


'''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 			UpperCAmelCase			:

 def __init__(			self			:						Union[str, Any],       a_			:						Optional[Any],       a_			:						Optional[Any]=sys.maxsize       ):





    """simple docstring"""



    UpperCamelCase__			    =    "bilinear"
    UpperCamelCase__			    =    max_size
    UpperCamelCase__			    =    short_edge_length

 def __call__(			self			:						int,       a_			:						int       ):





    """simple docstring"""



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

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

    return img_augs







class 			UpperCAmelCase			:

 def __init__(			self			:						int,       a_			:						Dict       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Dict,       a_			:						Tuple       ):





    """simple docstring"""



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





    """simple docstring"""



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

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







def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					int )		->       str:





   '''simple docstring'''







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






def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Tuple[int, int] )		->       int:





   '''simple docstring'''







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




'''simple docstring'''




import sys



def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     int:
							if not numbers:
														return 0
							if target == 0 and target in numbers:
														return 0
							if not isinstance(_lowercase      ,							(list, tuple)						) or not all(
							    isinstance(_lowercase      ,							_lowercase						) for number in numbers						):
														raise ValueError("""numbers must be an iterable of integers"""						)

							UpperCAmelCase	:						Optional[Any]					=						0
							UpperCAmelCase	:						List[str]					=						sys.maxsize

							while right < len(_lowercase						):
														curr_sum += numbers[right]
														while curr_sum >= target and left <= right:
																					UpperCAmelCase	:						Optional[Any]					=						min(_lowercase      ,							right - left + 1						)
																					curr_sum -= numbers[left]
																					left += 1
														right += 1

							return 0 if min_len == sys.maxsize else min_len

