





"""simple docstring"""
class    _UpperCAmelCase  :


					def __init__(   self     :Dict						):
												A						=    [0]
												A						=    0


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :List[Any]						):
												A						=    self.__heap[i]
												while i // 2 > 0:
																			if self.__heap[i] > self.__heap[i // 2]:
																										A						=    self.__heap[i // 2]
																										A						=    temporary
																			i //= 2


					def       lowerCamelCase			(   self     :Union[str, Any]	,			__UpperCamelCase     :Optional[Any]						):
												self.__heap.append(snake_case_						)
												self.__size += 1
												self.__swap_up(self.__size						)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Tuple						):
												while self.__size >= 2 * i:
																			if 2 * i + 1 > self.__size:
																										A						=    2 * i
																			else:
																										if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																																	A						=    2 * i
																										else:
																																	A						=    2 * i + 1
																			A						=    self.__heap[i]
																			if self.__heap[i] < self.__heap[bigger_child]:
																										A						=    self.__heap[bigger_child]
																										A						=    temporary
																			A						=    bigger_child


					def       lowerCamelCase			(   self     :int						):
												A						=    self.__heap[1]
												A						=    self.__heap[self.__size]
												self.__size -= 1
												self.__heap.pop()
												self.__swap_down(1						)
												return max_value


					@property
					def       lowerCamelCase			(   self     :Tuple						):
												return self.__heap[1:]


					def __len__(   self     :Optional[int]						):
												return self.__size


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# create an instance of BinaryHeap
		_snake_case	:						Any					=					BinaryHeap()
		binary_heap.insert(6)
		binary_heap.insert(10)
		binary_heap.insert(15)
		binary_heap.insert(12)
		# pop root(max-values because it is max heap)
		print(binary_heap.pop())  # 15
		print(binary_heap.pop())  # 12
		# get the list and size after operations
		print(binary_heap.get_list)
		print(len(binary_heap))



'''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				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






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

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

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






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



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

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






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# 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 :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





