



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 	snake_case_			:




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase=sys.maxsize):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[Any]					   =		'''bilinear'''
             UpperCAmelCase_	:						str					   =		max_size
             UpperCAmelCase_	:						Dict					   =		short_edge_length



       def __call__(  self  ,lowercase):

             """simple docstring"""





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

                   if img.dtype == np.uinta:
                         UpperCAmelCase_	:						str					   =		Image.fromarray(a__)
                         UpperCAmelCase_	:						Dict					   =		pil_image.resize((neww, newh)  ,PILImageResampling.BILINEAR)
                         UpperCAmelCase_	:						List[Any]					   =		np.asarray(a__)
                   else:
                         UpperCAmelCase_	:						Any					   =		img.permute(2  ,0  ,1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
                         UpperCAmelCase_	:						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 	snake_case_			:




       """simple docstring"""






       def __init__(  self  ,lowercase):

             """simple docstring"""





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



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						Any					   =		tuple(max(a__) for s in zip(*[img.shape for img in images]))
             UpperCAmelCase_	:						Tuple					   =		[im.shape[-2:] for im in images]
             UpperCAmelCase_	:						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  ,lowercase  ,lowercase=False):

             """simple docstring"""





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

                   if self.size_divisibility > 0:
                         raise NotImplementedError()
                   # pad
                   UpperCAmelCase_	:						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  _snake_case			(							__snake_case       ,	__snake_case     )  ->  int:






      '''simple docstring'''







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







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  List[str]:






      '''simple docstring'''







      assert torch.isfinite(__snake_case     ).all(), "Box tensor contains infinite or NaN!"
      UpperCAmelCase_	:						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'''





lowercase__								=						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]






class     lowercase  :






      def __init__(self			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			name
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         SCREAMING_SNAKE_CASE__      :					Any							    =			[]






      def  A__(self			,	a__       )							-> Dict:
         self.inbound.append(a__       )






      def  A__(self			,	a__       )							-> Any:
         self.outbound.append(a__       )






      def __repr__(self       )							-> List[str]:
         return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 					UpperCamelCase						(	snake_case						,  snake_case=3						,  snake_case=0.85 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			1

   SCREAMING_SNAKE_CASE__      :					int							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(node.outbound )

   for i in range(snake_case ):
      print(f"""======= Iteration {i + 1} =======""" )
      for _, node in enumerate(snake_case ):
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			(1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound )
      print(snake_case )


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			list(input('''Enter Names of the Nodes: ''' ).split() )

   SCREAMING_SNAKE_CASE__      :					str							    =			[Node(snake_case ) for name in names]

   for ri, row in enumerate(snake_case ):
      for ci, col in enumerate(snake_case ):
         if col == 1:
            nodes[ci].add_inbound(names[ri] )
            nodes[ri].add_outbound(names[ci] )

   print('''======= Nodes =======''' )
   for node in nodes:
      print(snake_case )

   page_rank(snake_case )


if __name__ == "__main__":
       main()

