






'''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 a_    :







   def __init__(self					,   __a					,   __a=sys.maxsize)			->      List[str]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= 'bilinear'
      __snake_case				:      Optional[int]     							= max_size
      __snake_case				:      str     							= short_edge_length







   def __call__(self					,   __a)			->      int:
      """simple docstring"""






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

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

      return img_augs




class a_    :







   def __init__(self					,   __a)			->      int:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Dict:
      """simple docstring"""






      __snake_case				:      int     							= tuple(max(__a) for s in zip(*[img.shape for img in images]))
      __snake_case				:      int     							= [im.shape[-2:] for im in images]
      __snake_case				:      Dict     							= [
          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)			->      Union[str, Any]:
      """simple docstring"""






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

         if self.size_divisibility > 0:
            raise NotImplementedError()
         # pad
         __snake_case				:      Any     							= 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  (				A  :       Any  ,		A  :       List[Any]  )    -> Union[str, Any]:







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





def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       Tuple[int, int]  )    -> Optional[int]:







   """simple docstring"""
   assert torch.isfinite(A  ).all(), "Box tensor contains infinite or NaN!"
   __snake_case							,__snake_case				:      Optional[int]     							= box_size
   tensor[:, 0].clamp_(min=0  ,		max=A  )
   tensor[:, 1].clamp_(min=0  ,		max=A  )
   tensor[:, 2].clamp_(min=0  ,		max=A  )
   tensor[:, 3].clamp_(min=0  ,		max=A  )







'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       list[float]  )    -> float:







   """simple docstring"""
   if discount_rate < 0:
      raise ValueError('Discount rate cannot be negative'  )
   if not cash_flows:
      raise ValueError('Cash flows list cannot be empty'  )
   __snake_case				:      List[str]     							= sum(
       cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(A  )  )
   return round(A  ,		ndigits=2  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()