
'''simple docstring'''







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			int:
	if index == r:
		for j in range(__A			):
			print(data[j]    ,       end=' '			)
		print(' '			)
		return
	#  When no more elements are there to put in data[]
	if i >= n:
		return
	# current is included, put next at next location
	_snake_case     		=	arr[i]
	combination_util(__A    ,       __A    ,       __A    ,       index + 1    ,       __A    ,       i + 1			)
	# current is excluded, replace it with
	# next (Note that i+1 is passed, but
	# index is not changed)
	combination_util(__A    ,       __A    ,       __A    ,       __A    ,       __A    ,       i + 1			)
	# The main function that prints all combinations
	# of size r in arr[] of size n. This function
	# mainly uses combinationUtil()







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			List[Any]:
	# A temporary array to store all combination one by one
	_snake_case     		=	[0] * r
	# Print all combination using temporary array 'data[]'
	combination_util(__A    ,       __A    ,       __A    ,       0    ,       __A    ,       0			)


if __name__ == "__main__":
					# Driver code to check the function above
					lowercase					:							Tuple   =      [10, 20, 30, 40, 50]
					print_combination(arr, len(arr), 3)
					# This code is contributed by Ambuj sahu



'''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			)

