



"""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 			__snake_case			:




  def __init__(					self	:		str		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		str=sys.maxsize						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					'bilinear'
         _lowerCamelCase		:							int            =					max_size
         _lowerCamelCase		:							str            =					short_edge_length




  def __call__(					self	:		Optional[int]		,   __lowerCAmelCase	:		Union[str, Any]						):






         """simple docstring"""






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

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

         return img_augs






class 			__snake_case			:




  def __init__(					self	:		str		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					tuple(max(_lowerCamelCase						) for s in zip(*[img.shape for img in images]						)						)
         _lowerCamelCase		:							List[Any]            =					[im.shape[-2:] for im in images]
         _lowerCamelCase		:							Dict            =					[
             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	:		str		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Optional[int]=False						):






         """simple docstring"""






         with torch.no_grad():
                if not isinstance(_lowerCamelCase		,   _lowerCamelCase						):
                       _lowerCamelCase		:							List[Any]            =					[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
                _lowerCamelCase		:							Tuple            =					torch.tensor([im.shape[:2] for im in images]						)
                _lowerCamelCase		:							Union[str, Any]            =					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
                _lowerCamelCase		:							int            =					[self.normalizer(_lowerCamelCase						) for x in images]
                # now pad them to do the following operations
                _lowerCamelCase		:							List[str]            =					self.pad(_lowerCamelCase						)
                # Normalize

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






def 			snake_case_    ( A_   :						Union[str, Any],							A_   :						str		):


       '''simple docstring'''





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







def 			snake_case_    ( A_   :						Union[str, Any],							A_   :						str		):


       '''simple docstring'''





       assert torch.isfinite(__lowercase		).all(), "Box tensor contains infinite or NaN!"
       _lowerCamelCase		:							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 collections.abc import Callable





class 				a__					:



     def __init__(							self :Tuple      ,  _lowerCamelCase :Callable | None = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					list   	=[]
      # Stores indexes of each item for supporting updates and deletion.
      UpperCamelCase_     :					dict   	={}
      # Stores current size of heap.
      UpperCamelCase_     :					Any   	=0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      UpperCamelCase_     :					List[str]   	=key or (lambda _lowerCamelCase   : x)



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return int((i - 1) / 2   ) if i > 0 else None



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=int(2 * i + 1   )
      return left if 0 < left < self.size else None



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=int(2 * i + 2   )
      return right if 0 < right < self.size else None



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Optional[int]   	=(
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      UpperCamelCase_	,     UpperCamelCase_     :					Union[str, Any]   	=self.arr[j], self.arr[i]



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return self.arr[i][1] < self.arr[j][1]



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self._left(_lowerCamelCase   )
      UpperCamelCase_     :					List[Any]   	=self._right(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=i

      if left is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					Optional[int]   	=left
      if right is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					List[Any]   	=right

      return valid_parent



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=self._parent(_lowerCamelCase   )
      while parent is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=parent, self._parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=self._get_valid_parent(_lowerCamelCase   )
      while valid_parent != index:
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					int   	=valid_parent, self._get_valid_parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					List[Any]   	=self.pos_map[item]
      UpperCamelCase_     :					int   	=[item, self.key(_lowerCamelCase   )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_lowerCamelCase   )
      self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					Any   	=self.pos_map[item]
      del self.pos_map[item]
      UpperCamelCase_     :					Dict   	=self.arr[self.size - 1]
      UpperCamelCase_     :					Optional[int]   	=index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
       self._heapify_up(_lowerCamelCase   )
       self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=len(self.arr   )
      if arr_len == self.size:
       self.arr.append([item, self.key(_lowerCamelCase   )]   )
      else:
       UpperCamelCase_     :					str   	=[item, self.key(_lowerCamelCase   )]
      UpperCamelCase_     :					Optional[int]   	=self.size
      self.size += 1
      self._heapify_up(self.size - 1   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      return self.arr[0] if self.size else None




     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self.get_top()
      if top_item_tuple:
       self.delete_item(top_item_tuple[0]   )
      return top_item_tuple




def       A_  (				):
 pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()
