



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 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						Union[str, Any]=sys.maxsize					)      ->			int:
							UpperCAmelCase_=       """bilinear"""
							UpperCAmelCase_=       max_size
							UpperCAmelCase_=       short_edge_length




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

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

							return img_augs






class 		lowercase						:






	"""simple docstring"""


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




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       tuple(max(__UpperCAmelCase					) for s in zip(*[img.shape for img in images]					)					)
							UpperCAmelCase_=       [im.shape[-2:] for im in images]
							UpperCAmelCase_=       [
							    nn.functional.pad(
							        __UpperCAmelCase						,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,  value=self.pad_value						,  )
							    for size, im in zip(__UpperCAmelCase						,  __UpperCAmelCase					)
							]

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




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

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



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : List[str]    )				->			str:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Tuple[int, int]    )				->			Dict:






						'''simple docstring'''



						assert torch.isfinite(lowerCAmelCase_    ).all(), "Box tensor contains infinite or NaN!"
						UpperCAmelCase_,       UpperCAmelCase_=       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_    )









from __future__ import annotations



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




