




'''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 __magic_name__:






						def __init__(   self						:						Union[str, Any]					,   __UpperCamelCase						:						Optional[Any]					,   __UpperCamelCase						:						Optional[Any]=sys.maxsize       ):



									'''simple docstring'''






									snake_case__          =						"""bilinear"""
									snake_case__          =						max_size
									snake_case__          =						short_edge_length







						def __call__(   self						:						int					,   __UpperCamelCase						:						int       ):



									'''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(a_					,   a_       )
												if h < w:
															snake_case__    ,			snake_case__          =						size, scale * w
												else:
															snake_case__    ,			snake_case__          =						scale * h, size
												if max(a_					,   a_       ) > self.max_size:
															snake_case__          =						self.max_size * 1.0 / max(a_					,   a_       )
															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(a_       )
															snake_case__          =						pil_image.resize((neww, newh)					,   PILImageResampling.BILINEAR       )
															snake_case__          =						np.asarray(a_       )
												else:
															snake_case__          =						img.permute(2					,   0					,   1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
															snake_case__          =						nn.functional.interpolate(
															    a_					,   (newh, neww)					,   mode=self.interp_method					,   align_corners=a_       ).squeeze(0       )
												img_augs.append(a_       )

									return img_augs







class __magic_name__:






						def __init__(   self						:						int					,   __UpperCamelCase						:						Dict       ):



									'''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 __UpperCamelCase       : (x - self.pixel_mean) / self.pixel_std






						def       __lowerCAmelCase(   self						:						Dict					,   __UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						tuple(max(a_       ) for s in zip(*[img.shape for img in images]       )       )
									snake_case__          =						[im.shape[-2:] for im in images]
									snake_case__          =						[
									    nn.functional.pad(
									        a_					,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]					,   value=self.pad_value					,   )
									    for size, im in zip(a_					,   a_       )
									]

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







						def __call__(   self						:						List[Any]					,   __UpperCamelCase						:						Optional[int]					,   __UpperCamelCase						:						List[Any]=False       ):



									'''simple docstring'''






									with torch.no_grad():
												if not isinstance(a_					,   a_       ):
															snake_case__          =						[images]
												if single_image:
															assert len(a_       ) == 1
												for i in range(len(a_       )       ):
															if isinstance(images[i]					,   torch.Tensor       ):
																		images.insert(a_					,   images.pop(a_       ).to(self.device       ).float()       )
															elif not isinstance(images[i]					,   torch.Tensor       ):
																		images.insert(
																		    a_					,   torch.as_tensor(img_tensorize(images.pop(a_       )					,   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(a_       )
												# 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(a_       ) for x in images]
												# now pad them to do the following operations
												snake_case__    ,			snake_case__          =						self.pad(a_       )
												# Normalize

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







def    snake_case__     (  a       ,      a	)			->       str:





			'''simple docstring'''






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




def    snake_case__     (  a       ,      a	)			->       int:





			'''simple docstring'''






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



'''simple docstring'''

from __future__ import annotations



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					list[list[int]]     ,		_UpperCamelCase   :					list[list[int]] )		->       list[int]:





   '''simple docstring'''







   assert len(_UpperCamelCase ) == len(_UpperCamelCase )

   UpperCamelCase__			    =    len(_UpperCamelCase )
   UpperCamelCase__			    =    list(range(_UpperCamelCase ) )
   UpperCamelCase__			    =    [-1] * n  # who the donor has donated to
   UpperCamelCase__			    =    [-1] * n  # who the recipient has received from
   UpperCamelCase__			    =    [0] * n

   while unmatched_donors:
      UpperCamelCase__			    =    unmatched_donors[0]
      UpperCamelCase__			    =    donor_pref[donor]
      UpperCamelCase__			    =    donor_preference[num_donations[donor]]
      num_donations[donor] += 1
      UpperCamelCase__			    =    recipient_pref[recipient]
      UpperCamelCase__			    =    rec_record[recipient]

      if prev_donor != -1:
         if rec_preference.index(_UpperCamelCase ) > rec_preference.index(_UpperCamelCase ):
            UpperCamelCase__			    =    donor
            UpperCamelCase__			    =    recipient
            unmatched_donors.append(_UpperCamelCase )
            unmatched_donors.remove(_UpperCamelCase )
      else:
         UpperCamelCase__			    =    donor
         UpperCamelCase__			    =    recipient
         unmatched_donors.remove(_UpperCamelCase )
   return donor_record