





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 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   Union[str, Any]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Optional[int]=sys.maxsize	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""bilinear"""
           SCREAMING_SNAKE_CASE		=						max_size
           SCREAMING_SNAKE_CASE		=						short_edge_length






     def __call__(							self						:   Tuple						,lowerCamelCase__						:   Optional[int]	)					->		List[Any]:




           '''simple docstring'''


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

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

           return img_augs

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   Any						,lowerCamelCase__						:   str	)					->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,cfg.INPUT.MAX_SIZE_TEST	)
           SCREAMING_SNAKE_CASE		=						cfg.INPUT.FORMAT
           SCREAMING_SNAKE_CASE		=						cfg.SIZE_DIVISIBILITY
           SCREAMING_SNAKE_CASE		=						cfg.PAD_VALUE
           SCREAMING_SNAKE_CASE		=						cfg.INPUT.MAX_SIZE_TEST
           SCREAMING_SNAKE_CASE		=						cfg.MODEL.DEVICE
           SCREAMING_SNAKE_CASE		=						torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)						,1						,1	)
           SCREAMING_SNAKE_CASE		=						torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)						,1						,1	)
           SCREAMING_SNAKE_CASE		=						lambda lowerCamelCase__	: (x - self.pixel_mean) / self.pixel_std






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   int	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						tuple(max(lowerCamelCase__	) for s in zip(*[img.shape for img in images]	)	)
           SCREAMING_SNAKE_CASE		=						[im.shape[-2:] for im in images]
           SCREAMING_SNAKE_CASE		=						[
               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						:   int						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   int=False	)					->		Optional[Any]:




           '''simple docstring'''


           with torch.no_grad():
                 if not isinstance(lowerCamelCase__						,lowerCamelCase__	):
                       SCREAMING_SNAKE_CASE		=						[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
                 SCREAMING_SNAKE_CASE		=						torch.tensor([im.shape[:2] for im in images]	)
                 SCREAMING_SNAKE_CASE		=						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
                 SCREAMING_SNAKE_CASE		=						[self.normalizer(lowerCamelCase__	) for x in images]
                 # now pad them to do the following operations
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.pad(lowerCamelCase__	)
                 # Normalize

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Dict:


      '''simple docstring'''


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









from __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						2
      SCREAMING_SNAKE_CASE		=						[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(_SCREAMING_SNAKE_CASE     )
      if n > 1:
            factors.append(_SCREAMING_SNAKE_CASE     )
      return factors


if __name__ == "__main__":
      import doctest

      doctest.testmod()


