
'''simple docstring'''







from graphs.minimum_spanning_tree_kruskal import kruskal







def        SCREAMING_SNAKE_CASE__			(     ) ->			Dict:
	_snake_case     		=	9
	_snake_case     		=	[
	    [0, 1, 4],
	    [0, 7, 8],
	    [1, 2, 8],
	    [7, 8, 7],
	    [7, 6, 1],
	    [2, 8, 2],
	    [8, 6, 6],
	    [2, 3, 7],
	    [2, 5, 4],
	    [6, 5, 2],
	    [3, 5, 14],
	    [3, 4, 9],
	    [5, 4, 10],
	    [1, 7, 11],
	]

	_snake_case     		=	kruskal(__A    ,       __A			)

	_snake_case     		=	[
	    [7, 6, 1],
	    [2, 8, 2],
	    [6, 5, 2],
	    [0, 1, 4],
	    [2, 5, 4],
	    [2, 3, 7],
	    [0, 7, 8],
	    [3, 4, 9],
	]

	assert sorted(__A			) == sorted(__A			)



'''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 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=sys.maxsize						):





								"""simple docstring"""






								_snake_case     		=	'bilinear'
								_snake_case     		=	max_size
								_snake_case     		=	short_edge_length




							def __call__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






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

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

								return img_augs


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]					,     cfg.INPUT.MAX_SIZE_TEST						)
								_snake_case     		=	cfg.INPUT.FORMAT
								_snake_case     		=	cfg.SIZE_DIVISIBILITY
								_snake_case     		=	cfg.PAD_VALUE
								_snake_case     		=	cfg.INPUT.MAX_SIZE_TEST
								_snake_case     		=	cfg.MODEL.DEVICE
								_snake_case     		=	torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)					,     1					,     1						)
								_snake_case     		=	torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)					,     1					,     1						)
								_snake_case     		=	lambda lowerCAmelCase_						: (x - self.pixel_mean) / self.pixel_std


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	tuple(max(lowerCAmelCase_						) for s in zip(*[img.shape for img in images]						)						)
								_snake_case     		=	[im.shape[-2:] for im in images]
								_snake_case     		=	[
								    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						):





								"""simple docstring"""






								with torch.no_grad():
									if not isinstance(lowerCAmelCase_					,     lowerCAmelCase_						):
										_snake_case     		=	[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
									_snake_case     		=	torch.tensor([im.shape[:2] for im in images]						)
									_snake_case     		=	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
									_snake_case     		=	[self.normalizer(lowerCAmelCase_						) for x in images]
									# now pad them to do the following operations
									_snake_case		,				_snake_case     		=	self.pad(lowerCAmelCase_						)
									# Normalize

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			List[Any]:
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			List[Any]:
	assert torch.isfinite(__A			).all(), "Box tensor contains infinite or NaN!"
	_snake_case		,				_snake_case     		=	box_size
	tensor[:, 0].clamp_(min=0    ,       max=__A			)
	tensor[:, 1].clamp_(min=0    ,       max=__A			)
	tensor[:, 2].clamp_(min=0    ,       max=__A			)
	tensor[:, 3].clamp_(min=0    ,       max=__A			)

