





'''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 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase=sys.maxsize )							->							str:







				'''simple docstring'''





				__lowercase										=							'''bilinear'''
				__lowercase										=							max_size
				__lowercase										=							short_edge_length


	def __call__(self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





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

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

				return img_augs






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							Optional[int]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				__lowercase										=							tuple(max(_lowerCamelCase ) for s in zip(*[img.shape for img in images] ) )
				__lowercase										=							[im.shape[-2:] for im in images]
				__lowercase										=							[
				    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 )							->							Optional[int]:







				'''simple docstring'''





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

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





def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Optional[int]				):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple[int, int]				):
			assert torch.isfinite(lowerCamelCase_				).all(), "Box tensor contains infinite or NaN!"
			__lowercase						,		__lowercase										=							box_size
			tensor[:, 0].clamp_(min=0		,       max=lowerCamelCase_				)
			tensor[:, 1].clamp_(min=0		,       max=lowerCamelCase_				)
			tensor[:, 2].clamp_(min=0		,       max=lowerCamelCase_				)
			tensor[:, 3].clamp_(min=0		,       max=lowerCamelCase_				)












'''simple docstring'''



from __future__ import annotations

from typing import Any






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							num_of_nodes
				__lowercase										=							[]
				__lowercase										=							{}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				self.m_edges.append([u_node, v_node, weight] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				if self.m_component[u_node] == u_node:
							return u_node
				return self.find_component(self.m_component[u_node] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if self.m_component[u_node] != u_node:
							for k in self.m_component:
										__lowercase										=							self.find_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if component_size[u_node] <= component_size[v_node]:
							__lowercase										=							v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(_lowerCamelCase )

				elif component_size[u_node] >= component_size[v_node]:
							__lowercase										=							self.find_component(_lowerCamelCase )
							component_size[u_node] += component_size[v_node]
							self.set_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							None:







				'''simple docstring'''





				__lowercase										=							[]
				__lowercase										=							0

				__lowercase										=							[-1] * self.m_num_of_nodes

				# A list of components (initialized to all of the nodes)
				for node in range(self.m_num_of_nodes ):
							self.m_component.update({node: node} )
							component_size.append(1 )

				__lowercase										=							self.m_num_of_nodes

				while num_of_components > 1:
							for edge in self.m_edges:
										__lowercase						,		__lowercase						,		__lowercase										=							edge

										__lowercase										=							self.m_component[u]
										__lowercase										=							self.m_component[v]

										if u_component != v_component:

													for component in (u_component, v_component):
																if (
																    minimum_weight_edge[component] == -1
																    or minimum_weight_edge[component][2] > w
																):
																			__lowercase										=							[u, v, w]

							for edge in minimum_weight_edge:
										if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
													__lowercase						,		__lowercase						,		__lowercase										=							edge

													__lowercase										=							self.m_component[u]
													__lowercase										=							self.m_component[v]

													if u_component != v_component:
																mst_weight += w
																self.union(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )
																print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
																num_of_components -= 1

							__lowercase										=							[-1] * self.m_num_of_nodes
				print(f"The total weight of the minimal spanning tree is: {mst_weight}" )





def    _lowerCAmelCase						(				):
			pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()





