






"""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    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ ,				snake_case_=sys.maxsize      )		->       Any:
       UpperCamelCase__         =    'bilinear'
       UpperCamelCase__         =    max_size
       UpperCamelCase__         =    short_edge_length







   def __call__(					self ,				snake_case_      )		->       int:
       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(snake_case_ ,				snake_case_      )
           if h < w:
               UpperCamelCase__       ,						UpperCamelCase__         =    size, scale * w
           else:
               UpperCamelCase__       ,						UpperCamelCase__         =    scale * h, size
           if max(snake_case_ ,				snake_case_      ) > self.max_size:
               UpperCamelCase__         =    self.max_size * 1.0 / max(snake_case_ ,				snake_case_      )
               UpperCamelCase__         =    newh * scale
               UpperCamelCase__         =    neww * scale
           UpperCamelCase__         =    int(neww + 0.5      )
           UpperCamelCase__         =    int(newh + 0.5      )

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

       return img_augs







class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_      )		->       Any:
       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 snake_case_      : (x - self.pixel_mean) / self.pixel_std

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    tuple(max(snake_case_      ) for s in zip(*[img.shape for img in images]      )      )
       UpperCamelCase__         =    [im.shape[-2:] for im in images]
       UpperCamelCase__         =    [
           nn.functional.pad(
               snake_case_ ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
           for size, im in zip(snake_case_ ,				snake_case_      )
       ]

       return torch.stack(snake_case_      ), torch.tensor(snake_case_      )







   def __call__(					self ,				snake_case_ ,				snake_case_=False      )		->       List[str]:
       with torch.no_grad():
           if not isinstance(snake_case_ ,				snake_case_      ):
               UpperCamelCase__         =    [images]
           if single_image:
               assert len(snake_case_      ) == 1
           for i in range(len(snake_case_      )      ):
               if isinstance(images[i] ,				torch.Tensor      ):
                   images.insert(snake_case_ ,				images.pop(snake_case_      ).to(self.device      ).float()      )
               elif not isinstance(images[i] ,				torch.Tensor      ):
                   images.insert(
                       snake_case_ ,				torch.as_tensor(img_tensorize(images.pop(snake_case_      ) ,				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(snake_case_      )
           # 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(snake_case_      ) for x in images]
           # now pad them to do the following operations
           UpperCamelCase__       ,						UpperCamelCase__         =    self.pad(snake_case_      )
           # Normalize

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
    """simple docstring"""

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












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




