




'''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     lowercase  :






      def __init__(self			,	a__			,	a__=sys.maxsize       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			'''bilinear'''
         SCREAMING_SNAKE_CASE__      :					str							    =			max_size
         SCREAMING_SNAKE_CASE__      :					Dict							    =			short_edge_length






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

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

         return img_augs






class     lowercase  :






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






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

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


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

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







def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

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







'''simple docstring'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.''' )
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


if __name__ == "__main__":
       print(F"{solution() = }")

