






"""simple docstring"""
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=sys.maxsize							):
		A_		:						str	    =   """bilinear"""
		A_		:						Tuple	    =   max_size
		A_		:						List[Any]	    =   short_edge_length





	def __call__(self     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   []
		for img in imgs:
			A_		:						Union[str, Any]	    =   img.shape[:2]
			# later: provide list and randomly choose index for resize
			A_		:						Tuple	    =   np.random.randint(self.short_edge_length[0]     ,     self.short_edge_length[1] + 1							)
			if size == 0:
				return img
			A_		:						List[Any]	    =   size * 1.0 / min(lowerCAmelCase_     ,     lowerCAmelCase_							)
			if h < w:
				A_		:						Union[str, Any]	    =   size, scale * w
			else:
				A_		:						Tuple	    =   scale * h, size
			if max(lowerCAmelCase_     ,     lowerCAmelCase_							) > self.max_size:
				A_		:						Tuple	    =   self.max_size * 1.0 / max(lowerCAmelCase_     ,     lowerCAmelCase_							)
				A_		:						Union[str, Any]	    =   newh * scale
				A_		:						Optional[Any]	    =   neww * scale
			A_		:						List[Any]	    =   int(neww + 0.5							)
			A_		:						Tuple	    =   int(newh + 0.5							)

			if img.dtype == np.uinta:
				A_		:						Union[str, Any]	    =   Image.fromarray(lowerCAmelCase_							)
				A_		:						Union[str, Any]	    =   pil_image.resize((neww, newh)     ,     PILImageResampling.BILINEAR							)
				A_		:						Dict	    =   np.asarray(lowerCAmelCase_							)
			else:
				A_		:						Dict	    =   img.permute(2     ,     0     ,     1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
				A_		:						Any	    =   nn.functional.interpolate(
				    lowerCAmelCase_     ,     (newh, neww)     ,     mode=self.interp_method     ,     align_corners=lowerCAmelCase_							).squeeze(0							)
			img_augs.append(lowerCAmelCase_							)

		return img_augs






class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]     ,     cfg.INPUT.MAX_SIZE_TEST							)
		A_		:						int	    =   cfg.INPUT.FORMAT
		A_		:						Dict	    =   cfg.SIZE_DIVISIBILITY
		A_		:						Union[str, Any]	    =   cfg.PAD_VALUE
		A_		:						Optional[Any]	    =   cfg.INPUT.MAX_SIZE_TEST
		A_		:						Union[str, Any]	    =   cfg.MODEL.DEVICE
		A_		:						Dict	    =   torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)     ,     1     ,     1							)
		A_		:						Any	    =   torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)     ,     1     ,     1							)
		A_		:						Optional[Any]	    =   lambda lowerCAmelCase_							: (x - self.pixel_mean) / self.pixel_std





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   tuple(max(lowerCAmelCase_							) for s in zip(*[img.shape for img in images]							)							)
		A_		:						Optional[int]	    =   [im.shape[-2:] for im in images]
		A_		:						Dict	    =   [
		    nn.functional.pad(
		        lowerCAmelCase_     ,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]     ,     value=self.pad_value     ,     )
		    for size, im in zip(lowerCAmelCase_     ,     lowerCAmelCase_							)
		]

		return torch.stack(lowerCAmelCase_							), torch.tensor(lowerCAmelCase_							)





	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=False							):
		with torch.no_grad():
			if not isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						Union[str, Any]	    =   [images]
			if single_image:
				assert len(lowerCAmelCase_							) == 1
			for i in range(len(lowerCAmelCase_							)							):
				if isinstance(images[i]     ,     torch.Tensor							):
					images.insert(lowerCAmelCase_     ,     images.pop(lowerCAmelCase_							).to(self.device							).float()							)
				elif not isinstance(images[i]     ,     torch.Tensor							):
					images.insert(
					    lowerCAmelCase_     ,     torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_							)     ,     input_format=self.input_format							)							)
					    .to(self.device							)
					    .float()     ,     )
            # resize smallest edge
			A_		:						str	    =   torch.tensor([im.shape[:2] for im in images]							)
			A_		:						List[Any]	    =   self.aug(lowerCAmelCase_							)
			# transpose images and convert to torch tensors
			# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
			# now normalize before pad to avoid useless arithmetic
			A_		:						Optional[int]	    =   [self.normalizer(lowerCAmelCase_							) for x in images]
			# now pad them to do the following operations
			A_		:						Optional[int]	    =   self.pad(lowerCAmelCase_							)
			# Normalize

			if self.size_divisibility > 0:
				raise NotImplementedError()
			# pad
			A_		:						Tuple	    =   torch.true_divide(lowerCAmelCase_     ,     lowerCAmelCase_							)
			if single_image:
				return images[0], sizes[0], scales_yx[0]
			else:
				return images, sizes, scales_yx






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	assert torch.isfinite(snake_case__					).all(), "Box tensor contains infinite or NaN!"
	A_		:						Dict	    =   box_size
	tensor[:, 0].clamp_(min=0   ,   max=snake_case__					)
	tensor[:, 1].clamp_(min=0   ,   max=snake_case__					)
	tensor[:, 2].clamp_(min=0   ,   max=snake_case__					)
	tensor[:, 3].clamp_(min=0   ,   max=snake_case__					)









"""simple docstring"""
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    "The `inpainting.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionInpaintPipeline` instead."
)

