






"""simple docstring"""







from __future__ import annotations

from typing import Any







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Dict				,		__A:     int    ):

												'''simple docstring'''

												a__          =  num_of_nodes
												a__          =  []
												a__          =  {}




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     int				,		__A:     int    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[str]				,		__A:     int    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[Any]				,		__A:     int    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     str				,		__A:     list[int]				,		__A:     int				,		__A:     int    ):

												'''simple docstring'''

												if component_size[u_node] <= component_size[v_node]:
																	a__          =  v_node
																	component_size[v_node] += component_size[u_node]
																	self.set_component(__A    )

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





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  []
												a__          =  0

												a__          =  [-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    )

												a__          =  self.m_num_of_nodes

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

																						a__          =  self.m_component[u]
																						a__          =  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
																																):
																																					a__          =  [u, v, w]

																	for edge in minimum_weight_edge:
																						if isinstance(__A				,		__A    ):
																											a__       ,a__       ,a__          =  edge

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

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

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





def        SCREAMING_SNAKE_CASE       (      ):
					pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	Any             = logging.get_logger(__name__)

__a       :	Union[str, Any]             = {
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='swin2sr'

							_SCREAMING_SNAKE_CASE    ={
							    'hidden_size': 'embed_dim',
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}




							def __init__(   self:     Union[str, Any]				,		__A:     List[Any]=64				,		__A:     int=1				,		__A:     Dict=3				,		__A:     List[Any]=180				,		__A:     int=[6, 6, 6, 6, 6, 6]				,		__A:     Tuple=[6, 6, 6, 6, 6, 6]				,		__A:     int=8				,		__A:     Optional[int]=2.0				,		__A:     Optional[int]=True				,		__A:     int=0.0				,		__A:     Any=0.0				,		__A:     Optional[Any]=0.1				,		__A:     Optional[Any]="gelu"				,		__A:     Dict=False				,		__A:     List[Any]=0.0_2				,		__A:     List[Any]=1e-5				,		__A:     List[str]=2				,		__A:     int=1.0				,		__A:     Dict="1conv"				,		__A:     Optional[Any]="pixelshuffle"				,		**__A:     Dict				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  embed_dim
												a__          =  depths
												a__          =  len(__A    )
												a__          =  num_heads
												a__          =  window_size
												a__          =  mlp_ratio
												a__          =  qkv_bias
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  drop_path_rate
												a__          =  hidden_act
												a__          =  use_absolute_embeddings
												a__          =  layer_norm_eps
												a__          =  initializer_range
												a__          =  upscale
												a__          =  img_range
												a__          =  resi_connection
												a__          =  upsampler


