



'''simple docstring'''

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


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''caidas/swin2sr-classicalsr-x2-64''': (
        '''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    List[str]   				=					'swin2sr'

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







							def __init__(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Tuple=64	,_UpperCAmelCase					:       str=1	,_UpperCAmelCase					:       List[Any]=3	,_UpperCAmelCase					:       Any=180	,_UpperCAmelCase					:       Optional[Any]=[6, 6, 6, 6, 6, 6]	,_UpperCAmelCase					:       Any=[6, 6, 6, 6, 6, 6]	,_UpperCAmelCase					:       int=8	,_UpperCAmelCase					:       Any=2.0	,_UpperCAmelCase					:       Optional[Any]=True	,_UpperCAmelCase					:       Tuple=0.0	,_UpperCAmelCase					:       Optional[int]=0.0	,_UpperCAmelCase					:       Tuple=0.1	,_UpperCAmelCase					:       List[str]="gelu"	,_UpperCAmelCase					:       Tuple=False	,_UpperCAmelCase					:       Optional[int]=0.02	,_UpperCAmelCase					:       Union[str, Any]=1E-5	,_UpperCAmelCase					:       int=2	,_UpperCAmelCase					:       Tuple=1.0	,_UpperCAmelCase					:       Union[str, Any]="1conv"	,_UpperCAmelCase					:       Tuple="pixelshuffle"	,**_UpperCAmelCase					:       List[str]	,):
									super().__init__(**_UpperCAmelCase		)

									_a     :      List[str]        =							image_size
									_a     :      Dict        =							patch_size
									_a     :      Optional[int]        =							num_channels
									_a     :      Optional[int]        =							embed_dim
									_a     :      Union[str, Any]        =							depths
									_a     :      Optional[int]        =							len(_UpperCAmelCase		)
									_a     :      Optional[Any]        =							num_heads
									_a     :      str        =							window_size
									_a     :      Optional[Any]        =							mlp_ratio
									_a     :      Optional[int]        =							qkv_bias
									_a     :      Tuple        =							hidden_dropout_prob
									_a     :      List[Any]        =							attention_probs_dropout_prob
									_a     :      Any        =							drop_path_rate
									_a     :      str        =							hidden_act
									_a     :      Tuple        =							use_absolute_embeddings
									_a     :      Dict        =							layer_norm_eps
									_a     :      Any        =							initializer_range
									_a     :      Optional[Any]        =							upscale
									_a     :      int        =							img_range
									_a     :      Union[str, Any]        =							resi_connection
									_a     :      int        =							upsampler









'''simple docstring'''

__lowerCAmelCase									=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							set()
		# keep track of all the paths to be checked
		_a     :      Any        =							[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				_a     :      Tuple        =							queue.pop(0  )
				# get the last node from the path
				_a     :      Tuple        =							path[-1]
				if node not in explored:
						_a     :      Optional[Any]        =							graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								_a     :      Any        =							list(lowerCAmelCase_  )
								new_path.append(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(lowerCAmelCase_  )

    # in case there's no path between the 2 nodes
		return []



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		_a     :      Optional[int]        =							[start]
		_a     :      Dict        =							set(lowerCAmelCase_  )
		# Keep tab on distances from `start` node.
		_a     :      Dict        =							{start: 0, target: -1}
		while queue:
				_a     :      List[str]        =							queue.pop(0  )
				if node == target:
						_a     :      Any        =							(
						    dist[node] if dist[target] == -1 else min(dist[target]						,			dist[node]  )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								_a     :      Any        =							dist[node] + 1
		return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




