'''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  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=sys.maxsize )  ->  Dict:
        lowercase__				:    Dict  =					'''bilinear'''
        lowercase__				:    Dict  =					max_size
        lowercase__				:    Any  =					short_edge_length


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

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

        return img_augs



class 			UpperCAmelCase  :





       '''simple docstring'''



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

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

         if self.size_divisibility > 0:
          raise NotImplementedError()
         # pad
         lowercase__				:    Any  =					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		,     UpperCAmelCase     ):
 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes





def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 assert torch.isfinite(UpperCAmelCase     ).all(), "Box tensor contains infinite or NaN!"
 lowercase__ ,							lowercase__				:    List[Any]  =					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'''

def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square(UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  # BASE CASE
  if row >= rows or col >= cols:
   return 0

  lowercase__				:    int  =					update_area_of_max_square(UpperCAmelCase		,     col + 1     )
  lowercase__				:    Any  =					update_area_of_max_square(row + 1		,     col + 1     )
  lowercase__				:    str  =					update_area_of_max_square(row + 1		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    List[Any]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    List[Any]  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Dict  =					[0]
 update_area_of_max_square(0		,     0     )
 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square_using_dp_array(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  if row >= rows or col >= cols:
   return 0
  if dp_array[row][col] != -1:
   return dp_array[row][col]

  lowercase__				:    int  =					update_area_of_max_square_using_dp_array(UpperCAmelCase		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Optional[int]  =					update_area_of_max_square_using_dp_array(row + 1		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Any  =					update_area_of_max_square_using_dp_array(row + 1		,     UpperCAmelCase		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    Optional[int]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    Any  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   lowercase__				:    int  =					sub_problem_sol
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Any  =					[0]
 lowercase__				:    List[Any]  =					[[-1] * cols for _ in range(UpperCAmelCase     )]
 update_area_of_max_square_using_dp_array(0		,     0		,     UpperCAmelCase     )

 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					[[0] * (cols + 1) for _ in range(rows + 1     )]
 lowercase__				:    str  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    str  =					dp_array[row][col + 1]
   lowercase__				:    Optional[Any]  =					dp_array[row + 1][col + 1]
   lowercase__				:    str  =					dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowercase__				:    Dict  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    str  =					max(dp_array[row][col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    Any  =					0

 return largest_square_area
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					[0] * (cols + 1)
 lowercase__				:    str  =					[0] * (cols + 1)
 lowercase__				:    Tuple  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    List[Any]  =					current_row[col + 1]
   lowercase__				:    Any  =					next_row[col + 1]
   lowercase__				:    Optional[Any]  =					next_row[col]

   if mat[row][col] == 1:
    lowercase__				:    List[str]  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    List[str]  =					max(current_row[col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    int  =					0
  lowercase__				:    int  =					current_row

 return largest_square_area


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

