
"""simple docstring"""






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCamelCase__ :       Union[str, Any]						     =			logging.get_logger(__name__)  # pylint: disable=invalid-name





def __A						(   a_ :							Union[List, PIL.Image.Image, torch.Tensor]     )->							List[Any]:




		'''simple docstring'''






		warnings.warn(
		    '''The preprocess method is deprecated and will be removed in a future version. Please'''
		    ''' use VaeImageProcessor.preprocess instead'''		,		a_		,		)
		if isinstance(a_		,		torch.Tensor     ):
				return image
		elif isinstance(a_		,		PIL.Image.Image     ):
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [image]

		if isinstance(image[0]		,		PIL.Image.Image     ):
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= image[0].size
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE       :     str				= [np.array(i.resize((w, h)		,		resample=PIL_INTERPOLATION['''lanczos''']     )     )[None, :] for i in image]
				SCREAMING_SNAKE_CASE       :     str				= np.concatenate(a_		,		axis=0     )
				SCREAMING_SNAKE_CASE       :     List[str]				= np.array(a_     ).astype(np.floataa     ) / 255.0
				SCREAMING_SNAKE_CASE       :     List[str]				= image.transpose(0		,		3		,		1		,		2     )
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 2.0 * image - 1.0
				SCREAMING_SNAKE_CASE       :     List[Any]				= torch.from_numpy(a_     )
		elif isinstance(image[0]		,		torch.Tensor     ):
				SCREAMING_SNAKE_CASE       :     Tuple				= torch.cat(a_		,		dim=0     )
		return image





def __A						(   a_ :							Union[List, PIL.Image.Image, torch.Tensor]     )->							Any:




		'''simple docstring'''






		if isinstance(a_		,		torch.Tensor     ):
				return mask
		elif isinstance(a_		,		PIL.Image.Image     ):
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [mask]

		if isinstance(mask[0]		,		PIL.Image.Image     ):
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= mask[0].size
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE       :     List[str]				= [np.array(m.convert('''L'''     ).resize((w, h)		,		resample=PIL_INTERPOLATION['''nearest''']     )     )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE       :     List[str]				= np.concatenate(a_		,		axis=0     )
				SCREAMING_SNAKE_CASE       :     Any				= mask.astype(np.floataa     ) / 255.0
				SCREAMING_SNAKE_CASE       :     str				= 0
				SCREAMING_SNAKE_CASE       :     str				= 1
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.from_numpy(a_     )
		elif isinstance(mask[0]		,		torch.Tensor     ):
				SCREAMING_SNAKE_CASE       :     Tuple				= torch.cat(a_		,		dim=0     )
		return mask




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  42
	UpperCamelCase				=  42





	def __init__(						self    :Union[str, Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :List[Any]					)							->			Optional[int]:







			'''simple docstring'''




			super().__init__()
			self.register_modules(unet=lowerCamelCase_		, scheduler=lowerCamelCase_					)







	@torch.no_grad()
	def __call__(						self    :Tuple		, lowerCamelCase_    :Union[torch.Tensor, PIL.Image.Image]		, lowerCamelCase_    :Union[torch.Tensor, PIL.Image.Image]		, lowerCamelCase_    :int = 2_50		, lowerCamelCase_    :float = 0.0		, lowerCamelCase_    :int = 10		, lowerCamelCase_    :int = 10		, lowerCamelCase_    :Optional[Union[torch.Generator, List[torch.Generator]]] = None		, lowerCamelCase_    :Optional[str] = "pil"		, lowerCamelCase_    :bool = True		, )							->			Union[ImagePipelineOutput, Tuple]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= image

			SCREAMING_SNAKE_CASE       :     Any				= _preprocess_image(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= original_image.to(device=self.device		, dtype=self.unet.dtype					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= _preprocess_mask(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= mask_image.to(device=self.device		, dtype=self.unet.dtype					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= original_image.shape[0]

			# sample gaussian noise to begin the loop
			if isinstance(lowerCamelCase_		, lowerCamelCase_					) and len(lowerCamelCase_					) != batch_size:
					raise ValueError(
					    f"You have passed a list of generators of length {len(lowerCamelCase_					)}, but requested an effective batch"
					    f" size of {batch_size}. Make sure the batch size matches the length of the generators."					)

			SCREAMING_SNAKE_CASE       :     Tuple				= original_image.shape
			SCREAMING_SNAKE_CASE       :     List[Any]				= randn_tensor(lowerCamelCase_		, generator=lowerCamelCase_		, device=self.device		, dtype=self.unet.dtype					)

			# set step values
			self.scheduler.set_timesteps(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, self.device					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= eta

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.scheduler.timesteps[0] + 1
			SCREAMING_SNAKE_CASE       :     Dict				= generator[0] if isinstance(lowerCamelCase_		, lowerCamelCase_					) else generator
			for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
					if t < t_last:
							# predict the noise residual
							SCREAMING_SNAKE_CASE       :     str				= self.unet(lowerCamelCase_		, lowerCamelCase_					).sample
							# compute previous image: x_t -> x_t-1
							SCREAMING_SNAKE_CASE       :     int				= self.scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					).prev_sample

					else:
							# compute the reverse: x_t-1 -> x_t
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.scheduler.undo_step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= t

			SCREAMING_SNAKE_CASE       :     Any				= (image / 2 + 0.5).clamp(0		, 1					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= image.cpu().permute(0		, 2		, 3		, 1					).numpy()
			if output_type == "pil":
					SCREAMING_SNAKE_CASE       :     List[str]				= self.numpy_to_pil(lowerCamelCase_					)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=lowerCamelCase_					)







"""simple docstring"""






import math





def __A						(   a_ :							list		,		a_ :							int     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= len(a_     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= int(math.floor(math.sqrt(a_     )     )     )
		SCREAMING_SNAKE_CASE       :     List[str]				= 0
		while arr[min(a_		,		a_     ) - 1] < x:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= step
				step += int(math.floor(math.sqrt(a_     )     )     )
				if prev >= n:
						return -1

		while arr[prev] < x:
				SCREAMING_SNAKE_CASE       :     Any				= prev + 1
				if prev == min(a_		,		a_     ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
					lowerCamelCase__ :       Union[str, Any]						     =			input("Enter numbers separated by a comma:\n").strip()
					lowerCamelCase__ :       List[str]						     =			[int(item) for item in user_input.split(",")]
					lowerCamelCase__ :       Dict						     =			int(input("Enter the number to be searched:\n"))
					lowerCamelCase__ :       Tuple						     =			jump_search(arr, x)
					if res == -1:
										print("Number not found!")
					else:
										print(f'''Number {x} is at index {res}''')





