





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     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=sys.maxsize ):
        A__											=						'''bilinear'''
        A__											=						max_size
        A__											=						short_edge_length




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

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

        return img_augs




class     SCREAMING_SNAKE_CASE__ :



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



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

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


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       Union[str, Any]  )->Dict:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int] ,     UpperCamelCase__			:       Tuple[int, int]  )->Any:
       assert torch.isfinite(UpperCamelCase__  ).all(), "Box tensor contains infinite or NaN!"
       A__					,	A__											=						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__  )












a__:      Dict									=		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

a__:      str									=		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[]

       for neighbour in graph[vert]:
              if not visited[neighbour]:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       order.append(UpperCamelCase__  )

       return order


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[vert]

       for neighbour in reversed_graph[vert]:
              if not visited[neighbour]:
                     component += find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       return component


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]]  )->list[list[int]]:
       A__											=						len(UpperCamelCase__  ) * [False]
       A__											=						{vert: [] for vert in range(len(UpperCamelCase__  )  )}

       for vert, neighbours in graph.items():
              for neighbour in neighbours:
                     reversed_graph[neighbour].append(UpperCamelCase__  )

       A__											=						[]
       for i, was_visited in enumerate(UpperCamelCase__  ):
              if not was_visited:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       A__											=						[]
       A__											=						len(UpperCamelCase__  ) * [False]

       for i in range(len(UpperCamelCase__  )  ):
              A__											=						order[len(UpperCamelCase__  ) - i - 1]
              if not visited[vert]:
                     A__											=						find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     components_list.append(UpperCamelCase__  )

       return components_list





