





'''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 lowerCamelCase    :







  def __init__(		self  ,  lowercase__  ,  lowercase__=sys.maxsize		)		->    Any:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  "bilinear"
        _snake_case		:						str         =  max_size
        _snake_case		:						Optional[int]         =  short_edge_length







  def __call__(		self  ,  lowercase__		)		->    List[Any]:






        """simple docstring"""





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

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

        return img_augs





class lowerCamelCase    :







  def __init__(		self  ,  lowercase__		)		->    Union[str, Any]:






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self  ,  lowercase__		)		->    int:






        """simple docstring"""





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

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







  def __call__(		self  ,  lowercase__  ,  lowercase__=False		)		->    Tuple:






        """simple docstring"""





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

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




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



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




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      assert torch.isfinite(lowerCAmelCase_						).all(), "Box tensor contains infinite or NaN!"
      _snake_case		:						int         =  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

from collections.abc import Generator







def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					) or n < 1:
										raise Exception("Sequence only defined for positive integers"					)

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_snake_case							:       Any												= 3 * n + 1
										yield n







def  A					(				):
					_snake_case							:       Dict												= 43
					_snake_case							:       List[str]												= tuple(collatz_sequence(UpperCAmelCase					)					)
					print(UpperCAmelCase					)
					print(F"""Collatz sequence from {n} took {len(UpperCAmelCase					)} steps."""					)


if __name__ == "__main__":
							main()