		 =			image_embeds.repeat_interleave(a_	, dim=0    )
										a__  :	List[Any]		 =			negative_image_embeds.repeat_interleave(a_	, dim=0    )
										a__  :	int		 =			hint.repeat_interleave(a_	, dim=0    )

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

							a__  :	Optional[int]		 =			self.movq.config.latent_channels

							a__ ,	a__  :	Tuple		 =			downscale_height_and_width(a_	, a_	, self.movq_scale_factor    )

							# create initial latent
							a__  :	Optional[Any]		 =			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
										a__  :	Optional[int]		 =			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

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

										if do_classifier_free_guidance:
													a__ ,	a__  :	List[Any]		 =			noise_pred.split(latents.shape[1]	, dim=1    )
													a__ ,	a__  :	List[Any]		 =			noise_pred.chunk(2    )
													a__ ,	a__  :	Dict		 =			variance_pred.chunk(2    )
													a__  :	List[str]		 =			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__  :	Dict		 =			noise_pred.split(latents.shape[1]	, dim=1    )

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

							if output_type == "pil":
										a__  :	List[str]		 =			self.numpy_to_pil(a_    )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a_    )






"""simple docstring"""






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			list[int]:



			'''simple docstring'''
			a__  :	List[str]		 =			[0 for i in range(len(lowerCAmelCase__			)			)]

			# initialize interval's left pointer and right pointer
			a__ ,	a__  :	int		 =			0, 0

			for i in range(1							,			len(lowerCAmelCase__			)			):
						# case when current index is inside the interval
						if i <= right_pointer:
									a__  :	List[Any]		 =			min(right_pointer - i + 1							,			z_result[i - left_pointer]			)
									a__  :	List[str]		 =			min_edge

						while go_next(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									a__ ,	a__  :	str		 =			i, i + z_result[i] - 1

			return z_result






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			list[int]							,			lowerCAmelCase__   :			str			)    ->			bool:



			'''simple docstring'''
			return i + z_result[i] < len(lowerCAmelCase__			) and s[z_result[i]] == s[i + z_result[i]]






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	List[Any]		 =			0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			a__  :	List[str]		 =			z_function(pattern + input_str			)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(lowerCAmelCase__			):
									answer += 1

			return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()