





"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A     =    logging.get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["pixel_values"]

 def __init__(self    :       str	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       int = 32	,			UpperCAmelCase_    :       List[str]=PILImageResampling.BILINEAR	,			UpperCAmelCase_    :       bool = True	,			**UpperCAmelCase_    :       str	,			)	->None:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =do_resize
   lowerCamelCase__:				int        =do_rescale
   lowerCamelCase__:				int        =size_divisor
   lowerCamelCase__:				Optional[int]        =resample
   super().__init__(**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Optional[ChannelDimension] = None	,			**UpperCAmelCase_    :       str)	->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =get_image_size(UpperCAmelCase_)
   # Rounds the height and width down to the closest multiple of size_divisor
   lowerCamelCase__:				Union[str, Any]        =height // size_divisor * size_divisor
   lowerCamelCase__:				Dict        =width // size_divisor * size_divisor
   lowerCamelCase__:				Optional[Any]        =resize(UpperCAmelCase_	,			(new_h, new_w)	,			resample=UpperCAmelCase_	,			data_format=UpperCAmelCase_	,			**UpperCAmelCase_)
   return image

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       Optional[ChannelDimension] = None	,			**UpperCAmelCase_    :       Any)	->np.ndarray:



   '''simple docstring'''



   return rescale(image=UpperCAmelCase_	,			scale=UpperCAmelCase_	,			data_format=UpperCAmelCase_	,			**UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]	,			UpperCAmelCase_    :       Optional[bool] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Optional[Any]=None	,			UpperCAmelCase_    :       Optional[bool] = None	,			UpperCAmelCase_    :       Optional[Union[TensorType, str]] = None	,			UpperCAmelCase_    :       ChannelDimension = ChannelDimension.FIRST	,			**UpperCAmelCase_    :       Any	,			)	->BatchFeature:



   '''simple docstring'''



   lowerCamelCase__:				str        =do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:				List[str]        =do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:				Optional[int]        =size_divisor if size_divisor is not None else self.size_divisor
   lowerCamelCase__:				Union[str, Any]        =resample if resample is not None else self.resample

   if do_resize and size_divisor is None:
     raise ValueError("size_divisor is required for resizing")

   lowerCamelCase__:				str        =make_list_of_images(UpperCAmelCase_)

   if not valid_images(UpperCAmelCase_):
     raise ValueError("Invalid image(s)")

   # All transformations expect numpy arrays.
   lowerCamelCase__:				int        =[to_numpy_array(UpperCAmelCase_) for img in images]

   if do_resize:
     lowerCamelCase__:				Optional[Any]        =[self.resize(UpperCAmelCase_	,			size_divisor=UpperCAmelCase_	,			resample=UpperCAmelCase_) for image in images]

   if do_rescale:
     lowerCamelCase__:				Dict        =[self.rescale(UpperCAmelCase_	,			scale=1 / 255) for image in images]

   lowerCamelCase__:				List[str]        =[to_channel_dimension_format(UpperCAmelCase_	,			UpperCAmelCase_) for image in images]

   lowerCamelCase__:				Union[str, Any]        ={"pixel_values": images}
   return BatchFeature(data=UpperCAmelCase_	,			tensor_type=UpperCAmelCase_)








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Any	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   lowerCamelCase__:				int        =num_of_nodes
   lowerCamelCase__:				list[list[int]]        =[]
   lowerCamelCase__:				dict[int, int]        ={}

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       int)	->int:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if self.m_component[u_node] != u_node:
     for k in self.m_component:
       lowerCamelCase__:				Dict        =self.find_component(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       list[int]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if component_size[u_node] <= component_size[v_node]:
     lowerCamelCase__:				Optional[int]        =v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(UpperCAmelCase_)

   elif component_size[u_node] >= component_size[v_node]:
     lowerCamelCase__:				Tuple        =self.find_component(UpperCAmelCase_)
     component_size[u_node] += component_size[v_node]
     self.set_component(UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =[]
   lowerCamelCase__:				List[str]        =0

   lowerCamelCase__:				list[Any]        =[-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)

   lowerCamelCase__:				List[str]        =self.m_num_of_nodes

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

       lowerCamelCase__:				List[Any]        =self.m_component[u]
       lowerCamelCase__:				str        =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
           ):
             lowerCamelCase__:				Union[str, Any]        =[u, v, w]

     for edge in minimum_weight_edge:
       if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
         lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

         lowerCamelCase__:				str        =self.m_component[u]
         lowerCamelCase__:				Any        =self.m_component[v]

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

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





def 					lowerCAmelCase_			(	)					->		None:







  """simple docstring"""







if __name__ == "__main__":
     import doctest

     doctest.testmod()

