






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							:						List[str]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[Any]=sys.maxsize   ):
       SCREAMING_SNAKE_CASE							:   str						    =     "bilinear"
       SCREAMING_SNAKE_CASE							:   str						    =     max_size
       SCREAMING_SNAKE_CASE							:   int						    =     short_edge_length






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

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

       return img_augs





class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


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


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     tuple(max(UpperCAmelCase_   ) for s in zip(*[img.shape for img in images]   )   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     [im.shape[-2:] for im in images]
       SCREAMING_SNAKE_CASE							:   str						    =     [
           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							:						Any       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Union[str, Any]=False   ):
       with torch.no_grad():
         if not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     [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
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     torch.tensor([im.shape[:2] for im in images]   )
         SCREAMING_SNAKE_CASE							:   Any						    =     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
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [self.normalizer(UpperCAmelCase_   ) for x in images]
         # now pad them to do the following operations
         SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.pad(UpperCAmelCase_   )
         # Normalize

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





def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






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







'''simple docstring'''

from __future__ import annotations



def snake_case_      (					__snake_case  :							list[int | str])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0	,   [0 for i in range(len(__snake_case))])



def snake_case_      (					__snake_case  :							list[int | str]	,   __snake_case  :							list[int | str]	,   __snake_case  :							int	,   __snake_case  :							list[int]	,   )			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   for i in range(len(__snake_case)):
      if not index_used[i]:
         current_sequence.append(sequence[i])
         lowerCAmelCase_			=      True
         create_state_space_tree(__snake_case	,   __snake_case	,   index + 1	,   __snake_case)
         current_sequence.pop()
         lowerCAmelCase_			=      False
A_     :						list[int | str]    							=[3, 1, 2, 4]
generate_all_permutations(sequence)

A_     :						list[int | str]    							=["A", "B", "C"]






generate_all_permutations(sequence_a)





